Name: Getting Started with Cloud Computing - Level 1
Price: 95.99 GBP
Availability: InStock

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is intended for the following participants:Cloud professionals interested in taking the Data Engineer certification exam.Data engineering professionals interested in taking the Data Engineer certification exam.</p>Overview<p>This course teaches participants the following skills:<br />Position the Professional Data Engineer Certification<br />Provide information, tips, and advice on taking the exam<br />Review the sample case studies<br />Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate<br />Connect candidates to appropriate target learning</p><p>This course will help prospective candidates plan their preparation for the Professional Data Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, quizzes, and discussions, candidates will familiarize themselves with the domain covered by the examination, to help them devise a preparation strategy. Rehearse useful skills including exam question reasoning and case comprehension. Tips and review of topics from the Data Engineering curriculum.</p></div><div id="course-pills-2"><h4>Understanding the Professional Data Engineer Certification</h4><ul><li>Position the Professional Data Engineer certification among the offerings</li><li>Distinguish between Associate and Professional</li><li>Provide guidance between Professional Data Engineer and Associate Cloud Engineer</li><li>Describe how the exam is administered and the exam rules</li><li>Provide general advice about taking the exam</li></ul><h4>Sample Case Studies for the Professional Data Engineer Exam</h4><ul><li>Flowlogistic</li><li>MJTelco</li><li></li></ul><h4>Designing and Building (Review and preparation tips)</h4><ul><li>Designing data processing systems</li><li>Designing flexible data representations</li><li>Designing data pipelines</li><li>Designing data processing infrastructure</li><li>Build and maintain data structures and databases</li><li>Building and maintaining flexible data representations</li><li>Building and maintaining pipelines</li><li>Building and maintaining processing infrastructure</li><li></li></ul><h4>Analyzing and Modeling (Review and preparation tips)</h4><ul><li>Analyze data and enable machine learning</li><li>Analyzing data</li><li>Machine learning</li><li>Machine learning model deployment</li><li>Model business processes for analysis and optimization</li><li>Mapping business requirements to data representations</li><li>Optimizing data representations, data infrastructure performance and cost</li><li></li></ul><h4>Reliability, Policy, and Security (Review and preparation tips)</h4><ul><li>Design for reliability</li><li>Performing quality control</li><li>Assessing, troubleshooting, and improving data representation and data processing infrastructure</li><li>Recovering data</li><li>Visualize data and advocate policy</li><li>Building (or selecting) data visualization and reporting tools</li><li>Advocating policies and publishing data and reports</li><li>Design for security and compliance</li><li>Designing secure data infrastructure and processes</li><li>Designing for legal compliance</li><li></li></ul><h4>Resources and next steps</h4><ul><li>Resources for learning more about designing data processing systems, data structures, and databases</li><li>Resources for learning more about data analysis, machine learning, business process analysis, and optimization</li><li>Resources for learning more about data visualization and policy Resources for learning more about reliability design</li><li>Resources for learning more about business process analysis and optimization</li><li>Resources for learning more about reliability, policies, security, and compliance     </li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Preparing for the Professional Data Engineer Examination 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 Preparing for the Professional Data Engineer Examination 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

