<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Get ready for an exceptional online learning experience with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong> bundle! This carefully curated collection of<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> 30 premium courses</strong> is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The Mechanical And Mechatronics Engineering &amp; Energy Engineer  is a dynamic package, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you&#39;re looking to break into a new field or deepen your existing knowledge, the<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer  </strong>package has something for everyone.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">As part of the Mechanical And Mechatronics Engineering &amp; Energy Engineer  package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Mechanical And Mechatronics Engineering &amp; Energy Engineer  bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This Bundle Comprises the Following </strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPD Accredited Courses:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 01:</strong> Mechanical Engineering</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 02:</strong> Engineering Mechanics Course for Beginners</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 03:</strong> Crack Your Mechanical Engineer Interview</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 04:</strong> Automotive Engineering: Onboard Diagnostics</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 05:</strong> Automotive Design</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 06:</strong> Hybrid Vehicle Expert Training</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 07:</strong> Engine Lubrication Systems Online Course</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 08:</strong> Rotating Machines</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 09:</strong> Electric Circuits for Electrical Engineering</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 10:</strong> Electrical Machines for Electrical Engineering</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 11:</strong> Electronic &amp; Electrical Devices Maintenance &amp; Troubleshooting</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 12:</strong> Digital Electric Circuits &amp; Intelligent Electrical Devices</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 13:</strong> MATLAB Simulink for Electrical Power Engineering</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 14:</strong> Power Electronics for Electrical Engineering</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 15:</strong> Energy Engineer Course</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 16:</strong> Energy Saving in Electric Motors</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 17:</strong> Electric Vehicle Battery Management System</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 18:</strong> A complete course on Turbocharging</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 19:</strong> Heating Ventilation and AirConditioning (HVAC) Technician</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 20:</strong> Python Intermediate Training</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 21:</strong> Spatial Data Visualisation and Machine Learning in Python</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 22:</strong> Data Center Training Essentials: Mechanical &amp; Cooling</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 23:</strong> Career Development Plan Fundamentals</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 24:</strong> CV Writing and Job Searching</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 25: </strong>Learn to Level Up Your Leadership</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 26:</strong> Networking Skills for Personal Success</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 27:</strong> Ace Your Presentations: Public Speaking Masterclass</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 28:</strong> Learn to Make a Fresh Start in Your Life</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 29:</strong> Motivation - Motivating Yourself &amp; Others</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 30:</strong> Excel: Top 50 Microsoft Excel Formulas in 50 Minutes!</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What will make you stand out?</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Upon completion of this online <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong>  bundle, you will gain the following:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">CPD QS Accredited Proficiency with this  Mechanical And Mechatronics Engineering &amp; Energy Engineer  </li><li fr-original-style="" style="box-sizing: border-box;">After successfully completing the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong>&nbsp; bundle, you will receive a FREE CPD PDF Certificates as evidence of your newly acquired abilities.</li><li fr-original-style="" style="box-sizing: border-box;">Lifetime access to the whole collection of learning materials of this Mechanical And Mechatronics Engineering &amp; Energy Engineer .</li><li fr-original-style="" style="box-sizing: border-box;">The online test with immediate results </li><li fr-original-style="" style="box-sizing: border-box;">You can study and complete the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical And Mechatronics Engineering &amp; Energy Engineer </strong> bundle at your own pace.</li><li fr-original-style="" style="box-sizing: border-box;">Study for the Mechanical And Mechatronics Engineering &amp; Energy Engineer  bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Each course in this <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer  </strong>bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Embrace the future of learning with the Mechanical And Mechatronics Engineering &amp; Energy Engineer, a rich anthology of 30 diverse courses. Each course in the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong> bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisMechanical And Mechatronics Engineering &amp; Energy Engineer  bundle will take you on a unique and enriching educational journey.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Mechanical And Mechatronics Engineering &amp; Energy Engineer  bundle offers you the flexibility and convenience to learn at your own pace. Make the Mechanical And Mechatronics Engineering &amp; Energy Engineer  package your trusted companion in your lifelong learning journey.</p><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">CPD</div>
    </h2>

                        <div id="cpd" fr-original-style="" style="box-sizing: border-box;">
                            <div fr-original-style="" style="box-sizing: border-box;">
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                                        300 CPD hours / points
                                    </div>
                                        <div fr-original-style="" style="box-sizing: border-box;">
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer </strong> bundle is perfect for:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lifelong learners looking to expand their knowledge and skills.</li><li fr-original-style="" style="box-sizing: border-box;">Professionals seeking to enhance their career with CPD certification.</li><li fr-original-style="" style="box-sizing: border-box;">Individuals wanting to explore new fields and disciplines.</li><li fr-original-style="" style="box-sizing: border-box;">Anyone who values flexible, self-paced learning from the comfort of home.</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Requirements</div>
    </h2>

                        <div id="requirements" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You are cordially invited to enroll in this bundle; please note that there are no formal prerequisites or qualifications required. We&#39;ve designed this curriculum to be accessible to all, irrespective of prior experience or educational background.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Career path</div>
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                        <div id="career_path" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Unleash your potential with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Mechanical Engineering, Mechatronics Engineering &amp; Energy Engineer  </strong>bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with thisbundle package.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Certificates</div>
    </h2>

                        <div id="optional_extras" fr-original-style="" style="box-sizing: border-box;">
                                    <h4 fr-original-style="" style="box-sizing: border-box;">CPD Quality Standard Certificate</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>
            <div fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">30 CPD Accredited Digital Certificates and A Hard Copy Certificate </p></div>

                        </div>

NextGen Learning

Mechatronics

Mechanical Engineering

Engineering Construction Industry Training (ECITB)

Mechanical Engineer

Institution of Mechanical Engineers (IMechE)

Engineering Management

Mechanical Engineering, Mechatronics Engineering & Energy Engineer - 30 Courses Bundle

General Admission

<strong>In this course, you will be learning one of the widely used deep learning frameworks, that is, PyTorch, and learn the basics of convolutional neural networks in PyTorch. We will also cover the basics of Python and understand how to implement different Python libraries.</strong>
        <p>Note: The course is primarily focused on teaching PyTorch and deep learning for computer vision, but it also includes a few sections on the fundamentals of Python (Sections 8-12). These optional learning sections are designed for individuals who may be new to Python or who want to refresh their knowledge of Python basics.

In this course, we will take a step-by-step method by first grasping PyTorch's fundamentals. Then, using a guide to getting free GPU for learning, you will learn how to code in GPU. You will then learn about PyTorch's AutoGrad feature and how to use it. Later, you will learn how to use PyTorch to create deep learning models and understand the fundamentals of convolutional neural networks (CNN). You will also learn how to use CNN with a real-world dataset.

Additionally, the course will emphasize the fundamentals and lay the groundwork for an understanding of Python. We will also talk about the three significant Python libraries known as NumPy, Pandas, and Matplotlib. In this part of the course, we will also build a mini project where we will be building a hangman game in Python.

By the end of this course, we will be able to perform Computer Vision tasks with deep learning.

