Name: Deep Learning with Real-World Projects
Price: 338.99 GBP
Availability: InStock

<p>Unlock the power of machine learning with our Machine Learning with Python course. In this course, you'll learn the fundamentals of machine learning, exploring the life cycle of developing a machine learning model from scratch. With a comprehensive curriculum that covers everything from Numpy and Pandas libraries to decision tree classifiers and K-means clustering, this course is perfect for those looking to upskill in the latest technology.</p><p>Our course modules are designed to provide you with a hands-on learning experience. You'll learn how to create arrays from scratch, manipulate data using Numpy and Pandas libraries, and build and evaluate machine learning models. You'll also explore the world of data analysis with Pandas DataFrame and learn how to scale and encode data using the Sklearn pipeline and column transformer. By the end of this course, you'll have a deep understanding of how to build and evaluate machine learning models using Python.</p><p>This course is perfect for aspiring data scientists, machine learning engineers, and developers who want to gain real-world experience in machine learning development. With the skills and knowledge gained from this course, you'll be able to start a career in the exciting field of machine learning and contribute to the development of cutting-edge technology.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Understand the fundamentals of machine learning.</li><li>Manipulate data using Numpy and Pandas libraries.</li><li>Build and evaluate machine learning models using Sklearn pipeline and column transformer.</li><li>Explore the world of data analysis with Pandas DataFrame.</li><li>Contribute to the development of cutting-edge technology in the field of machine learning.</li></ul><p>The Machine Learning with Python course is designed to provide you with the skills and knowledge needed to develop and evaluate machine learning models using Python. In this course, you'll learn how to manipulate data using Numpy and Pandas libraries, build and evaluate machine learning models using Sklearn pipeline and column transformer, and explore the world of data analysis with Pandas DataFrame. The course is perfect for aspiring data scientists, machine learning engineers, and developers interested in machine learning development.</p><p>By the end of this course, you'll have a deep understanding of the fundamentals of machine learning and how to contribute to the development of cutting-edge technology in this exciting field. With hands-on experience in manipulating data and building and evaluating machine learning models, you'll be well-equipped to start a career in machine learning.</p><p><strong>â±â± Machine Learning with Python Course</strong><strong> Course Curriculum </strong></p><ul><li><strong>Section 01: </strong>Introduction<strong><br /></strong></li><li><strong>Section 02: </strong>Numpy Library<strong><br /></strong></li><li><strong>Section 03: </strong>Matplotlib<strong><br /></strong></li><li><strong>Section 04: </strong>Polynomial Regression</li></ul><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.</p><p><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Aspiring data scientists.</li><li>Machine learning engineers.</li><li>Developers interested in machine learning development.</li><li>Anyone interested in the field of machine learning.</li><li>Professionals looking to upskill in the latest technology.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Aspiring data scientists.</li><li>Machine learning engineers.</li><li>Developers interested in machine learning development.</li><li>Anyone interested in the field of machine learning.</li><li>Professionals looking to upskill in the latest technology.</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

One Education

Machine Learning with Python Course

General Admission

<strong>This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.</strong>
        <p>Welcome to the course where you will learn about the NumPy stack in Python, which is an important prerequisite for deep learning, machine learning, and data science.

In this self-paced course, you will learn how to use NumPy, Matplotlib, Pandas, and SciPy to perform critical tasks related to data science and machine learning. This involves performing numerical computation and representing data, visualizing data with plots, loading in, and manipulating data using DataFrames, performing statistics and probability, and building machine learning models for classification and regression.

In this course, we will first start with NumPy; we will understand the benefits of NumPy array and then we will look at some complicated matrix operations, such as products, inverses, determinants, and solving linear systems.

Then we will cover Matplotlib. In this section, we will go over some common plots, namely the line chart, scatter plot, and histogram. We will also look at how to show images using Matplotlib.

Next, we will talk about Pandas. We will look at how much easier it is to load a dataset using Pandas versus trying to do it manually. Then we will look at some data frame operations useful in machine learning, such as filtering by column, filtering by row, and the apply function.

Later, you will learn about SciPy. In this section, you will learn how to do common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing.

Finally, we will also cover some basics of machine learning that will help us start our deep learning journey.

By the end of the course, we will be able to confidently use the NumPy stack in deep learning and data science.</p>
        <h2>What You Will Learn</h2><p>Understand supervised machine learning with real-world examples<br>Understand and code using the NumPy stack<br>Make use of NumPy, SciPy, Matplotlib, and Pandas to implement numerical algorithms<br>Understand the pros and cons of various machine learning models<br>Get a brief introduction to the classification and regression<br>Learn how to calculate the PDF and CDF under the normal distribution</p>
        <h2>Audience</h2><p>This course is designed for anyone who is interested in data science and machine learning, who knows Python and wants to take the next step into Python libraries for data science, or who is interested in acquiring tools to implement machine learning algorithms.<br><br>One must have decent Python programming skills and a basic understanding of linear algebra and probability for this course.</p>
        <h2>Approach</h2><p>You will learn how to use NumPy, Matplotlib, Pandas, and SciPy to carry out crucial data science and machine learning tasks in this self-paced course. The course is well-balanced with both theoretical and practical coding exercises. Each section, we first cover the theory concept and demonstrate it using a real-world example for better understanding.</p>
        <h2>Key Features</h2><p>Study basics of machine learning and understand how to use the NumPy stack for deep learning in data science * Learn how to use NumPy, Matplotlib, Pandas, and SciPy for critical tasks in data science and machine learning * Perform numerical computations, visualize data, load, and manipulate datasets using Pandas</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer is an AI/ML engineer focusing on deep learning with experience in data science, big data engineering, and full-stack development. With a background in computer engineering and specialization in ML, he holds two master's degrees in computer engineering and statistics with finance applications. His online advertising and digital media expertise include data science and big data.