Data Engineering

Japanese Information Technology Engineers Examination

AWS Certified Developer

INCOSE Systems Engineering Professional Certification

AWS Certified Cloud Practitioner

AWS Certified Solutions Architect

Preparing for the Professional Data Engineer Examination

<p><strong>Title: Mastering the Future: Digital Electrical Circuits and Intelligence Electrical Devices Course</strong></p><p> </p><p><strong>Overview:</strong></p><p>Welcome to a transformative journey into the cutting-edge realm of 'Digital Electrical Circuits and Intelligence Electrical Devices.' In today's rapidly evolving technological landscape, the demand for skilled professionals who can navigate the complexities of digital circuits and intelligent devices is greater than ever. This comprehensive course is designed to equip you with the knowledge and skills needed to not only understand but also master the intricacies of digital electrical circuits and harness the power of intelligent electrical devices.</p><p> </p><p><strong>Key Features:</strong></p><ol><li><strong>Comprehensive Curriculum:</strong> Our course covers a wide spectrum of topics, from the fundamentals of digital electrical circuits to the advanced principles behind intelligent electrical devices. You'll delve into topics such as digital signal processing, microcontrollers, embedded systems, and more.</li><li><strong>Hands-On Learning:</strong> Theory comes to life through hands-on projects and practical exercises. You'll have the opportunity to apply your knowledge in real-world scenarios, ensuring that you not only understand the concepts but can also implement them effectively.</li><li><strong>Cutting-Edge Technologies:</strong> Stay ahead of the curve by learning about the latest advancements in digital circuits and intelligent devices. Explore the world of artificial intelligence, machine learning, and the Internet of Things (IoT) to understand how they intersect with electrical engineering.</li><li><strong>Expert Instruction:</strong> Benefit from the guidance of experienced instructors who bring a wealth of industry knowledge to the classroom. Our instructors are dedicated to your success, providing support and insights throughout the course.</li><li><strong>Industry-Relevant Projects:</strong> Gain practical experience by working on projects that mirror real-world challenges faced by professionals in the field. Build a portfolio that showcases your abilities and sets you apart in a competitive job market.</li><li><strong>Networking Opportunities:</strong> Connect with like-minded individuals and industry experts through networking events, forums, and collaborative projects. Expand your professional network and open doors to new opportunities.</li><li><strong>Flexible Learning:</strong> Designed to accommodate various schedules, our course offers flexibility through online lectures and resources. Access course materials at your own pace, allowing you to balance your learning with other commitments.</li><li><strong>Certification:</strong> Upon successful completion of the course, receive a recognized certification that validates your expertise in digital electrical circuits and intelligent electrical devices, enhancing your credibility in the job market.</li></ol><p> </p><p>Whether you're a seasoned professional looking to upskill or a student aspiring to enter the field of electrical engineering, our 'Digital Electrical Circuits and Intelligence Electrical Devices' course provides the knowledge and practical experience needed to thrive in the dynamic world of technology. Enroll now to embark on a journey towards mastering the future of electrical engineering!</p><div><div><div><h3>
Course Curriculum</h3><div><div><h4>
Introduction To Digital Electric Circuits</h4><div><div><ul><li><div>
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Introduction To Digital Electric Circuits</h5></div><div>
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Numbering Systems</h4><div><div><ul><li><div>
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Numbering Systems</h5></div><div>
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Binary Arithmetic</h4><div><div><ul><li><div>
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Binary Arithmetic</h5></div><div>
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Logic Gates</h4><div><div><ul><li><div>
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Logic Gates</h5></div><div>
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Flip-Flops</h4><div><div><ul><li><div>
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Counters Shift Registers</h4><div><div><ul><li><div>
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Adders</h4><div><div><ul><li><div>
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Adders</h5></div><div>
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Compete High