All the resources for this course are available at:  https://github.com/PacktPublishing/Deep-Learning---Computer-Vision-for-Beginners-Using-PyTorch</p>
        <h2>What You Will Learn</h2><p>Learn how to work with PyTorch<br>Build intuition on convolution operation on images<br>Implement gradient descent using AutoGrad<br>Learn about LeNet architecture<br>Create a mini-Python project game<br>Understand how to use NumPy, Pandas, and Matplotlib libraries</p>
        <h2>Audience</h2><p>Software developers, machine learning practitioners, data scientists, and anybody else interested in understanding PyTorch and deep learning should take this course. While a basic knowledge of Python would be beneficial, it is not a prerequisite as we will be covering the necessary fundamentals during the course.</p>
        <h2>Approach</h2><p>This is a step-by-step learning course where we will start with the basics and move toward real-world implementation. You will also learn the basics of Python programming and build a mini project to test our learning in the real world.</p>
        <h2>Key Features</h2><p>Learn how to perform Computer Vision tasks with deep learning * Learn to implement LeNet architecture on CIFAR10 dataset, which has 60,000 images * Build your programming foundation with Python</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Deep-Learning---Computer-Vision-for-Beginners-Using-PyTorch</p>
        <h2>About the Author</h2><h3>Manifold AI Learning</h3><p>Manifold AI Learning is an online academy with the goal to empower students with the knowledge and skills that can be directly applied to solving real-world problems in data science, machine learning, and artificial intelligence.