He has created DL models for prediction and has experience in recommender systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He is a web programmer with experience in Python, Ruby/Rails, PHP, and Angular.</p>
        <h2>Course Outline</h2><h4>1. Welcome and Logistics</h4><p>Welcome to the course! In this section, we will get introduced to the course goals.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction and Outline</p><p style='margin: 0'>In this video, we will get introduced to the course and understand the learning objective.</p></td></tr></tbody></table><br><h4>2. NumPy</h4><p>In this section of the course, we will dive into the world of NumPy, a powerful numerical computing library in Python that is widely used in data science and machine learning.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. NumPy Section Introduction</p><p style='margin: 0'>In this video, we will have a quick overview of NumPy.</p></td></tr><tr><td style='padding: 15px;'><p>2. Arrays Versus Lists</p><p style='margin: 0'>In this video, we will be comparing NumPy array to Python list.</p></td></tr><tr><td style='padding: 15px;'><p>3. Dot Product</p><p style='margin: 0'>In this video, we will discuss Dot Product in NumPy.</p></td></tr><tr><td style='padding: 15px;'><p>4. Speed Test</p><p style='margin: 0'>In this video, we will do a speed test to measure how much faster NumPy is than regular Python list in terms of the dot product.</p></td></tr><tr><td style='padding: 15px;'><p>5. Matrices</p><p style='margin: 0'>In this video, you will learn about matrices.</p></td></tr><tr><td style='padding: 15px;'><p>6. Solving Linear Systems</p><p style='margin: 0'>In this video, we will work on solving an example problem to understand linear systems.</p></td></tr><tr><td style='padding: 15px;'><p>7. Generating Data</p><p style='margin: 0'>In this video, we will discuss about generatingabout how to generate data.</p></td></tr><tr><td style='padding: 15px;'><p>8. NumPy Exercise</p><p style='margin: 0'>In this video, we will look at the NumPy exercise that must be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>9. Where to Learn More NumPy</p><p style='margin: 0'>In this video, we will understand why we need NumPy and how it is applied in more advanced scenarios.</p></td></tr></tbody></table><br><h4>3. Matplotlib</h4><p>In this section, we will talk about Matplotlib.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Matplotlib Section Introduction</p><p style='margin: 0'>In this video, we will have a quick overview of Matplotlib that is used to visualize data and see what we are going to cover in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Line Chart</p><p style='margin: 0'>In this video, you will learn about a line chart.</p></td></tr><tr><td style='padding: 15px;'><p>3. Scatterplot</p><p style='margin: 0'>In this video, you will learn about a scatterplot.</p></td></tr><tr><td style='padding: 15px;'><p>4. Histogram</p><p style='margin: 0'>In this video, you will learn about a histogram.</p></td></tr><tr><td style='padding: 15px;'><p>5. Plotting Images</p><p style='margin: 0'>In this video, you will learn how to plot images.</p></td></tr><tr><td style='padding: 15px;'><p>6. Matplotlib Exercise</p><p style='margin: 0'>In this video, we will have a look at the Matplotlib exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>7. Where to Learn More Matplotlib</p><p style='margin: 0'>In this video, we will understand from where we can learn more about Matplotlib.</p></td></tr></tbody></table><br><h4>4. Pandas</h4><p>In this section, we will talk about Pandas.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Pandas Section Introduction</p><p style='margin: 0'>In this video, we will be introduced to Pandas and understand this section''s learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Loading in Data</p><p style='margin: 0'>In this video, you will learn how to load in data using Pandas.</p></td></tr><tr><td style='padding: 15px;'><p>3. Selecting Rows and Columns</p><p style='margin: 0'>In this video, you will learn how to select rows and columns from a data frame.</p></td></tr><tr><td style='padding: 15px;'><p>4. The apply() Function</p><p style='margin: 0'>In this video, you will learn about the apply() function.</p></td></tr><tr><td style='padding: 15px;'><p>5. Plotting with Pandas</p><p style='margin: 0'>In this video, you will learn how to plot with Pandas.</p></td></tr><tr><td style='padding: 15px;'><p>6. Pandas Exercise</p><p style='margin: 0'>In this video, we will have a look at the Pandas exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>7. Where to Learn More Pandas</p><p style='margin: 0'>In this video, we will understand from where we can learn more about Pandas.</p></td></tr></tbody></table><br><h4>5. SciPy</h4><p>In this section, we will talk about SciPy.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. SciPy Section Introduction</p><p style='margin: 0'>In this video, we will be introduced to SciPy and understand this section''s learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. PDF and CDF</p><p style='margin: 0'>In this video, you will learn how to calculate PDF and CDF under normal distribution.</p></td></tr><tr><td style='padding: 15px;'><p>3. Convolution</p><p style='margin: 0'>In this video, you will learn how to apply convolution to an image to create a blurring filter.</p></td></tr><tr><td style='padding: 15px;'><p>4. SciPy Exercise</p><p style='margin: 0'>In this video, we will have a look at the SciPy exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>5. Where to Learn More SciPy</p><p style='margin: 0'>In this video, we will summarize what we have learnt about SciPy and understand where it can be applied.</p></td></tr></tbody></table><br><h4>6. Machine Learning Basics</h4><p>In this section, we will cover machine learning basics.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Machine Learning: Section Introduction</p><p style='margin: 0'>In this video, we will understand what machine learning is and the learning objective of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Is Classification?</p><p style='margin: 0'>In this video, we will understand classification.</p></td></tr><tr><td style='padding: 15px;'><p>3. Classification in Code</p><p style='margin: 0'>In this video, we will practice all that we have learnt in the previous video about classification.</p></td></tr><tr><td style='padding: 15px;'><p>4. What Is Regression?</p><p style='margin: 0'>In this video, we will understand what regression is.</p></td></tr><tr><td style='padding: 15px;'><p>5. Regression in Code</p><p style='margin: 0'>In this video, we will practice all that we have learnt in the previous video about regression.</p></td></tr><tr><td style='padding: 15px;'><p>6. What Is a Feature Vector?</p><p style='margin: 0'>In this video, you will learn about a feature vector.</p></td></tr><tr><td style='padding: 15px;'><p>7. Machine Learning Is Nothing but Geometry.</p><p style='margin: 0'>In this video, we will understand that machine learning is nothing but a geometry problem and see how it works for classification and regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. All Data Is the Same</p><p style='margin: 0'>In this video, we will understand how all data is the same as all machine learning interfaces are the same.</p></td></tr><tr><td style='padding: 15px;'><p>9. Comparing Different Machine Learning Models</p><p style='margin: 0'>In this video, we will understand how all machine learning interfaces are the same.</p></td></tr><tr><td style='padding: 15px;'><p>10. Machine Learning and Deep Learning: Future Topics</p><p style='margin: 0'>In this video, you will learn what other types of machine learning exist and some future topics that we can look forward to learning.</p></td></tr><tr><td style='padding: 15px;'><p>11. Machine Learning: Section Summary</p><p style='margin: 0'>In this video, we will summarize our learning from this section.</p></td></tr></tbody></table>