Digital Electrical Circuits and Intelligence Electrical Devices

General Admission

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							<p><span>By enroling in Big Data Analytics with PySpark Power BI and MongoDB, you can kickstart your vibrant career and strengthen your profound knowledge. You can learn everything you need to know about the topic.</span></p><p><span>The Big Data Analytics with PySpark Power BI and MongoDB course includes all of the most recent information to keep you abreast of the employment market and prepare you for your future. The curriculum for this excellent Big Data Analytics with PySpark Power BI and MongoDB course includes modules at all skill levels, from beginner to expert. You will have the productivity necessary to succeed in your organisation once you have completed our Big Data Analytics with PySpark Power BI and MongoDB Program.</span></p><p><span>So enrol in our Big Data Analytics with PySpark Power BI and MongoDB course right away if you're keen to envision yourself in a rewarding career.</span></p>						</div>
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							<p><span>Enroling in this Big Data Analytics with PySpark Power BI and MongoDB course can improve your Big Data Analytics with PySpark Power BI and MongoDB perspective, regardless of your skill levels in the Big Data Analytics with PySpark Power BI and MongoDB topics you want to master. If you're already a Big Data Analytics with PySpark Power BI and MongoDB expert, this peek under the hood will provide you with suggestions for accelerating your learning, including advanced Big Data Analytics with PySpark Power BI and MongoDB insights that will help you make the most of your time. This Big Data Analytics with PySpark Power BI and MongoDB course will act as a guide for you if you've ever wished to excel at Big Data Analytics with PySpark Power BI and MongoDB.</span></p>						</div>
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										<span>Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.</span>
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							<p>Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £9.99.</p>						</div>
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							<p><span>This Big Data Analytics with PySpark Power BI and MongoDB course is a great place to start if you're looking to start a new career in Big Data Analytics with PySpark Power BI and MongoDB field. This training is for anyone interested in gaining in-demand Big Data Analytics with PySpark Power BI and MongoDB proficiency to help launch a career or their business aptitude. </span></p>						</div>
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							<p><span>The Big Data Analytics with PySpark Power BI and MongoDB course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of Big Data Analytics with PySpark Power BI and MongoDB, without any fuss.</span></p>						</div>
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							<p><span>This Big Data Analytics with PySpark Power BI and MongoDB training will assist you develop your Big Data Analytics with PySpark Power BI and MongoDB ability, establish a personal brand, and present a portfolio of relevant talents. It will help you articulate a  Big Data Analytics with PySpark Power BI and MongoDB professional story and personalise your path to a new career. Furthermore, developing this Big Data Analytics with PySpark Power BI and MongoDB skillset can lead to numerous opportunities for high-paying jobs in a variety of fields.</span></p>						</div>
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			<h4>Order Your Certificate</h4>		</div>
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							<div><div><div><div id="1693981461.590199"><div><div><div><div><div><div><div><div><div><div><div>To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:</div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>						</div>
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	<span>Course Curriculum</span>
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					<td colspan="4">Section 01: Introduction</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td colspan="4">Section 02: Setup And Installations</td>
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					<td><span><i></i> 00:03:00</span></td>
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					<td>Testing Apache Spark Installation</td>
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					<td><span><i></i> 00:06:00</span></td>
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					<td>Installing NoSQL Booster for MongoDB</td>
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					<td colspan="4">Section 03: Data Processing With PySpark And MongoDB</td>
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					<td>Integrating PySpark with Jupyter Notebook</td>
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					<td><span><i></i> 00:19:00</span></td>
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					<td colspan="4">Section 04: Machine Learning With PySpark And MLlib</td>
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					<td colspan="4">Section 05: Creating The Data Pipeline Scripts</td>
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					<td><span><i></i> 00:06:00</span></td>
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NextGen Learning

Big Data Analytics with PySpark Power BI and MongoDB Course

<strong>Learn to use cutting-edge language models ChatGPT, Dalle-2, and Midjourney to create high-quality written content and generative art in this course. Discover how to fine-tune these models for specific tasks and explore the ethical implications and future-proofing strategies for using AI in your work.</strong>
        <p>Learn to unleash your creativity and automate tasks using cutting-edge language models such as ChatGPT, Dalle-2, and Midjourney. This course introduces the basic concepts of these models, teaching you how to generate text, images, and other media types.

Through hands-on projects, including writing a blog post, creating a code snippet, and generating artwork, you will learn to fine-tune these models for specific tasks and use them for creative writing. Additionally, you will explore generative art, including image and video creation.

By the course's end, you will have a deep understanding of ChatGPT, Dalle-2, and Midjourney, enabling you to create high-quality content and generative artwork. You will also learn the ethical implications of AI and how to future-proof your work using generative AI.