With a curated curriculum and a hands-on guide, you will always be an industry-ready professional.</p>
        <h2>Course Outline</h2><h4>1. Welcome Aboard</h4><p>Welcome to the course! This is a quick introductory section.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Introduction</p><p style='margin: 0'>In this video, we will have a quick course introduction.</p></td></tr><tr><td style='padding: 15px;'><p>2. Why Is PyTorch Powerful?</p><p style='margin: 0'>In this video, we will look at a quick demo and understand why PyTorch is powerful.</p></td></tr></tbody></table><br><h4>2. Introduction to PyTorch and Tensors</h4><p>In this section, we will get introduced to PyTorch and Tensors.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is PyTorch</p><p style='margin: 0'>In this video, we will get a brief introduction to PyTorch.</p></td></tr></tbody></table><br><h4>3. Diving into PyTorch</h4><p>In this section, we will dive into PyTorch and get in action.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Installing PyTorch</p><p style='margin: 0'>In this video, you will learn how to install PyTorch using Google Colab ipynb.</p></td></tr><tr><td style='padding: 15px;'><p>2. Create Tensors in PyTorch</p><p style='margin: 0'>In this video, you will learn how to create Tensors in PyTorch.</p></td></tr><tr><td style='padding: 15px;'><p>3. Tensor Slicing and Reshape</p><p style='margin: 0'>In this video, you will learn how to reshape and slice a Tensor.</p></td></tr><tr><td style='padding: 15px;'><p>4. Mathematical Operations on Tensors</p><p style='margin: 0'>In this video, let's see how we can perform mathematical operations on Tensors.</p></td></tr><tr><td style='padding: 15px;'><p>5. NumPy in PyTorch</p><p style='margin: 0'>In this video, you will learn how to convert a NumPy array in PyTorch.</p></td></tr><tr><td style='padding: 15px;'><p>6. What Is CUDA</p><p style='margin: 0'>In this video, we will first understand what CUDA is and then see it in action.</p></td></tr><tr><td style='padding: 15px;'><p>7. PyTorch on GPU</p><p style='margin: 0'>In this video, we will test the competition speed with GPU.</p></td></tr></tbody></table><br><h4>4. AutoGrad in PyTorch</h4><p>In this section, we will have a look at AutoGrad in PyTorch.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. AutoGrad in PyTorch</p><p style='margin: 0'>In this video, you will learn what is AutoGrad in PyTorch.</p></td></tr><tr><td style='padding: 15px;'><p>2. AutoGrad in a Loop</p><p style='margin: 0'>In this video, you will learn to implement the AutoGrad function in a loop.</p></td></tr></tbody></table><br><h4>5. Creating Deep Neural Networks in PyTorch</h4><p>In this section, we will be working on creating a deep neural network in PyTorch.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Building the First Neural Network</p><p style='margin: 0'>In this video, you will learn how to build your first neural network.</p></td></tr><tr><td style='padding: 15px;'><p>2. Writing a Deep Neural Network</p><p style='margin: 0'>In this video, you will learn how to write a deep neural network.</p></td></tr><tr><td style='padding: 15px;'><p>3. Writing a Custom NN Module</p><p style='margin: 0'>In this video, you will learn how to write a custom NN (Neural Network) module.</p></td></tr></tbody></table><br><h4>6. CNN in PyTorch</h4><p>In this section, you will learn how to implement a convolutional neural network in PyTorch.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Loading - CIFAR10</p><p style='margin: 0'>In this video, you will learn how to load our CIFAR10 dataset in PyTorch.</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Visualization</p><p style='margin: 0'>In this video, you will learn how to visualize your data to understand it better.</p></td></tr><tr><td style='padding: 15px;'><p>3. CNN Recap</p><p style='margin: 0'>In this video, we will have a quick recap on the convolution operation.</p></td></tr><tr><td style='padding: 15px;'><p>4. First CNN</p><p style='margin: 0'>In this video, we will work on building our first convolution layer.</p></td></tr><tr><td style='padding: 15px;'><p>5. CNN Deep Layers</p><p style='margin: 0'>In this video, you will learn how to perform a series of convolution operations to the required output.</p></td></tr></tbody></table><br><h4>7. LeNet Architecture in PyTorch</h4><p>In this section, you will learn how to implement the LeNet architecture in PyTorch.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. LeNet Overview</p><p style='margin: 0'>In this video, let's first understand what LeNet architecture is.</p></td></tr><tr><td style='padding: 15px;'><p>2. LeNet Model in PyTorch</p><p style='margin: 0'>In this video, you will learn how to implement the LeNet model in PyTorch.</p></td></tr><tr><td style='padding: 15px;'><p>3. Preparation and Evaluation</p><p style='margin: 0'>In this video, you will learn how to train our LeNet model.</p></td></tr></tbody></table><br><h4>8. Optional Learning- Python Basics</h4><p>In this section, you will be learning the basics of Python.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Why Learn Any Programming Language</p><p style='margin: 0'>In this video, we will first understand why we should learn any programming language.</p></td></tr><tr><td style='padding: 15px;'><p>2. Why Choose Python</p><p style='margin: 0'>In this video, we will understand the benefits of Python.</p></td></tr><tr><td style='padding: 15px;'><p>3. Installing Jupyter Notebook</p><p style='margin: 0'>In this video, you will learn how to install Jupyter Notebook.</p></td></tr><tr><td style='padding: 15px;'><p>4. Jupyter Notebook - Tips and Tricks</p><p style='margin: 0'>In this video, you will learn some useful tips and tricks of working in Jupyter Notebook.</p></td></tr><tr><td style='padding: 15px;'><p>5. What We Will Cover in This Section</p><p style='margin: 0'>In this video, we will understand the learning objective of the Python basics section.</p></td></tr><tr><td style='padding: 15px;'><p>6. Variables in Python</p><p style='margin: 0'>In this video, you will learn about variables in Python.</p></td></tr><tr><td style='padding: 15px;'><p>7. Print Function</p><p style='margin: 0'>In this video, you will learn about the Print function.</p></td></tr><tr><td style='padding: 15px;'><p>8. Numerical Data Types and Arithmetic Operations in Python</p><p style='margin: 0'>In this video, you will learn about numerical data types and arithmetic operations in Python.</p></td></tr><tr><td style='padding: 15px;'><p>9. String Data Type</p><p style='margin: 0'>In this video, you will learn about the string data type.</p></td></tr><tr><td style='padding: 15px;'><p>10. Boolean Data Type</p><p style='margin: 0'>In this video, we will understand the Boolean data type.</p></td></tr><tr><td style='padding: 15px;'><p>11. Type Conversion and Type Casting</p><p style='margin: 0'>In this video, we will understand how to convert one data type to another data type.</p></td></tr><tr><td style='padding: 15px;'><p>12. Adding Comments in Python Programming Language</p><p style='margin: 0'>In this video, you will learn how to add comments to our programs.</p></td></tr><tr><td style='padding: 15px;'><p>13. Data Structures in Python</p><p style='margin: 0'>In this video, we will understand the data structures in Python.</p></td></tr><tr><td style='padding: 15px;'><p>14. Tuples and Sets in Python</p><p style='margin: 0'>In this video, you will learn about tuples and sets in Python.</p></td></tr><tr><td style='padding: 15px;'><p>15. Python Dictionaries</p><p style='margin: 0'>In this video, you will learn about Python dictionaries.</p></td></tr><tr><td style='padding: 15px;'><p>16. Conditional Statements in Python - if</p><p style='margin: 0'>In this video, you will learn about the "If" conditional statement and how to implement it.</p></td></tr><tr><td style='padding: 15px;'><p>17. Conditional Statements in Python - While</p><p style='margin: 0'>In this video, you will learn about the "While" conditional statement and how to implement it.</p></td></tr><tr><td style='padding: 15px;'><p>18. Inbuilt Functions in Python - range and input</p><p style='margin: 0'>In this video, you will learn two important inbuilt functions in Python, which are range and input functions.</p></td></tr><tr><td style='padding: 15px;'><p>19. For Loops</p><p style='margin: 0'>In this video, you will learn about for loops.</p></td></tr><tr><td style='padding: 15px;'><p>20. Functions in Python</p><p style='margin: 0'>In this video, you will learn about functions in Python.</p></td></tr><tr><td style='padding: 15px;'><p>21. Classes in Python</p><p style='margin: 0'>In this video, you will learn about classes in Python.</p></td></tr></tbody></table><br><h4>9. Optional Learning - Mini Project with Python Basics</h4><p>In this section, we will be working on a mini project where we will implement our learning from the Python basics section.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Mini Project - Hangman</p><p style='margin: 0'>In this video, we will get introduced to our mini project, which is called the Hangman.</p></td></tr><tr><td style='padding: 15px;'><p>2. Writing a Class</p><p style='margin: 0'>In this video, we will start writing the program for our classes and objects.</p></td></tr><tr><td style='padding: 15px;'><p>3. Mini Project - Continued</p><p style='margin: 0'>In this video, we will continue writing the program for our hangman game.</p></td></tr><tr><td style='padding: 15px;'><p>4. Logic Building</p><p style='margin: 0'>In this video, we will work on building the logic.</p></td></tr><tr><td style='padding: 15px;'><p>5. Logic for Single-Letter input</p><p style='margin: 0'>In this video, we will continue building the logic for single-letter input.</p></td></tr><tr><td style='padding: 15px;'><p>6. Final Testing</p><p style='margin: 0'>In this video, we will run our project and check whether our program runs as required.</p></td></tr></tbody></table><br><h4>10. Optional Learning - Python for Data Science with NumPy</h4><p>IN this section, we will understand how to work with the NumPy library.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. NumPy</p><p style='margin: 0'>In this video, we will first understand how to create arrays using NumPy library.</p></td></tr><tr><td style='padding: 15px;'><p>2. Resize and Reshape Arrays</p><p style='margin: 0'>In this video, you will learn how to resize and reshape an array.</p></td></tr><tr><td style='padding: 15px;'><p>3. Slicing</p><p style='margin: 0'>In this video, we will understand how to perform slicing on NumPy arrays.</p></td></tr><tr><td style='padding: 15px;'><p>4. Broadcasting</p><p style='margin: 0'>In this video, we will understand the concept of broadcasting.</p></td></tr><tr><td style='padding: 15px;'><p>5. Mathematical Operations and Functions in NumPy</p><p style='margin: 0'>In this video, we will understand the different mathematical operations and functions that we can perform on NumPy arrays.</p></td></tr></tbody></table><br><h4>11. Optional Learning - Python for Data Science with Pandas</h4><p>In this section, you will learn about Pandas.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Pandas Library</p><p style='margin: 0'>In this video, you will learn about the Pandas library.</p></td></tr><tr><td style='padding: 15px;'><p>2. Pandas Dataframe</p><p style='margin: 0'>In this video, you will learn about Pandas Dataframe.</p></td></tr><tr><td style='padding: 15px;'><p>3. Pandas Dataframe - Load from External File</p><p style='margin: 0'>In this video, you will learn how to load the data to our dataframe from an external file.</p></td></tr><tr><td style='padding: 15px;'><p>4. Working with Null Values</p><p style='margin: 0'>In this video, you will learn how to work with null values.</p></td></tr><tr><td style='padding: 15px;'><p>5. Slicing Pandas Dataframe</p><p style='margin: 0'>In this video, we will understand how we can return the required elements from our dataframe using the concept of slicing.</p></td></tr><tr><td style='padding: 15px;'><p>6. Imputation</p><p style='margin: 0'>In this video, we will understand how to perform imputation on our dataframe.</p></td></tr></tbody></table><br><h4>12. Optional Learning - Python for Data Science with Matplotlib</h4><p>In this section, we will talk about Matplotlib.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Matplotlib Introduction</p><p style='margin: 0'>In this video, we will get introduced to Matplotlib.</p></td></tr><tr><td style='padding: 15px;'><p>2. Format the Plot</p><p style='margin: 0'>In this video, you will learn how to format our Matplotlib plot.</p></td></tr><tr><td style='padding: 15px;'><p>3. Plot Formatting and Scatter Plot</p><p style='margin: 0'>In this video, we will cover plot formatting and scatter plot.</p></td></tr><tr><td style='padding: 15px;'><p>4. Histplot</p><p style='margin: 0'>In this video, you will learn how to create a Histplot.</p></td></tr></tbody></table>

Packt

Computer Vision

Python

Pandas

TensorFlow

In this course, you will be learning one of the widely used deep learning frameworks, that is, PyTorch, and learn the basics of convolutional neural networks in PyTorch. We will also cover the basics of Python and understand how to implement different Python libraries.