Packt

Data Science

Python

Pandas

Deep Learning

This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.

Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python

<p>In today's data-driven world, 82% of businesses demand Data Science professionals - and they're willing to pay for it! With average global salaries of Data Scientists and Machine Learning experts surpassing $120,000 annually, it's clear: the future belongs to those who understand data.</p><p>Our Advanced Diploma in Data Science &amp; Machine Learning - Level 7 is meticulously designed to offer profound knowledge and skills . This Data Science &amp; Machine Learning bundle encompasses a comprehensive selection, from the nuances of Statistics &amp; Probability to the intriguing applications of Machine Learning in Flutter. Also, this comprehensive Data Science &amp; Machine Learning bundle ensures you're well-equipped with the essential knowledge required in today's competitive global market.</p><p>By opting for this Data Science &amp; Machine Learning advanced diploma, you're not just learning - you're investing in a future filled with opportunities, lucrative incomes, and immense professional demand globally.</p><p><strong>Data Science &amp; Machine Learning Course Learning Outcomes</strong><strong><br /></strong></p><ul><li>Grasp the fundamentals of Statistics &amp; Probability.</li><li>Acquire proficiency in Python for Data Science.</li><li>Understand R programming in data analytics context.</li><li>Dive deep into MySQL and its applications in Data Science.</li><li>Master SQL techniques for Data Visualization and Analytics.</li><li>Lay a strong foundation in basic machine learning concepts.</li><li>Navigate through Azure Machine Learning and its utilities.</li></ul><p>This <strong>Data Science &amp; Machine Learning </strong>bundle course, however, is designed to be your compass, guiding you through the intricacies of the most pivotal topics. With the Advanced Diploma, you delve deep into the mathematical bedrock of data science through Statistics &amp; Probability. Python and R, the stalwarts of data programming, are unravelled, offering insights into their unique capabilities. SQL's unmatched prowess in data visualisation and analytics is highlighted, while Azure provides a window into the world of cloud-based machine learning. Lastly, understand the synergy of machine learning and mobile applications through Flutter.</p><p>The benefits? A holistic understanding, positioning you ahead in the academic sphere and offering a competitive edge should you venture into research or further studies.</p><p><strong>Courses in this Data Science &amp; Machine Learning bundle: </strong></p><ul><li>Advanced Diploma in Statistics &amp; Probability for Data Science &amp; Machine Learning at QLS Level 7</li><li>Data Science &amp; Machine Learning with Python</li><li>R Programming for Data Science</li><li>Learn MySQL from Scratch for Data Science and Analytics</li><li>SQL for Data Science, Data Analytics and Data Visualization</li><li>Machine Learning Basics</li><li>"Azure Machine Learning"</li><li>Machine Learning use in Flutter, The Complete Guide</li></ul><p><strong>Certificate of Achievement</strong></p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme</strong></p><p>Learners will be able to achieve an endorsed certificate after completing the Data Science &amp; Machine Learning course as proof of their achievement. You can order the endorsed certificate for only £129 to be delivered to your home by post. For international students, there is an additional postage charge of £10.</p><p><strong>Endorsement</strong></p><p>The Quality Licence Scheme (QLS) has endorsed this Data Science &amp; Machine Learning course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the Data Science &amp; Machine Learning course has been reviewed and approved by the QLS and meets the highest quality standards.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>This Data Science &amp; Machine Learning bundle is ideal for:</p><ul><li>Aspiring Data Scientists.</li><li>Statisticians looking to expand into Machine Learning.</li><li>Developers keen on integrating Machine Learning in apps.</li><li>Research scholars in data-driven fields.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>To enrol in this Data Science &amp; Machine Learning 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">
                            <ul><li>Data Scientist</li><li>Machine Learning Engineer</li><li>Data Analyst</li><li>SQL Database Administrator</li><li>Research Scientist (Data Specialization)</li><li>Flutter Developer with ML Integration</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>8 CPD Accredited PDF Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p><strong>Delivery Charge: </strong></p><ul><li>Inside the UK: £3.99</li><li>Outside the UK: £9.99</li></ul></div>
        <h4>QLS Endorsed Hard Copy  Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p><strong>Delivery Charge: </strong></p><ul><li>Inside the UK: Free</li><li>Outside the UK: £9.99</li></ul></div>
        <h4>Other CPD Accredited Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            £9.99
        </p>
            <div><p><strong>Free Courses Hard Copy Certificates Are £9.99 Each.</strong></p><p><strong>Delivery Charge: </strong></p><ul><li>Inside the UK: Free</li><li>Outside the UK: £9.99</li></ul></div>