This course is ideal for entrepreneurs looking to improve their work efficiency and content creators seeking to increase their output. Gain valuable skills that will help you continue learning and experimenting with these powerful tools.</p>
        <h2>What You Will Learn</h2><p>Generate high-quality written content with ChatGPT<br>Create new artwork using Dalle-2 and Midjourney<br>Fine-tune models for specific tasks and creative writing<br>Understand the ethical implications of AI<br>Future-proof your work using Generative AI<br>Develop skills for efficiency and increased content output</p>
        <h2>Audience</h2><p>This course is designed for entrepreneurs who want to streamline their work processes and increase productivity, as well as content creators who want to expand their creative output.<br><br>No programming experience is required, making this course accessible to anyone with a basic understanding of computer usage. By the end of the course, you will have a good understanding of how to use ChatGPT, Dalle-2, and Midjourney to generate text, images, and other media.</p>
        <h2>Approach</h2><p>The course takes a hands-on approach to learning, with a focus on practical applications. You will learn by doing projects that include writing a blog post, creating a code snippet, and generating a piece of artwork. The course is designed for beginners and assumes no prior programming experience.</p>
        <h2>Key Features</h2><p>Learn to use cutting-edge language models (ChatGPT, Dalle-2, Midjourney) to generate text and media * Hands-on projects include writing, coding, and creating generative artwork * Gain understanding of ethical implications and future-proofing work using Generative AI</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/How-to-Use-ChatGPT-and-Generative-AI-to-Help-Create-Content</p>
        <h2>About the Author</h2><h3>Delta X Technology LLC</h3><p>Delta X Technology LLC is owned by Justin B, a highly skilled software engineer and AI creator with five years of industry experience. With a strong background in both theoretical and practical aspects of software engineering, Justin has worked on several projects involving machine learning. As the owner of Delta X Technology LLC, he is dedicated to sharing his expertise with students and helping them develop the skills needed to succeed in the field of AI. With a passion for teaching and a wealth of real-world experience, Justin offers students a unique perspective and valuable insights into the latest trends and best practices in AI development.</p>
        <h2>Course Outline</h2><h4>1. Introduction to Generative AI</h4><p>In this section, you will learn the fundamentals of Generative AI, including how it works and its various applications in different fields.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is Generative AI</p><p style='margin: 0'>This video provides an introduction to the concept of Generative AI and explains what it is.</p></td></tr><tr><td style='padding: 15px;'><p>2. Fundaments of Using Generative AI</p><p style='margin: 0'>In this video, you will learn about the basic principles of using Generative AI, including how to choose the right model for your needs and some common use cases.</p></td></tr><tr><td style='padding: 15px;'><p>3. Generating Images with DALLE-2</p><p style='margin: 0'>This video will teach you how to use DALLE-2, a powerful generative AI model, to create high-quality images from textual descriptions.</p></td></tr><tr><td style='padding: 15px;'><p>4. Generating Images with Midjourney</p><p style='margin: 0'>In this video, you will learn how to use Midjourney, another generative AI model, to create unique and creative images.</p></td></tr><tr><td style='padding: 15px;'><p>5. Generating Images with Stable Diffusion</p><p style='margin: 0'>This video will teach you how to use the Stable Diffusion model to generate high-quality images using a diffusion process.</p></td></tr><tr><td style='padding: 15px;'><p>6. Generating Text Content with ChatGPT</p><p style='margin: 0'>In this video, you will learn how to use ChatGPT, a state-of-the-art language model, to generate high-quality text content, including articles, blog posts, and more.</p></td></tr><tr><td style='padding: 15px;'><p>7. Generating Code with ChatGPT</p><p style='margin: 0'>This video will teach you how to use ChatGPT to generate code snippets and automate programming tasks.</p></td></tr><tr><td style='padding: 15px;'><p>8. Dos and Don'ts of Using Generative AI</p><p style='margin: 0'>In this video, you will learn about the ethical implications of generative AI, as well as best practices for using these models in a responsible and effective way.</p></td></tr></tbody></table>

Packt

Blogging

Generative AI

Web Development

Midjourney

Automation

ChatGPT

Learn to use cutting-edge language models ChatGPT, Dalle-2, and Midjourney to create high-quality written content and generative art in this course. Discover how to fine-tune these models for specific tasks and explore the ethical implications and future-proofing strategies for using AI in your work.