Deep Learning - Computer Vision for Beginners Using PyTorch

<strong>This course does not require any prior knowledge of Apache Spark or Hadoop. The author explains Spark architecture and fundamental concepts to help you come up to speed and grasp the content of this course.

The course will help you understand Spark programming and apply that knowledge to build data engineering solutions.</strong>
        <p>Apache Spark is a lightning-fast unified analytics engine for big data and machine learning. Since its release, Apache Spark has seen rapid adoption by enterprises across a wide range of industries. Internet powerhouses such as Netflix, Yahoo, and eBay have deployed Spark at a massive scale. It has quickly become the largest open-source community in big data. So, mastering Apache Spark opens a wide range of professional opportunities.

This course starts with an introduction to Apache Spark where you see what Apache Spark is in brief. Then, you will be installing and using Apache Spark. After that, you will look at the Spark execution model and architecture in detail. Next, you will learn the Spark programming model and developer experience. Following that, you will look at the Spark Structured API foundation, and Spark data sources and sinks. Then, you will explore Spark Data frame and dataset transformations along with aggregations in Apache Spark. Finally, you will look at the Spark Data frame joins in detail.

By the end of this course, you will understand Spark programming and apply that knowledge to build data engineering solutions.

All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Spark-Programming-in-Scala-for-Beginners-with-Apache-Spark-3</p>
        <h2>What You Will Learn</h2><p>Learn Apache Spark and Spark architecture<br>Look at data engineering and data processing in Spark<br>Work with data sources and sinks<br>Work with data frames, datasets, and Spark SQL<br>Use IntelliJ IDEA for Spark development and debugging<br>Understand unit testing, managing application logs, and cluster deployment</p>
        <h2>Audience</h2><p>This course is designed for software engineers willing to develop a data engineering pipeline and application using Apache Spark. It is also for data architects and data engineers who are responsible for designing and building the organization's data-centric infrastructure. It will also be beneficial for the managers and architects who do not directly work with Spark implementation, and still, they work with the people who implement Apache Spark at the ground level.<br><br>Before proceeding with the course, you will need basic knowledge of the Scala programming language.</p>
        <h2>Approach</h2><p>This is a hands-on and well-balanced course with theoretical and practical content. The course is example-driven and follows a working session-like approach. The author will be taking a live coding approach and explaining all the concepts needed along the way. The section will have a summary video at the end to revise the learned concepts.</p>
        <h2>Key Features</h2><p>A comprehensive course designed for the beginner-level for Spark programming in Scala * Deep dive into Spark 3 architecture and data engineering * Complete tested source code and examples used on Apache Spark 3.0.0 open-source distribution from the author's end</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Spark-Programming-in-Scala-for-Beginners-with-Apache-Spark-3</p>
        <h2>About the Author</h2><h3>Scholar Nest</h3><p>ScholarNest is a small team of people passionate about helping others learn and grow in their careers by bridging the gap between their existing and required skills.

Together, they have over 40+ years of experience in IT as a developer, architect, consultant, trainer, and mentor. They have worked with international software services organizations on various data-centric and Big Data projects.

It is a team of firm believers in lifelong continuous learning and skill development. To popularize the importance of continuous learning, they started publishing free training videos on their YouTube channel. They conceptualized the notion of continuous learning, creating a journal of our learning under the Learning Journal banner.</p>
        <h2>Course Outline</h2><h4>1. Apache Spark Introduction</h4><p></p><br><h4>2. Installing and Using Apache Spark</h4><p></p><br><h4>3. Spark Execution Model and Architecture</h4><p></p><br><h4>4. Spark Programming Model and Developer Experience</h4><p></p><br><h4>5. Spark Structured API Foundation</h4><p></p><br><h4>6. Spark Data Sources and Sinks</h4><p></p><br><h4>7. Spark Dataframe and Dataset Transformations</h4><p></p><br><h4>8. Aggregations in Apache Spark</h4><p></p><br><h4>9. Spark Dataframe Joins</h4><p></p><br><h4>10. Keep Learning</h4><p></p>

Scala

Apache Spark

Hadoop

Big Data

This course does not require any prior knowledge of Apache Spark or Hadoop. The author explains Spark architecture and fundamental concepts to help you come up to speed and grasp the content of this course.

The course will help you understand Spark programming and apply that knowledge to build data engineering solutions.