                        </div>

Compliance Central

Advanced Diploma in Data Science & Machine Learning - Level 7

<h2>Managing Successful Machine Learning Projects</h2>
<p>Machine learning projects are a different beast. You have to secure access to the required data, often from multiple siloed sources. You have to switch back and forth between research mode and execution mode. You have to delicately guide data exploration towards a well-defined machine learning objective. You have to align this machine learning objective with your business objectives. You have to ensure that any sensitive data is adequately protected. How do you tame this beast and lead your project to successful completion?</p>
<p>In this presentation, Dr. Neeraj Kashyap will share some practical tips for succeeding at machine learning, gained from his years at Google and in healthcare. We will discuss the life cycles of healthy machine learning projects and unhealthy ones so that you can identify impending disasters and avert them before they get out of hand. Throughout the session, we will emphasize data privacy, because no amount of intelligence is worth compromising your users for.</p>

IIL Europe Ltd

Managing Successful Machine Learning Projects

<p>Do you want to prepare for your dream job but strive hard to find the right courses? Then, stop worrying, for our strategically modified<strong> SQL, Python and Machine Learning</strong> bundle will keep you up to date with the relevant knowledge and most recent matters of this emerging field. So, invest your money and effort in our <strong> 37 </strong>course mega bundle that will exceed your expectations within your budget.</p><p>The<strong> SQL, Python and Machine Learning</strong> related fields are thriving across the UK, and recruiters are hiring the most knowledgeable and proficient candidates. It's a demanding field with magnitudes of lucrative choices. If you need more guidance to specialise in this area and need help knowing where to start, then <strong>StudyHub </strong>proposes a preparatory bundle. This comprehensive <strong>SQL, Python and Machine Learning</strong> bundle will help you build a solid foundation to become a proficient worker in the sector.</p><p><strong>This SQL, Python and Machine Learning Bundle consists of the following</strong><strong> 30 </strong><strong>CPD Accredited Premium courses - </strong></p><ul><li>Course 01 : IT Support Technician</li><li>Course 02 : Network Security and Risk Management</li><li>Course 03 : CompTIA Network</li><li>Course 04 : Information Management</li><li>Course 05 : Web Application Penetration Testing Course</li><li>Course 06 : CompTIA Cloud+ (CV0-002)</li><li>Course 07 : SQL Server 101 : Microsoft SQL Server for Absolute Beginners</li><li>Course 08 : Ruby on Rails Web Application Framework</li><li>Course 09 : U&amp;P AI - Natural Language Processing (NLP) with Python</li><li>Course 10 : Typescript</li><li>Course 11 : Learn Spring &amp; Angular Material with a Full Web Application</li><li>Course 12 : ASP.Net MVC and Entity Framework Course</li><li>Course 13 : Learn Ethical Hacking with Kali Linux</li><li>Course 14 : Computer Operating System and Troubleshooting</li><li>Course 15 : Building Your Own Computer Course</li><li>Course 16 : It: Ethical Hacking, IT Security and IT Cyber Attacking</li><li>Course 17 : SQL Database Administrator</li><li>Course 18 : MS Word Essentials - The Complete Word Course - Level 3</li><li>Course 19 : Data Protection (GDPR) Practitioner</li><li>Course 20 : Website Hacking From Scratch</li><li>Course 21 : Diploma in Front-End Web Development (HTML, CSS, &amp; Bootstrap)</li><li>Course 22 : Data Science and Visualisation with Machine Learning</li><li>Course 23 : SQL For Data Analytics &amp; Database Development</li><li>Course 24 : PLC Programming Diploma</li><li>Course 25: Cisco ASA Firewall Training Course</li><li>Course 26: Create Your First Responsive Website</li><li>Course 27: C# (C-Sharp)</li><li>Course 28: Web Design (HTML, CSS, Bootstrap) Complete Course</li><li>Course 29: Python Course</li><li>Course 30: JavaScript</li></ul><p><strong>7 </strong><strong>Extraordinary Career Oriented courses that will assist you in reimagining your thriving techniques-</strong></p><ul><li>Course 1: Career Development Plan Fundamentals</li></ul><ul><li>Course 2: CV Writing and Job Searching</li></ul><ul><li>Course 3: Interview Skills: Ace the Interview</li></ul><ul><li>Course 4: Video Job Interview for Job Seekers</li></ul><ul><li>Course 5: Create a Professional LinkedIn Profile</li></ul><ul><li>Course 6: Business English Perfection Course</li></ul><ul><li>Course 7: Networking Skills for Personal Success</li></ul><p><strong>Learning Outcome</strong></p><p><strong>This tailor-made </strong><strong>SQL, Python and Machine Learning</strong><strong> bundle will allow you to-</strong></p><ul><li>Uncover your skills and aptitudes to break new ground in the related fields</li><li>Deep dive into the fundamental knowledge</li><li>Acquire some hard and soft skills in this area</li><li>Gain some transferable skills to elevate your performance</li><li>Maintain good report with your clients and staff</li><li>Gain necessary office skills and be tech savvy utilising relevant software</li><li>Keep records of your work and make a report</li><li>Know the regulations around this area</li><li>Reinforce your career with specific knowledge of this field</li><li>Know your legal and ethical responsibility as a professional in the related field</li></ul> <p>This SQL, Python and Machine Learning Bundle resources were created with the help of industry experts, and all subject-related information is kept updated on a regular basis to avoid learners from falling behind on the latest developments.</p><p><strong>Certification</strong></p><p>After studying the complete training you will be able to take the assessment. After successfully passing the assessment you will be able to claim all courses pdf certificates and <strong>1 hardcopy certificate for the Title Course completely free</strong>. Other Hard Copy certificates need to be ordered at an additional cost of •8.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Ambitious learners who want to strengthen their CV for their desired job should take advantage of the SQL, Python and Machine Learning bundle! This bundle is also ideal for professionals looking for career advancement. Computer Hacking Forensic Investigator (CHFI)</p>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>To participate in this course, all you need is -</p><ul><li>A smart device</li><li>A secure internet connection</li><li>And a keen interest in SQL, Python and Machine Learning</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Upon completing this essential Bundle, you will discover a new world of endless possibilities. These courses will help you to get a cut above the rest and allow you to be more efficient in the relevant fields.</p>
                        </div>