How to Use ChatGPT and Generative AI to Help Create Content

Data Structure

Python

Programming

Algorithms

Learn to program with Python 3, visualize algorithms and data structures, and implement them in Python projects

Python 3: Project-based Python, Algorithms, Data Structures

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business.</p>Overview<p>In this course, you will learn to:<br />Use data science principles to address business issues.<br />Apply the extract, transform, and load (ETL) process to prepare datasets.<br />Use multiple techniques to analyze data and extract valuable insights.<br />Design a machine learning approach to address business issues.<br />Train, tune, and evaluate classification models.<br />Train, tune, and evaluate regression and forecasting models.<br />Train, tune, and evaluate clustering models.<br />Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.</p><p>For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice<br /></p></div><div id="course-pills-2"><h4>Addressing Business Issues with Data Science</h4><ul><li>Topic A: Initiate a Data Science Project</li><li>Topic B: Formulate a Data Science Problem</li></ul><h4>Extracting, Transforming, and Loading Data</h4><ul><li>Topic A: Extract Data</li><li>Topic B: Transform Data</li><li>Topic C: Load Data</li></ul><h4>Analyzing Data</h4><ul><li>Topic A: Examine Data</li><li>Topic B: Explore the Underlying Distribution of Data</li><li>Topic C: Use Visualizations to Analyze Data</li><li>Topic D: Preprocess Data</li></ul><h4>Designing a Machine Learning Approach</h4><ul><li>Topic A: Identify Machine Learning Concepts</li><li>Topic B: Test a Hypothesis</li></ul><h4>Developing Classification Models</h4><ul><li>Topic A: Train and Tune Classification Models</li><li>Topic B: Evaluate Classification Models</li></ul><h4> Developing Regression Models</h4><ul><li>Topic A: Train and Tune Regression Models</li><li>Topic B: Evaluate Regression Models</li></ul><h4>Developing Clustering Models</h4><ul><li>Topic A: Train and Tune Clustering Models</li><li>Topic B: Evaluate Clustering Models</li></ul><h4>Finalizing a Data Science Project</h4><ul><li>Topic A: Communicate Results to Stakeholders</li><li>Topic B: Demonstrate Models in a Web App</li><li>Topic C: Implement and Test Production Pipelines</li></ul></div>

Service Design Courses (SDC)

Data Science

Data Skills

Certified in Data Protection (CDP)

CertNexus Certified Data Science Practitioner (CDSP)

<strong>Learn Python Programming using a Step By Step Approach with 200+ code examples.</strong>
        <p>Python is one of the most popular programming languages. Python offers both object-oriented and structural programming features. We love Programming. Our aim with this course is to create a love for Programming. 
In more than 150 Steps, we explore the most important Python Programming Language Features
??Basics of Python Programming - Expressions, Variables, and Printing Output
??Python Operators - Python Assignment Operator, Relational and Logical Operators, Short Circuit Operators
??Python Conditionals and If Statement
??Methods - Parameters, Arguments and Return Values
??An Overview Of Python Platform
??Object-Oriented Programming - Class, Object, State and Behavior
??Basics of OOPS - Encapsulation, Inheritance and Abstract Class.
??Basics about Python Data Types
??Basics about Python Built-in Modules
??Conditionals with Python - If Else Statement, Nested If Else
??Loops - For Loop, While Loop in Python, Break and Continue
??Immutablity of Python Basic Types
??Python Data Structures - List, Set, Dictionary and Tuples
??Introduction to Variable Arguments
??Basics of Designing a Class - Class, Object, State and Behavior. Deciding State and Constructors.
??Introduction to Exception Handling - Your Thought Process during Exception Handling. try, except, else and finally. Exception Hierarchy. Throwing an Exception. Creating and Throwing a Custom Exception.
All the code and supporting files for this course are available at: https://github.com/PacktPublishing/Python-Programming-for-Beginners---Learn-in-100-Easy-Steps
</p>
        <h2>What You Will Learn</h2><p>You will learn Python the modern way - Step By Step - with 200 hands-on Code Examples<br>You will understand the best practices in writing high-quality Pythonic Code<br>You will solve a Wide Range of Hands-on Programming exercises with Python<br>You will learn to Write awesome Object-Oriented Programs with Python<br>You will acquire all the Python Skills needed to transition into Analytics, Machine Learning and Data Science Roles<br>You will Acquire all the SKILLS to demonstrate an EXPERTISE with Python Programming in Your Job Interviews<br>You will learn about a wide variety of Python Data Structures - List, Set, Dictionary and Tuples<br>You will learn the basics of PyCharm IDE and Python Shell<br>You will learn how to think as a Python Programmer<br>You will learn the basics of programming - variables, choosing a data type, conditional execution, loops, writing great methods, breaking down problems into subproblems and implementing exception handling.<br>You will learn the basics of Object Oriented Programming - Inheritance, Abstract Class and Constructors<br>You will learn the important concepts of Object Oriented Programming - Abstraction and Inheritance</p>
        <h2>Audience</h2><p>This course is for anyone who wants to learn Programming with Python. You want to automate things with Python.</p>
        <h2>Approach</h2><p>We take a hands-on approach using a combination of Python Shell and PyCharm as an IDE to illustrate more than 150 Python Coding Exercises, Puzzles and Code Examples.</p>
        <h2>Key Features</h2><p>The course is amazing, the instructor has good knowledge in the field  * This course can be viewed with zero Python Programming Experience</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/python-programming-for-beginners---learn-in-100-easy-steps</p>
        <h2>About the Author</h2><h3>In28Minutes Official</h3><p>	Ranga Karanam is a seasoned technologist and architect with over two decades of expertise in programming, design, and architecture. He founded in28Minutes with the goal of helping students master cutting-edge cloud-native technologies such as AWS, Azure, Google Cloud, Docker, and Kubernetes. With 15 years of experience in Java programming and design, Ranga has collaborated with top banking clients worldwide.