Spark Programming in Scala for Beginners with Apache Spark 3

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						</div>
					</div>
		</div>
								</div>
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			<p>The course is designed for Google Ads (Google AdWords) practitioners that already have basic knowledge in setting up and running AdWords accounts.  The course explores advanced techniques and ideas that I have picked up and developed over 10 years running AdWords campaigns for small to large scale clients.</p>
<p>I explain the idea behind each technique and I give you ideas and example cases on how I have used it.  I then show you how to set it up step by step inside Google Ads.  We also look at how to interpret results and how to apply it to your own campaigns, we look at real data and actual results.</p>
<div>
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<h3><strong>What you'll learn</strong></h3>
<ul>
<li><span>Google Ads Campaign Experiments: Best Use Cases, Step-by-Step Setup, Analysis, Implementation</span></li>
<li>Access Google Ads tools for forecasting performance</li>
<li>Budget Optimisation techniques using regression formula</li>
<li><span>Advanced Concept Testing: moving beyond A/B ad testing to testing concepts</span></li>
<li><span>Improving Geotargeting for Local Businesses With A Double Campaign Structure</span></li>
<li><span>How To Setup, Run, Pivot &amp; Interpret the Paid &amp; Organic Report</span></li>
<li><span>Make A Strong Argument For Running Branded Ads Using the Paid &amp; Organic Report</span></li>
<li><span>Identify Opportunities Between Organic &amp; Paid Listings</span></li>
<li><span>Learn to use a free Google Sheets template for automating and tracking budgets in multiple ad accounts</span></li>
<li><span>How to find the key pitfalls in any Google Ads account</span></li>
<li><span>How to Audit a new or existing Google Ads account and find issues to improve upon quickly</span></li>
<li><span>Utilise Dynamic Ad Customisers for more personalised ads</span></li>
<li><span>Understand the power of using Google Analytics with Google Ads and be familiar with Audience setup and relevant Google Ads reports in GA</span></li>
<li><span>Understand attribution models and why the default last click is not always ideal</span></li>
<li><span>Implement Machine Learning features within Google Ads to benefit your account in ways that human optimisation could never reach</span></li>
<li><span>Develop a multi-funnel remarketing strategy for GDN and search campaign</span></li>
</ul>
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<h3><strong>Requirements</strong></h3>
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<ul>
<li>Able to set up your own Google Ads account</li>
<li>Be confident navigating Google Ads accounts</li>
<li>Basic exposure to excel filtering &amp; pivot tables</li>
</ul>
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</div><table>				<tbody><tr>
					<td colspan="4">Introduction</td>
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					<td><i></i></td>
					<td>1. Introduction</td>
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					<td colspan="4">Testing to Ad Concepts </td>
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					<td>2. Introduction to Ad Concepts</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
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					<td>3.Example Ad Concepts</td>
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					<td><span><i></i> 00:04:00</span></td>
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					<td>4. Label Setup</td>
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					<td><span><i></i> 00:04:00</span></td>
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					<td>5. Reporting on Ad Concepts</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
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					<td colspan="4">Google Ads Paid % Organic Interaction Report</td>
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					<td>6. Understanding the usefulness of this report</td>
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					<td><span><i></i> 00:03:00</span></td>
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					<td>7.Linking Google Ads with Search Console</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>8.Excel Report Setup &amp; Pivot</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>9.Identifying Google Ads &amp; SEO Search Term Opportunities</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
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					<td>10.CTR Interaction between SEO &amp; SEM</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
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					<td colspan="4">Geolocal Campaigns - Double Campaign Structure</td>
				</tr>
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					<td><i></i></td>
					<td>11.The theory behind the double campaign</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
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					<td>12.A practical example inside Google Ads</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
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					<td colspan="4">Google Ads Campaign - Experiments (Drafts &amp; Experiments) </td>
				</tr>
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					<td><i></i></td>
					<td>13.Introduction to Drafts &amp; Experiments</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
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					<td>14.Example experiment Ideas for use</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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					<td>15.Draft &amp; Experiment Setup</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>16.Report Analysis &amp; explanation of symbols</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
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					<td colspan="4">Automate Budgeting for Multiple Ad Accounts</td>
				</tr>
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					<td><i></i></td>
					<td>17. Introduction to the Google Spreedsheet template</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
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					<td>18. Data Inputs</td>
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					<td>19. Reading the sheet output</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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					<td colspan="4">AdWords Audit</td>
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					<td>20. Audit Overview</td>
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					<td><span><i></i> 00:02:00</span></td>
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					<td>21. Auditing the Account Structure</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
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					<td>22. Campaign Settings</td>
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					<td>23. Auditing on the AdGroup Level</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>24. Other Things to look for in an Audit</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
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					<td colspan="4">Remarketing Strategy &amp; Implementation</td>
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					<td><i></i></td>
					<td>25. Why Remarketing Works</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>26.GDN Remarketing</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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					<td><i></i></td>
					<td>27.Remarketing Lists for Search Ads (RLSA) &amp; IF Statements</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>28. Customer Match (Email Retargeting)</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>29. Dynamic Remarketing</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>30. Lookalike Audiences</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>31. GDN Banner Sizes &amp; Other Things to Consider</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>32. Audience List Sharing</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>33. Remarketing with Events</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
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					<td colspan="4">Artificial Intelligence &amp; Machine Learning </td>
				</tr>
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					<td><i></i></td>
					<td>34.What is AI &amp; ML</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>35. Auto Rotate for Budgets &amp; Ads</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>36.Dynamic Search Ads</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>37.Smart Bidding Strategies</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>38.Lookalike Audiences</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>39.Data Driven Attribution Models</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Dynamic Ad Customisers </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>40.Introduction to Ad Customisers</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>41. Advantages &amp; Example Uses</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>42.Setting up the Business Data Feed</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>43.Best Practice for Ads</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Google Analytics for Google Ads</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>44. The Advantages of linking AdWords with GA</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>45. Linking GA &amp; AdWords</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>46. AdWords Reports in GA</td>
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>47. GA Conversion Tracking &amp; Goal Setup</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>48. Remarketing Segments in GA</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>49. Attribution Models in Google Analytics</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Using Scripts</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>50. Introduction to AdWords Scripts</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>51. 404 Link Checker Script</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>52. Experiment Results Script</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>53. Google Ads Audit Script</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">AdWords Editor </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>54. Introduction and advantages of AdWords Editor over the online interface</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>55. Example Uses for AdWords Editor</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>56.AdWords Editor - what you can't do</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Competitor Research Tools</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>57.Auction Insights displayed graphically</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>58.3rd Party Competitor Research Tools -SEMrush</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Landing Page Software</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>59.Why Use Landing Page Software</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>60.Unbounce Walk-through</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>61. Tracking integration for landing pages</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">New Features in 2018/19</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>62.Ad Variations</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>63.Search Responsive Ads</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>64. Life Events Audiences</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>65. Custom Intent Audiences</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
				</tbody></table>

iStudy UK

Google Ads

Online Course Marketing

Paid Advertising

Facebook Ads

YouTube Marketing

Google Digital Marketing

Advanced Google Ads / AdWords Training - Updated for 2019

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This course is geared for experienced skilled Java developers, software developers, data scientists, machine learning experts or others who wish to transtion their coding skills to Scala, learning how to code in Scala and apply it in a practical way. This is not a basic class.</p>Overview<p>Working in a hands-on learning environment led by our expert instructor you'll:<br />Get comfortable with Scala's core principles and unique features, helping you navigate the language confidently and boosting your programming skills.<br />Discover the power of functional programming and learn techniques that will make your code more efficient,maintainable, and enjoyable to write.<br />Become proficient in creating dynamic web applications using the Play Framework, and easily connect to databases with the user-friendly Slick library.<br />Master concurrency programming with Akka, empowering you to build scalable and fault-tolerant applications that excel in performance.<br />Enhance your testing skills using ScalaTest and ScalaCheck, ensuring the reliability and quality of your Scala applications, while having fun in the process.<br />Explore the fascinating world of generative AI and GPT technologies, and learn how to integrate them into your projects,adding a touch of innovation and intelligence to your Scala solutions.<br />If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals.</p><p>Discover the power of Scala programming in our comprehensive, hands-on technical training course designed specifically for experienced object-oriented (OO) developers. Scala is a versatile programming language that combines the best of both OO and functional programming paradigms, making it ideal for a wide range of projects, from web applications to big data processing and machine learning. By mastering Scala, you'll be able to develop more efficient, scalable, and maintainable applications. Fast Track to Scala Programming for OO / Java Developers is a four day hands-on course covers the core principles of Scala, functional programming, web application development, database connectivity, concurrency programming, testing, and interoperability between Scala and Java. Additionally, you'll explore cutting-edge generative AI and GPT technologies, learning how to integrate them into your Scala applications for intelligent suggestions or automation. Throughout the course you?ll explore the latest tools and best practices in the Scala ecosystem, gaining valuable knowledge and experience that can be directly applied to your day-to-day work. With 50% of the course content dedicated to hands-on labs, you'll gain practical experience applying the concepts you've learned across various projects, such as building functional web applications, connecting to databases, designing modular components, and implementing concurrency. Upon completing the course, you'll have a solid understanding of the language and its features, empowering you to confidently apply your new skills in data science and machine learning projects. You'll exit well-prepared to create efficient, scalable, and maintainable Scala applications, regardless of the complexity of your projects.</p></div><div id="course-pills-2"><h4>Introduction to Scala</h4><ul><li>Scala features and benefits</li><li>Comparing Scala with Java and other OO languages</li><li>Installing Scala and setting up the development environment</li></ul><h4>Object-Oriented Programming in Scala</h4><ul><li>Classes and objects</li><li>Traits, mixins, and inheritance</li><li>Companion objects and factories</li><li>Encapsulation and polymorphism</li></ul><h4>Functional Programming Basics</h4><ul><li>Pure functions and referential transparency</li><li>Higher-order functions and currying</li><li>Immutability and persistent data structures</li><li>Pattern matching and recursion</li></ul><h4>Having Fun with Functional Data Structures</h4><ul><li>Lists, sets, and maps in Scala</li><li>Folding and reducing operations</li><li>Stream processing and lazy evaluation For-comprehensions</li></ul><h4>Building Web Applications in Functional Style</h4><ul><li>Introduction to Play Framework</li><li>Functional web routing and request handling</li><li>JSON handling with Play-JSON</li><li>Middleware and functional composition</li></ul><h4>Connecting to a Database</h4><ul><li>Introduction to Slick library</li><li>Database configuration and setup</li><li>Querying and updating with Slick</li><li>Transactions and error handling</li></ul><h4>Building Scalable and Extensible Components </h4><ul><li>Modular architecture and design patterns<br />Dependency injection with MacWire<br />Type classes and type-level programming<br />Implicit parameters and conversions</li></ul><h4>Concurrency Programming &amp; Akka</h4><ul><li>Introduction to Akka framework and Actor model</li><li>Actor systems and message passing</li><li>Futures and Promises</li><li>Supervision and fault tolerance</li><li></li></ul><h4>Building Confidence with Testing</h4><ul><li>Introduction to ScalaTest and ScalaCheck</li><li>Unit testing and property-based testing</li><li>Test-driven development in Scala</li><li>Mocking and integration testing</li><li></li></ul><h4>Interoperability between Scala and Java</h4><ul><li>Calling Java code from Scala</li><li>Using Java libraries in Scala projects</li><li>Converting Java collections to Scala collections</li><li>Writing Scala code that can be called from Java</li></ul><h4>Using Generative AI and GPT</h4><ul><li>Technologies in Scala Programming</li><li>Overview of GPT and generative AI</li><li>Integrating GPT with Scala applications</li><li>Use cases and practical examples</li></ul></div>