Studyhub UK

Learning & Development

Microsoft Certification

Computing Skills

Computer Skills

Professional Skills Course (PSC)

Trainers Course

CPD Certified Ethical Hacking, SQL, Python and Machine Learning

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					<div><p>The comprehensive Level-3 Machine Learning Course with Python 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 Level-3 Machine Learning Course with Python today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Level-3 Machine Learning Course with Python has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Level-3 Machine Learning Course with Python is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Level-3 Machine Learning Course with Python 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 Level-3 Machine Learning Course with Python 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 Level-3 Machine Learning Course with Python, 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 Level-3 Machine Learning Course with Python 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 Level-3 Machine Learning Course with Python 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>
				</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>

Course Cloud

Level-3 Machine Learning Course with Python

Get involved in a learning adventure, mastering R from foundational basics to advanced techniques. This course is a gateway to the realm of data science. Explore statistical machine learning models and intricacies of deep learning and create interactive Shiny apps. Unleash the power of R and elevate your proficiency in data-driven decision-making.

R Ultimate 2023 - R for Data Science and Machine Learning

<p>Picture a world of endless possibilities; a realm where knowledge is power, and data reigns supreme. In today's digital age, the ability to decipher complex data sets and extract valuable insights has become an invaluable skill. With the Data Analysis with Data Science &amp; Machine Learning bundle, you're one step closer to harnessing the full potential of data and unlocking a brighter future.</p><p>The age of data is upon us, and the UK is no exception. As businesses and organisations increasingly rely on data to inform strategic decisions, professionals skilled in data analysis and machine learning are in high demand. By acquiring expertise in these fields, you'll not only enhance your career prospects but also contribute to the growth and success of the nation.</p><p>Don't miss out on this opportunity to take your career to the next level. Enrol in the Data Analysis with Data Science &amp; Machine Learning bundle today and unlock the power of data for your business.</p><p><strong>This Data Analysis with Data Science &amp; Machine Learning Bundle Consists of the following Premium courses: </strong></p><ul><li> Course 01: Introduction to Data Analysis</li><li> Course 02: Data Analytics with Tableau</li><li> Course 03: Complete Google Analytics Course</li><li> Course 04: Python for Data Analysis</li><li> Course 05: Quick Data Science Approach from Scratch</li><li> Course 06: R Programming for Data Science</li><li> Course 07: Learn MySQL from Scratch for Data Science and Analytics</li><li> Course 08: Master JavaScript with Data Visualization</li><li> Course 09: Excel Data Analysis</li><li> Course 10: Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li> Course 11: Root Cause Analysis</li><li> Course 12: Google Data Studio: Data Analytics</li><li> Course 13: Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query</li><li> Course 14: Business Intelligence and Data Mining Masterclass</li></ul><p>Learning Outcomes:</p><ul><li>Develop a solid understanding of data analysis techniques.</li><li>Learn how to collect, manage, and manipulate data effectively.</li><li>Gain knowledge in the programming languages commonly used in data analysis, such as Python and R.</li><li>Become proficient in data visualisation and presentation.</li><li>Develop an understanding of machine learning algorithms and their application in data analysis.</li><li>Learn how to use various data analysis software, including Tableau and Google Analytics.</li></ul><p>The Data Analysis with Data Science &amp; Machine Learning bundle includes a variety of courses aimed at providing theoretical knowledge in the field of data analysis. The bundle covers a wide range of topics, starting with the basics of data analysis such as data cleaning, exploration, and visualisation. With courses such as Introduction to Data Analysis, Data Analytics with Tableau, Complete Google Analytics Course, Python for Data Analysis, Quick Data Science Approach from Scratch, R Programming for Data Science, Learn MySQL from Scratch for Data Science and Analytics, and Master JavaScript with Data Visualization, learners can get a comprehensive understanding of data analysis concepts.</p><p>The course material is presented in a theoretical format to help learners gain a deep understanding of the topics. Learners can engage with the material at their own pace. The course content is presented in a way that is easy to understand, making it ideal for those who are new to the field of data analysis.</p><p><strong>Course Curriculum: Data Analysis with Data Science &amp; Machine Learning</strong></p><ul><li>Module 01: Introduction</li><li>Module 02: Agenda and Principles of Process Management</li><li>Module 03: The Voice of the Process</li><li>Module 04: Working as One Team for Improvement</li><li>Module 05: Exercise: The Voice of the Customer</li><li>Module 06: Tools for Data Analysis</li><li>Module 07: The Pareto Chart</li><li>Module 08: The Histogram</li><li>Module 09: The Run Chart</li><li>Module 10: Exercise: Presenting Performance Data</li><li>Module 11: Understanding Variation</li><li>Module 12: The Control Chart</li><li>Module 13: Control Chart Example</li><li>Module 14: Control Chart Special Cases</li><li>Module 15: Interpreting the Control Chart</li><li>Module 16: Control Chart Exercise</li><li>Module 17: Strategies to Deal with Variation</li><li>Module 18: Using Data to Drive Improvement</li><li>Module 19: A Structure for Performance Measurement</li><li>Module 20: Data Analysis Exercise</li><li>Module 21: Course Project</li><li>Module 22: Test your Understanding</li></ul><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Professionals seeking to upskill and enhance their data analysis knowledge and abilities.</li><li>Individuals interested in pursuing a career in data analysis, data science or machine learning.</li><li>Business owners or entrepreneurs looking to gain insights into their data and make data-driven decisions.</li><li>Students or recent graduates seeking to gain valuable experience and knowledge in data analysis.</li><li>Anyone looking to expand their knowledge and understanding of data analysis and its useful applications.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Data Analysis with Data Science &amp; Machine Learning course has been designed to be fully compatible with tablets and smartphones.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £24k-£42k per annum.</li><li>Business Intelligence Analyst: £25k-£60k per annum.</li><li>Data Scientist: £27k-£65k per annum.</li><li>Machine Learning Engineer: £28k-£80k per annum.</li><li>Business Analyst: £24k-£52k per annum.</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £10
        </p>
            <div> <p>You will get the Hard Copy certificate for the <strong>Data Analysis with Data Science &amp; Machine Learning</strong> course absolutely Free! Other Hard Copy certificates are available for £10 each.</p><p><strong>Please Note:</strong> The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