His passion for creating hands-on courses with real-world projects motivated him to develop the Step-By-Step series of courses that follow a problem-solution-based approach, with practical and real-world application examples.

Ranga and his team at in28Minutes specialize in Java and related frameworks such as Spring, Spring Boot, Spring MVC, Struts, and Hibernate. They are committed to equipping learners with the skills and knowledge necessary to succeed in today's rapidly evolving technology landscape.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Introduction to Python Programming With Multiplication Table</h4><p></p><br><h4>3. Introduction to Methods - Multiplication Table</h4><p></p><br><h4>4. Introduction to Python Platform</h4><p></p><br><h4>5. Introduction to PyCharm</h4><p></p><br><h4>6. Basic Numeric Data Types and Conditional Execution</h4><p></p><br><h4>7. Text in Python</h4><p></p><br><h4>8. Python Loops</h4><p></p><br><h4>9. Python Tips for Beginners</h4><p></p><br><h4>10. Introduction to Object Oriented Programming</h4><p></p><br><h4>11. Python Data Structures</h4><p></p><br><h4>12. Object Oriented Programming Again</h4><p></p><br><h4>13. Error Handling with Python</h4><p></p><br><h4>14. A Few More Python Tips</h4><p></p><br><h4>15. Thank You and Congratulations</h4><p></p>

Learn Python Programming using a Step By Step Approach with 200+ code examples.

Python Programming for Beginners - Learn in 100 Easy Steps

<strong>A carefully structured advanced-level course on Apache Spark 3 to help you clear your job interviews. This course covers advanced topics and concepts that are part of the Databricks Spark certification exam. Boost your skills in Spark 3 architecture and memory management.</strong>
        <p>Apache Spark is a lightning-fast unified analytics engine for big data and machine learning. Since its release, Apache Spark has seen rapid adoption by enterprises across a wide range of industries. Internet powerhouses such as Netflix, Yahoo, and eBay have deployed Spark at a massive scale. It has quickly become the largest open-source community in big data. So, mastering Apache Spark opens a wide range of professional opportunities.

This course covers some advanced topics and concepts such as Spark 3 architecture and memory management, AQE, DPP, broadcast, accumulators, and multithreading in Spark 3 along with common job interview questions and answers. The objective of this course is to prepare you for advanced certification topics.