Nexus Human

Java

Programming Fundamentals

Programming Languages

Software Development

Fast Track to Scala Programming for OO / Java Developers (TTSCL2104)

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is intended for the following participants:<br />Individuals planning to deploy applications and create application environments on Google Cloud Platform<br />Developers, systems operations professionals, and solution architects getting started with Google Cloud Platform<br />Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs.</p>Overview<p>This course teaches participants the following skills:<br />Identify Google Cloud counterparts for Azure IaaS, Azure PaaS, Azure SQL, Azure Blob Storage, Azure Application Insights, and Azure Data Lake<br />Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing,storage, databases, IAM, and more<br />Manage and monitor applications<br />Explain feature and pricing model differences</p><p>This 1-day instructor led course introduces Azure professionals to the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. It is designed for Azure system administrators, Solution Architects and SysOps Administrators familiar with Azure features and setup; and want to gain experience configuring Google Cloud products immediately. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly.</p></div><div id="course-pills-2"><h4>Introducing Google Cloud</h4><ul><li>Explain the advantages of Google Cloud.</li><li>Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones.</li><li>Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS).</li><li></li></ul><h4>Getting Started with Google Cloud</h4><ul><li>Identify the purpose of projects on Google Cloud.</li><li>Understand how Azure's resource hierarchy differs from Google Cloud's</li><li>Understand the purpose of and use cases for Identity and Access Management.</li><li>Understand how Azure AD differs from Google Cloud IAM.</li><li>List the methods of interacting with Google Cloud.</li><li>Launch a solution using Cloud Marketplace.</li></ul><h4>Virtual Machines in the Cloud</h4><ul><li>Identify the purpose and use cases for Google Compute Engine</li><li>Understand the basics of networking in Google Cloud.</li><li>Understand how Azure VPC differs from Google VPC.</li><li>Understand the similarities and differences between Azure VM and Google Compute Engine.</li><li>Understand how typical approaches to load-balancing in Google Cloud differ from those in Azure.</li><li>Deploy applications using Google Compute Engine</li></ul><h4>Storage in the Cloud</h4><ul><li>Understand the purpose of and use cases for: Cloud Storage,</li><li>Cloud SQL, Cloud Bigtable and Cloud Datastore.</li><li>Understand how Azure Blob compares to Cloud Storage.</li><li>Compare Google Cloud?s managed database services with Azure SQL.</li><li>Learn how to choose among the various storage options on Google Cloud.</li><li>Load data from Cloud Storage into BigQuery</li></ul><h4>Containers in the Cloud</h4><ul><li>Define the concept of a container and identify uses for containers.</li><li>Identify the purpose of and use cases for Google Container Engine and Kubernetes.</li><li>Understand how Azure Kubernetes Service differs from from Google Kubernetes Engine.</li><li>Provision a Kubernetes cluster using Kubernetes Engine.</li><li>Deploy and manage Docker containers using kubectl</li></ul><h4>Applications in the Cloud</h4><ul><li>Understand the purpose of and use cases for Google App Engine.</li><li>Contrast the App Engine Standard environment with the App Engine Flexible environment.</li><li>Understand how App Engine differs from Azure App Service.</li><li>Understand the purpose of and use cases for Google Cloud Endpoints.</li><li></li></ul><h4>Developing, Deploying and Monitoring in the Cloud</h4><ul><li>Understand options for software developers to host their source code.</li><li>Understand the purpose of template-based creation and management of resources.</li><li>Understand how Google Cloud Deployment Manager differs from Azure Resource Manager.</li><li>Understand the purpose of integrated monitoring, alerting, and debugging</li><li>Understand how Google Monitoring differs from Azure Application Insights and Azure Log Analytics.</li><li>Create a Deployment Manager deployment.</li><li>Update a Deployment Manager deployment.</li><li>View the load on a VM instance using Google Monitoring.</li></ul><h4>Big Data and Machine Learning in the Cloud</h4><ul><li>Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms.</li><li>Understand how Google Cloud BigQuery differs from Azure Data Lake.</li><li>Understand how Google Cloud Pub/Sub differs from Azure Event Hubs and Service Bus.</li><li>Understand how Google Cloud?s machine-learning APIs differ from Azure's.</li><li>Load data into BigQuery from Cloud Storage.</li><li>Perform queries using BigQuery to gain insight into data</li></ul><h4>Summary and Review</h4><ul><li>Review the products that make up Google Cloud and remember how to choose among them</li><li>Understand next steps for training and certification</li><li>Understand, at a high level, the process of migrating from Azure to Google Cloud.</li></ul></div>