                        </div>

Apex Learning

Data Analysis with Data Science & Machine Learning

<p><b>Artificial neural networks (ANNs)</b><span> are the most powerful machine learning algorithms available today. They are capable of learning complex relationships in data, and they have been used to achieve state-of-the-art results in a wide variety of fields, including image recognition, natural language processing, and speech recognition.</span></p>
<p><span>The Future of Machine Learning is Here!</span></p>
<p><span>This </span><b>Project on Deep Learning - Artificial Neural Network</b><span> course will teach you how to build and train </span><b>ANNs</b><span> from scratch. You will learn about the different components of an ANN, such as the input layer, hidden layers, and output layer. You will also learn about the different </span><b>activation functions</b><span> that can be used in ANNs, and you will see how to optimise ANNs for different tasks.</span></p>
<h3></h3>
<p><span>In addition to the theoretical concepts, you will also get </span><b>experience with ANNs</b><span>. You will work on a project where you will build an ANN to classify images. You will use the </span><b>TensorFlow library</b><span> to build your ANN, and you will see how to train your ANN on a dataset of images.</span></p>
<h3></h3>
<p><span>By the end of this </span><b>Project on Deep Learning - Artificial Neural Network</b><span> course, you will have a deep understanding of ANNs and how to use them. You will be able to build your own ANNs to solve a variety of problems. You will also be able to use the TensorFlow library to build and train ANNs.</span></p>
<h3></h3>
<p><span>So what are you waiting for? Enrol in this course today and start learning about the future of </span><b>machine learning!</b></p>
<h3></h3>
<p> </p>
<p><b>Learning Outcomes:</b></p>
<p><span>Through this comprehensive course, you should be able to:</span></p>
<h3></h3>
<ul>
<li><span>Understand the fundamental concepts of deep learning and artificial neural networks.</span></li>
<li><span>Install and configure an artificial neural network framework.</span></li>
<li><span>Preprocess and structure data for optimal model performance.</span></li>
<li><span>Encode data effectively for neural network training and predictions.</span></li>
<li><span>Build and deploy artificial neural networks for real-world applications.</span></li>
<li><span>Address data imbalance challenges and optimise model accuracy.</span></li>
</ul>
<h3></h3>
<p> </p>
<p><b>Who is this course for?</b></p>
<p><span>This Project on Deep Learning - Artificial Neural Network course is ideal for:</span></p>
<h3></h3>
<ul>
<li><span>Data scientists and machine learning practitioners seeking to expand their knowledge.</span></li>
<li><span>Software engineers interested in leveraging deep learning techniques.</span></li>
<li><span>Students pursuing a career in artificial intelligence and machine learning.</span></li>
<li><span>Professionals looking to enhance their skills in neural network development.</span></li>
<li><span>Individuals with a passion for exploring advanced machine learning techniques.</span></li>
</ul>
<h3></h3>
<p> </p>
<p><b>Career Path </b></p>
<p><span>Our course will prepare you for a range of careers, including:</span></p>
<h3></h3>
<ul>
<li><span>Deep Learning Engineer: £40,000 - £100,000 per year.</span></li>
<li><span>Machine Learning Researcher: £45,000 - £120,000 per year.</span></li>
<li><span>Data Scientist: £50,000 - £110,000 per year.</span></li>
<li><span>Artificial Intelligence Specialist: £55,000 - £130,000 per year.</span></li>
<li><span>Software Engineer (specialising in AI): £45,000 - £100,000 per year.</span></li>
<li><span>Research Scientist (Machine Learning): £50,000 - £120,000 per year.</span></li>
<li><span>AI Consultant: £60,000 - £150,000 per year.</span></li>
</ul>
<h3></h3>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials of the </span><b>Project on Deep Learning - Artificial Neural Network (ANNs)</b><span> there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60.</span></p>
<h3></h3>
<h3><b>Prerequisites</b></h3>
<p><span>This </span><b>Project on Deep Learning - Artificial Neural Network (ANNs)</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Project on Deep Learning - Artificial Neural Network (ANNs)</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<p> </p><div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4"> Section 01: Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction of Project</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 02: ANN Installation</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Setup Environment for ANN</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>ANN Installation</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 03: Data Preprocessing</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Import Libraries and Data Preprocessing</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Preprocessing</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Preprocessing Continue</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 04: Data Encoding</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Exploration</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Encoding</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Encoding Continue</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Preparation of Dataset for Training</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 05: Steps to Build ANN</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Steps to Build ANN Part 1</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Steps to Build ANN Part 2</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Steps to Build ANN Part 3</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Steps to Build ANN Part 4</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 06: Predictions and Imbalance-Learn</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predictions</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Predictions Continue</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Resampling Data with Imbalance-Learn</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Resampling Data with Imbalance-Learn Continue</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Machine Learning (ML)