By the end of this course, you will have learned some advanced topics and concepts that are asked for in the Databricks Spark Certification or Spark job interviews. This will not only help you develop advanced skills in Apache Spark but also crack your job interviews.</p>
        <h2>What You Will Learn</h2><p>Learn AQE, DPP, Broadcast in Spark 3<br>Explore Spark 3 architecture<br>Understand memory management in Spark 3<br>Learn Spark AQE Dynamic Join Optimization<br>Explore accumulators and multithreading in Spark<br>Discover Spark Dynamic Partition Pruning</p>
        <h2>Audience</h2><p>This course is for anyone who wants to learn and develop advanced skills in Apache Spark or those who are preparing for job interviews and want to learn advanced skills.<br><br>Before proceeding with the course, you will need basic knowledge of Spark programming in Python - PySpark.</p>
        <h2>Approach</h2><p>This is a short and crisp yet comprehensive course on cracking your job interviews on Spark programming and around the topic of Apache Spark 3 architecture and memory management. Leverage your knowledge with some tips/tricks profoundly used in the corporate environment and reinforce your learning with four sets of quizzes.</p>
        <h2>Key Features</h2><p>Practice common job interview questions and answers * Deep dive into Spark 3 architecture and memory management * Prepare for Databricks Spark certification in a structured way</p>
        
        <h2>About the Author</h2><h3>ScholarNest </h3><p>ScholarNest is a small team of people passionate about helping others learn and grow in their careers by bridging the gap between their existing and required skills.

Together, they have over 40+ years of experience in IT as a developer, architect, consultant, trainer, and mentor. They have worked with international software services organizations on various data-centric and Big Data projects.

It is a team of firm believers in lifelong continuous learning and skill development. To popularize the importance of continuous learning, they started publishing free training videos on their YouTube channel. They conceptualized the notion of continuous learning, creating a journal of our learning under the Learning Journal banner.</p>
        <h2>Course Outline</h2><h4>1. Before You Begin</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Introduction</p><p style='margin: 0'>In this video, the author gives an overview of the course. He talks about the objective of the course and the prerequisites needed to continue with the course.</p></td></tr></tbody></table><br><h4>2. Spark Architecture</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Spark Cluster and Runtime Architecture</p><p style='margin: 0'>This video shows the concept of Spark cluster and runtime architecture.</p></td></tr><tr><td style='padding: 15px;'><p>2. Spark Submit and Important Options</p><p style='margin: 0'>In this video, the author describes Spark submit and important options.</p></td></tr><tr><td style='padding: 15px;'><p>3. Deploy Modes - Client and Cluster Mode</p><p style='margin: 0'>In this video, the author describes types of deploy modes in Spark.</p></td></tr><tr><td style='padding: 15px;'><p>4. Spark Jobs - Stage, Shuffle, Task, Slots</p><p style='margin: 0'>In this video, the author gives a detailed overview of the types of Spark jobs.</p></td></tr><tr><td style='padding: 15px;'><p>5. Spark SQL Engine and Query Planning</p><p style='margin: 0'>In this video, the author describes SQL engine and query planning in Spark.</p></td></tr><tr><td style='padding: 15px;'><p>6. Let's Practice - Quiz 1 Solution Video</p><p style='margin: 0'>In this video, the author discusses some frequently asked questions and answers in certification exams.</p></td></tr><tr><td style='padding: 15px;'><p>7. Let's Practice - Quiz 2 Solution Video</p><p style='margin: 0'>In this video, the author discusses more questions and answers that are commonly asked in Spark certification and other related exams.</p></td></tr></tbody></table><br><h4>3. Performance and Applied Understanding</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Spark Memory Allocation</p><p style='margin: 0'>In this video, the author describes the concept of Spark memory allocation in detail.</p></td></tr><tr><td style='padding: 15px;'><p>2. Spark Memory Management</p><p style='margin: 0'>In this video, the author shows how memory management works in Spark.</p></td></tr><tr><td style='padding: 15px;'><p>3. Spark Adaptive Query Execution</p><p style='margin: 0'>In this video, the author describes the details of Spark adaptive query execution and its capabilities and features.</p></td></tr><tr><td style='padding: 15px;'><p>4. Spark AQE Dynamic Join Optimization</p><p style='margin: 0'>In this video, the author describes AQE Dynamic Join Optimization and its importance.</p></td></tr><tr><td style='padding: 15px;'><p>5. Handling Data Skew in Spark Joins</p><p style='margin: 0'>In this video, the author discusses the concept of data skew in Spark joins and why it is used.</p></td></tr><tr><td style='padding: 15px;'><p>6. Spark Dynamic Partition Pruning</p><p style='margin: 0'>In this video, the author discusses dynamic partition pruning and its importance.</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Caching in Spark</p><p style='margin: 0'>In this video, the author gives a detailed discussion of data caching and its importance.</p></td></tr><tr><td style='padding: 15px;'><p>8. Repartition and Coalesce</p><p style='margin: 0'>In this video, the author talks about repartition and coalesce in Spark.</p></td></tr><tr><td style='padding: 15px;'><p>9. Dataframe Hints</p><p style='margin: 0'>In this video, the author gives an overview of dataframe hints in Apache Spark.</p></td></tr><tr><td style='padding: 15px;'><p>10. Broadcast Variables</p><p style='margin: 0'>In this video, the author discusses in detail about Spark broadcast variables.</p></td></tr><tr><td style='padding: 15px;'><p>11. Accumulators</p><p style='margin: 0'>In this video, the author covers accumulators and its features.</p></td></tr><tr><td style='padding: 15px;'><p>12. Speculative Execution</p><p style='margin: 0'>In this video, the author gives a detailed description of Spark speculative execution.</p></td></tr><tr><td style='padding: 15px;'><p>13. Dynamic Resource Allocation</p><p style='margin: 0'>In this video, the author gives a detailed description of resource allocation and its types.</p></td></tr><tr><td style='padding: 15px;'><p>14. Spark Schedulers</p><p style='margin: 0'>In this video, the author describes Spark schedulers within an application.</p></td></tr><tr><td style='padding: 15px;'><p>15. Let's Practice - Quiz 3 Solution Video</p><p style='margin: 0'>In this video, the author discusses a few questions and answers that frequently appear in certification exams.</p></td></tr><tr><td style='padding: 15px;'><p>16. Let's Practice - Quiz 4 Solution Video</p><p style='margin: 0'>In this video, the author discusses a few questions and answers that frequently appear in certification exams.</p></td></tr></tbody></table><br><h4>4. Open-Ended Topics</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Help Me Add More Content - Demand for More</p><p style='margin: 0'>In this video, the author talks about the course and how to continue it further.</p></td></tr></tbody></table>