AWS Certified Cloud Practitioner

Google Suite

Google Cloud

Microsoft Azure

Google Workspace

Google Cloud Fundamentals for Azure Professionals

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>The primary audience for this course is as follows:<br />IT Solution Architects<br />Network Security Architects<br />Networking Admin and Operations<br />Networking Field Engineers</p><p>In this workshop you will learn why we are implementing Assurance, and what does looks like and key features. We will also discuss the Cisco DNA center appliance and workflow and tasks associated with an implementation.</p></div><div id="course-pills-2"><h4>Module 1: Introduction to Cisco DNA Center Assurance</h4><ul><li>Introduction to DNA Center</li><li>System Architecture</li><li>Key Features and Use Cases</li><li>Introduction to Cisco ISE and DNAC-ISE integration</li></ul><h4>Module 2: Design</h4><ul><li>Network design options</li><li>Sites</li><li>Creating Enterprise and Sites Hierarchy</li><li>Configuring General Network Settings</li><li>Loading maps into the GUI</li><li>IP Address Management</li><li>Software Image Management ? Lab on Day 5</li><li>Network Device Profiles</li><li>AAA</li><li>SNMP</li><li>Syslog</li><li>IP address pools</li><li>Creating Enterprise and Guest SSIDs ? Lab on Day 2 </li><li>Creating the wireless RF Profile</li><li>Cresting the Guest Portal for the Guest SSIDs</li><li>Network profiles ? Lab on Day 2</li><li>Authentication templates</li></ul><h4>Module 3: Discovery and Provision</h4><ul><li>Device Discovery and Protocols</li><li>Devices Onboarding</li><li>Assigning Devices to a site</li><li>Provisioning device with profiles</li><li>Plug and Play</li><li>Templates</li><li>Templates for Day-0</li><li>Templates for Day-N operations</li></ul><h4>Module 4: Monitoring Device, Client, and Application Health</h4><ul><li>DNAC Telemetry<br />Monitoring Network Device Health and Performance<br />Monitoring Wired and Wireless Client Health and Performance<br />Monitoring Application Health and Performance</li></ul><h4>Module 5: Application Visibility and DNAC Integration with Umbrella</h4><ul><li>Monitoring Application Visibility in DNA Center<br />Umbrella Introduction<br />DNA Center Umbrella Integration and Use Cases</li></ul><h4>Module 6: Troubleshooting Issues, Observing Insights and Trends</h4><ul><li>Detect Issues, Insights, and Trends in the Network<br />DNA Center Integration with other tools for Monitoring and Management</li></ul><h4>Module 7: Troubleshooting using Cisco DNA Center Assurance Tools</h4><ul><li>DNAC Assurance Troubleshooting Tools<br />Using Sensor Tests<br />Using Intelligent Capture <br />Spectrum Analysis</li></ul><h4>Module 8: DNAC and Thousand Eyes</h4><ul><li>Introduction to ThousandEyes<br />ThousandEyes Use Cases<br />DNA Center and ThousandEyes Integration</li></ul><h4>Module 9: AI Analytics and Machine Learning in DNA Center</h4><ul><li>Overview of AI Analytics and ML (Machine Learning)<br />DNA Center AI and ML Use Cases</li></ul><h4>Module 10: DNA Center Maintenance</h4><ul><li>DNA Center Reports<br />DNA Center Backup and Restore</li></ul></div>

Cisco Certified Network Associate (CCNA)

AWS Certification

CompTIA Network+

IT Certifications

Cisco Certified Internetwork Expert (CCIE)

AWS Certified Solutions Architect

Cisco Implementing and Administering DNA Center for Assurance v2.0 (IADNACA)

<section>
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					<div><p>In this captivating course, you will embark on an exciting adventure through the Python programming language. Our journey begins with Section 1, 'Introduction &amp; Getting Started,' where you'll uncover the magic of Python and set the stage for your coding expedition. As you progress through the course, you'll explore essential concepts such as variables, data types, and conditional statements, all while acquiring the skills to craft your own Python programs.</p><p>With engaging exercises like building a program to say hello and creating a simple calculator, you'll gain hands-on experience, solidifying your newfound knowledge. Sections 10 and 11 delve into the powerful world of loops, covering both for and while loops, providing you with the tools to automate tasks and solve real-world problems. To put your skills to the test, Section 12 guides you in building a Username Password App, demonstrating the practical application of Python in everyday life.</p></div>
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			<h2><b> Learning outcome</b></h2>		</div>
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										<span>Master the fundamentals of Python programming.</span>
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										<span>Create Python programs to solve practical problems.</span>
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										<span>Understand variables, data types, and conditional statements.</span>
									</li>
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										<span>Harness the power of loops to automate tasks.</span>
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										<span>Build a Username Password App for real-world use.</span>
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						</ul>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
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										<span>Jumpstart your coding journey with a beginner-friendly Python course.</span>
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										<span>Build a strong foundation for further programming exploration.</span>
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										<span>Gain practical Python skills for everyday applications.</span>
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										<span>Challenge yourself with engaging exercises and real-world projects.</span>
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										<span>Open doors to a world of opportunities in technology and software development.</span>
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			<h2> <b>Career Path</b></h2>		</div>
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										<span>Python Developer</span>
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										<span>Data Analyst</span>
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										<span>Web Developer</span>
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										<span>Software Engineer</span>
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										<span>Machine Learning Engineer</span>
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			<h2> <b>Requirements</b></h2>		</div>
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										<span>Access to a computer with an internet connection.</span>
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										<span>Basic familiarity with using a computer.</span>
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										<span>Eagerness to learn and explore the world of programming.</span>
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		</section><div>
                            <div>
                                <h2>Course Curriculum</h2>
                            </div>
                            			<div>
				

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<table width="100%">				<tbody><tr>
					<td colspan="4"><strong>Section 1: Introduction &amp; Getting Started</strong></td>
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					<td>Unit 1: Introduction</td><td><span>🕐 00:03:00</span></td>
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					<td>Unit 2: Instructor's Introduction</td><td><span>🕐 00:03:00</span></td>
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					<td>Unit 3: Why Python</td><td><span>🕐 00:03:00</span></td>
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					<td colspan="4"><strong>Section 2: Downloading and Installing Python Editor</strong></td>
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								<tr>
					<td>▶</td>
					<td>Unit 1: Downloading and Installing Python</td><td><span>🕐 00:12:00</span></td>
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					<td colspan="4"><strong>Section 3: Getting Started</strong></td>
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					<td>Unit 1: Hello World and Help Function</td><td><span>🕐 00:09:00</span></td>
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					<td colspan="4"><strong>Section 4: Variables and Basic Data Types in Python</strong></td>
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					<td>Unit 1: Variables and Basic Data Types in Python</td><td><span>🕐 00:12:00</span></td>
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					<td>Unit 1: Commenting Your Code</td><td><span>🕐 00:07:00</span></td>
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					<td>Unit 1: Exercise - Build a Program to Say Hello</td><td><span>🕐 00:05:00</span></td>
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					<td>Unit 1: Exercise - Build a Simple Calculator App</td><td><span>🕐 00:08:00</span></td>
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					<td>Unit 1: Loops - While Loop</td><td><span>🕐 00:08:00</span></td>
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					<td colspan="4"><strong>Section 11: Loops - While Loop</strong></td>
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					<td>Unit 1: Loops - While Loop</td><td><span>🕐 00:08:00</span></td>
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					<td colspan="4"><strong>Section 12: Exercise - Building a Username Password App</strong></td>
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					<td>Unit 1: Exercise - Building a Username and Password App</td><td><span>🕐 00:05:00</span></td>
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Skill Up

iscover the fascinating world of Python programming as you take your first steps into the realm of coding. This course is designed to ignite your passion for programming, providing a solid foundation for beginners. Get ready to unleash your creative potential in the world of technology.