Azure Machine Learning

AI (Artificial Intelligence)

Project on Deep Learning - Artificial Neural Network (ANNs)

Keras

TensorFlow

You will learn Python-based deep learning and machine learning techniques through this course. With numerous real-world case studies, we will go over all the mathematics needed to master deep learning algorithms. We will study Backpropagation, Feed Forward Network, Artificial Neural Networks, CNN, RNN, Transfer Learning, and more.

Deep Learning with Real-World Projects

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p></p><p>The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don?t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful.</p><p></p><p></p><p>This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. </p><p></p>Prerequisites<p>Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services.   Specifically:  </p><ul> <li>Experience using computers and the internet.</li> <li>Interest in use cases for AI applications and machine learning models. </li> <li>A willingness to learn through hands-on exp...</li><p></p></ul></div><div id="course-pills-2"><h3>1 - Fundamental AI Concepts</h3> <ul> <li>Understand machine learning</li> <li>Understand computer vision</li> <li>Understand natural language processing</li> <li>Understand document intelligence and knowledge mining</li> <li>Understand generative AI</li> <li>Challenges and risks with AI</li> <li>Understand Responsible AI</li> </ul> <h3>2 - Fundamentals of machine learning</h3> <ul> <li>What is machine learning?</li> <li>Types of machine learning</li> <li>Regression</li> <li>Binary classification</li> <li>Multiclass classification</li> <li>Clustering</li> <li>Deep learning</li> <li>Azure Machine Learning</li> </ul> <h3>3 - Fundamentals of Azure AI services</h3> <ul> <li>AI services on the Azure platform</li> <li>Create Azure AI service resources</li> <li>Use Azure AI services</li> <li>Understand authentication for Azure AI services</li> </ul> <h3>4 - Fundamentals of Computer Vision</h3> <ul> <li>Images and image processing</li> <li>Machine learning for computer vision</li> <li>Azure AI Vision</li> </ul> <h3>5 - Fundamentals of Facial Recognition</h3> <ul> <li>Understand Face analysis</li> <li>Get started with Face analysis on Azure</li> </ul> <h3>6 - Fundamentals of optical character recognition</h3> <ul> <li>Get started with Vision Studio on Azure</li> </ul> <h3>7 - Fundamentals of Text Analysis with the Language Service</h3> <ul> <li>Understand Text Analytics</li> <li>Get started with text analysis</li> </ul> <h3>8 - Fundamentals of question answering with the Language Service</h3> <ul> <li>Understand question answering</li> <li>Get started with the Language service and Azure Bot Service</li> </ul> <h3>9 - Fundamentals of conversational language understanding</h3> <ul> <li>Describe conversational language understanding</li> <li>Get started with conversational language understanding in Azure</li> </ul> <h3>10 - Fundamentals of Azure AI Speech</h3> <ul> <li>Understand speech recognition and synthesis</li> <li>Get started with speech on Azure</li> </ul> <h3>11 - Fundamentals of Azure AI Document Intelligence</h3> <ul> <li>Explore capabilities of document intelligence</li> <li>Get started with receipt analysis on Azure</li> </ul> <h3>12 - Fundamentals of Knowledge Mining with Azure Cognitive Search</h3> <ul> <li>What is Azure Cognitive Search?</li> <li>Identify elements of a search solution</li> <li>Use a skillset to define an enrichment pipeline</li> <li>Understand indexes</li> <li>Use an indexer to build an index</li> <li>Persist enriched data in a knowledge store</li> <li>Create an index in the Azure portal</li> <li>Query data in an Azure Cognitive Search index</li> </ul> <h3>13 - Fundamentals of Generative AI</h3> <ul> <li>What is generative AI?</li> <li>Large language models</li> <li>What is Azure OpenAI?</li> <li>What are copilots?</li> <li>Improve generative AI responses with prompt engineering</li> </ul> <h3>14 - Fundamentals of Azure OpenAI Service</h3> <ul> <li>What is generative AI</li> <li>Describe Azure OpenAI</li> <li>How to use Azure OpenAI</li> <li>Understand OpenAI's natural language capabilities</li> <li>Understand OpenAI code generation capabilities</li> <li>Understand OpenAI's image generation capabilities</li> <li>Describe Azure OpenAI's access and responsible AI policies</li> </ul> <h3>15 - Fundamentals of Responsible Generative AI</h3> <ul> <li>Plan a responsible generative AI solution</li> <li>Identify potential harms</li> <li>Measure potential harms</li> <li>Mitigate potential harms</li> <li>Operate a responsible generative AI solution</li> </ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans AI-900T00 - Microsoft Azure AI Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the AI-900T00 - Microsoft Azure AI Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Microsoft AI-900