Python Crash Course - Level 1

<p>Are you looking to enhance your Python- Beginner to Advance skills? If yes, then you have come to the right place. Our comprehensive course on Python- Beginner to Advance will assist you in producing the best possible outcome by mastering the Python- Beginner to Advance skills.</p><p>The Python- Beginner to Advance course is for those who want to be successful. In the Python- Beginner to Advance course, you will learn the essential knowledge needed to become well versed in Python- Beginner to Advance.</p><p>Our course starts with the basics of Python- Beginner to Advance and gradually progresses towards advanced topics. Therefore, each lesson of this Python- Beginner to Advance course is intuitive and easy to understand.</p><p><strong>Why would you choose the Python- Beginner to Advance course from Compliance Central:</strong></p><ul><li>Lifetime access to Python- Beginner to Advance course materials</li><li>Full tutor support is available from Monday to Friday with the Python- Beginner to Advance course</li><li>Learn Python- Beginner to Advance skills at your own pace from the comfort of your home</li><li>Gain a complete understanding of Python- Beginner to Advance course</li><li>Accessible, informative Python- Beginner to Advance learning modules designed by experts</li><li>Get 24/7 help or advice from our email and live chat teams with the Python- Beginner to Advance course</li><li>Study Python- Beginner to Advance in your own time through your computer, tablet or mobile device</li><li>A 100% learning satisfaction guarantee with your Python- Beginner to Advance course</li></ul><p><strong>Curriculum Breakdown of the Python- Beginner to Advance Course</strong></p><ul><li>Introduction</li><li>Curriculum Overview</li><li>What's New</li><li>command line basics</li><li>python installation</li><li>Pycham-ce ide installation</li><li>Setting up environment</li><li>Running python code</li><li>git and github overview</li><li>Python Data Types</li><li>Python Arithmetic Operators Numbers</li><li>Variable Assignments</li><li>Strings Introduction</li><li>Indexing and Slicing with Strings</li><li>String Properties and Methods</li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
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                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
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                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>The Python- Beginner to Advance course helps aspiring professionals who want to obtain the knowledge and familiarise themselves with the skillsets to pursue a career in Python- Beginner to Advance. It is also great for professionals who are already working in Python- Beginner to Advance and want to get promoted at work.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>To enrol in this Python- Beginner to Advance course, all you need is a basic understanding of the English Language and an internet connection.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>The Python- Beginner to Advance course will enhance your knowledge and improve your confidence in exploring opportunities in various sectors.</p><ul><li>Python Developer: £35,000 to £70,000 per year</li><li>Data Analyst: £25,000 to £55,000 per year</li><li>Machine Learning Engineer: £45,000 to £85,000 per year</li><li>Software Engineer: £40,000 to £75,000 per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited PDF Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p><strong>CPD Accredited PDF Certificate</strong></p></div>
        <h4>CPD Accredited Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            £10.79
        </p>
            <div><p><strong>CPD Accredited Hard Copy Certificate</strong></p><p><strong>Delivery Charge:</strong></p><p>Inside the UK: Free</p><p>Outside of the UK: £9.99 each</p></div>

                        </div>

Compliance Central

Python- Beginner to Advance

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>This course is designed for people who want to learn the Python programming language in preparation for using Python to develop software for a wide range of applications, such as data science, machine learning, artificial intelligence, and web development.</p>Overview<p>In this course, you will develop simple command-line programs in Python. You will:<br />Set up Python and develop a simple application.<br />Declare and perform operations on simple data types, including strings, numbers, and dates.<br />Declare and perform operations on data structures, including lists, ranges, tuples, dictionaries, and sets.<br />Write conditional statements and loops.<br />Define and use functions, classes, and modules.<br />Manage files and directories through code.<br />Deal with exceptions.</p><p>Though Python has been in use for nearly thirty years, it has become one of the most popular languages for software development, particularly within the fields of data science, machine learning, artificial intelligence, and web development?all areas in which Python is widely used. Whether you're relatively new to programming, or have experience in other programming languages, this course will provide you with a comprehensive first exposure to the Python programming language that can provide you with a quick start in Python, or as the foundation for further learning.<br /><br />You will learn elements of the Python 3 language and development strategies by creating a complete program that performs a wide range of operations on a variety of data types, structures, and objects, implements program logic through conditional statements and loops, structures code for reusability through functions, classes, and modules, reads and writes files, and handles error conditions.</p></div><div id="course-pills-2"><h4>Lesson 1: Setting Up Python and Developing a Simple Application</h4><ul><li>Topic A: Set Up the Development Environment</li><li>Topic B: Write Python Statements</li><li>Topic C: Create a Python Application</li><li>Topic D: Prevent Errors</li></ul><h4>Lesson 2: Processing Simple Data Types</h4><ul><li>Topic A: Process Strings and Integers</li><li>Topic B: Process Decimals, Floats, and Mixed Number Types</li></ul><h4>Lesson 3: Processing Data Structures</h4><ul><li>Topic A: Process Ordered Data Structures</li><li>Topic B: Process Unordered Data Structures</li></ul><h4>Lesson 4: Writing Conditional Statements and Loops in Python</h4><ul><li>Topic A: Write a Conditional Statement</li><li>Topic B: Write a Loop</li></ul><h4>Lesson 5: Structuring Code for Reuse</h4><ul><li>Topic A: Define and Call a Function</li><li>Topic B: Define and Instantiate a Class</li><li>Topic C: Import and Use a Module</li></ul><h4>Lesson 6: Writing Code to Process Files and Directories</h4><ul><li>Topic A: Write to a Text File</li><li>Topic B: Read from a Text File</li><li>Topic C: Get the Contents of a Directory</li><li>Topic D: Manage Files and Directories</li></ul><h4>Lesson 7: Dealing with Exceptions</h4><ul><li>Topic A: Handle Exceptions</li><li>Topic B: Raise Exceptions</li></ul></div>

Online Course Creation

Computer Programming

Continuous Professional Development (CPD)

Introduction to Programming with Python (v1.01)

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Data Engineering

Microsoft AZ-900

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

DP-100T01 Designing and Implementing a Data Science Solution on Azure

Data Science

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Python Machine Learning Bootcamp

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Machine Learning with Real World Projects

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
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					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Cloud

Machine Learning (ML)

Machine Learning for Apps Level 4

<strong>The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.</strong>
        <p>Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects.

You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project.

By the end of this course, you'll have created a face recognition application using everything you've learned in this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Python Machine Learning Crash Course for Beginners

<strong>Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis</strong>
        <p>Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. 

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. 

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! 

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

Machine Learning 101 with Scikit-learn and StatsModels

<strong>In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.</strong>
        <p>Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library.

The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms.

By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine Learning for Absolute Beginners - Level 2

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

The Complete Machine Learning Course with Python

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					<div><p>The comprehensive Machine Learning - Use in Flutter has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Machine Learning - Use in Flutter today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Machine Learning - Use in Flutter, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning - Use in Flutter will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Machine Learning - Use in Flutter to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Learning & Development

Learning To Learn

Machine Learning - Use in Flutter

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

Deep Learning

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

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