AI-900T00 Microsoft Azure AI Fundamentals

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

<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
			<h2><b>Course Overview</b></h2>		</div>
				</div>
				<div>
				<div>
					<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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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>

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<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure.</p><p></p><p></p><p>AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage?Azure AI Services,?Azure AI Search, and?Azure OpenAI. The course will use C# or Python as the programming language.</p><p></p>Prerequisites<p>Before attending this course, students must have:  </p><li>Knowledge of Microsoft Azure and ability to navigate the Azure portal  </li><li>Knowledge of either C# or Python  </li><li>Familiarity with JSON and REST programming semantics  <br />  Recommended course prerequisites  </li><li>AI-900T00: Microsoft Azure AI Fundamentals course<p></p></li></div><div id="course-pills-2"><h3>1 - Prepare to develop AI solutions on Azure</h3> <ul> <li>Define artificial intelligence</li> <li>Understand AI-related terms</li> <li>Understand considerations for AI Engineers</li> <li>Understand considerations for responsible AI</li> <li>Understand capabilities of Azure Machine Learning</li> <li>Understand capabilities of Azure AI Services</li> <li>Understand capabilities of the Azure Bot Service</li> <li>Understand capabilities of Azure Cognitive Search</li> </ul> <h3>2 - Create and consume Azure AI services</h3> <ul> <li>Provision an Azure AI services resource</li> <li>Identify endpoints and keys</li> <li>Use a REST API</li> <li>Use an SDK</li> </ul> <h3>3 - 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Create a custom text classification solution</h3> <ul> <li>Understand types of classification projects</li> <li>Understand how to build text classification projects</li> </ul> <h3>15 - Create a custom named entity extraction solution</h3> <ul> <li>Understand custom named entity recognition</li> <li>Label your data</li> <li>Train and evaluate your model</li> </ul> <h3>16 - Translate text with Azure AI Translator service</h3> <ul> <li>Provision an Azure AI Translator resource</li> <li>Specify translation options</li> <li>Define custom translations</li> </ul> <h3>17 - Create speech-enabled apps with Azure AI services</h3> <ul> <li>Provision an Azure resource for speech</li> <li>Use the Azure AI Speech to Text API</li> <li>Use the text to speech API</li> <li>Configure audio format and voices</li> <li>Use Speech Synthesis Markup Language</li> </ul> <h3>18 - Translate speech with the Azure AI Speech service</h3> <ul> <li>Provision an Azure resource for speech translation</li> <li>Translate speech to text</li> <li>Synthesize translations</li> </ul> <h3>19 - Create an Azure AI Search solution</h3> <ul> <li>Manage capacity</li> <li>Understand search components</li> <li>Understand the indexing process</li> <li>Search an index</li> <li>Apply filtering and sorting</li> <li>Enhance the index</li> </ul> <h3>20 - Create a custom skill for Azure AI Search</h3> <ul> <li>Create a custom skill</li> <li>Add a custom skill to a skillset</li> </ul> <h3>21 - Create a knowledge store with Azure AI Search</h3> <ul> <li>Define projections</li> <li>Define a knowledge store</li> </ul> <h3>22 - Plan an Azure AI Document Intelligence solution</h3> <ul> <li>Understand AI Document Intelligence</li> <li>Plan Azure AI Document Intelligence resources</li> <li>Choose a model type</li> </ul> <h3>23 - Use prebuilt Azure AI Document Intelligence models</h3> <ul> <li>Understand prebuilt models</li> <li>Use the General Document, Read, and Layout models</li> <li>Use financial, ID, and tax models</li> </ul> <h3>24 - Extract data from forms with Azure Document Intelligence</h3> <ul> <li>What is Azure Document Intelligence?</li> <li>Get started with Azure Document Intelligence</li> <li>Train custom models</li> <li>Use Azure Document Intelligence models</li> <li>Use the Azure Document Intelligence Studio</li> </ul> <h3>25 - Get started with Azure OpenAI Service</h3> <ul> <li>Access Azure OpenAI Service</li> <li>Use Azure OpenAI Studio</li> <li>Explore types of generative AI models</li> <li>Deploy generative AI models</li> <li>Use prompts to get completions from models</li> <li>Test models in Azure OpenAI Studio's playgrounds</li> </ul> <h3>26 - Build natural language solutions with Azure OpenAI Service</h3> <ul> <li>Integrate Azure OpenAI into your app</li> <li>Use Azure OpenAI REST API</li> <li>Use Azure OpenAI SDK</li> </ul> <h3>27 - Apply prompt engineering with Azure OpenAI Service</h3> <ul> <li>Understand prompt engineering</li> <li>Write more effective prompts</li> <li>Provide context to improve accuracy</li> </ul> <h3>28 - Generate code with Azure OpenAI Service</h3> <ul> <li>Construct code from natural language</li> <li>Complete code and assist the development process</li> <li>Fix bugs and improve your code</li> </ul> <h3>29 - Generate images with Azure OpenAI Service</h3> <ul> <li>What is DALL-E?</li> <li>Explore DALL-E in Azure OpenAI Studio</li> <li>Use the Azure OpenAI REST API to consume DALL-E models</li> </ul> <h3>30 - Use your own data with Azure OpenAI Service</h3> <ul> <li>Understand how to use your own data</li> <li>Add your own data source</li> <li>Chat with your model using your own data</li> </ul> <h3>31 - Fundamentals of Responsible Generative AI</h3> <ul> <li>Plan a responsible generative AI solution</li> <li>Identify potential harms</li> <li>Measure potential harms</li> <li>Mitigate potential harms</li> <li>Operate a responsible generative AI solution</li> </ul></div>

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