Scala

Apache Spark

Big Data

A carefully structured advanced-level course on Apache Spark 3 to help you clear your job interviews. This course covers advanced topics and concepts that are part of the Databricks Spark certification exam. Boost your skills in Spark 3 architecture and memory management.

Apache Spark 3 Advance Skills for Cracking Job Interviews

<p>Embark on a transformative journey into Python programming, ranging from fundamentals to advanced concepts like NumPy and Pandas. This course not only equips you with a comprehensive understanding of Python but also guides you through practical applications such as scripting and library usage. With the demand for Python expertise soaring in the UK, where Python Developers earn an average of £45,000 per year, this course opens doors to a realm of opportunities. Join us and immerse yourself in the dynamic world of Python, unlocking a pathway to a lucrative career in programming.</p><p><strong>________________________________________________________________________</strong></p><p><strong>Key Features:</strong></p><ul><li>CPD Certified <em>Diploma in </em>Python Programming: Beginner To Expert With Complete Career Guide</li><li>10 Instant e-certificate and hard copy dispatch by next working day</li><li>Fully online, interactive course with audio voiceover</li><li>Developed by qualified professionals in the field</li><li>Self-paced learning and laptop, tablet, smartphone-friendly</li><li>24/7 Learning Assistance</li><li>Discounts on bulk purchases</li></ul><p><strong>Course Curriculum:</strong></p><ul><li><strong>Module 01:</strong> Introduction to Python Programming from A-Z</li><li><strong>Module 02:</strong> Getting Familiar with Python</li><li><strong>Module 03:</strong> Basic Data Types</li><li><strong>Module 04:</strong> Python Operators</li><li><strong>Module 05:</strong> Advanced Data Types</li><li><strong>Module 06:</strong> Control Flow Part 1</li><li><strong>Module 07:</strong> Control Flow Part 2</li><li><strong>Module 08:</strong> Python Functions</li><li><strong>Module 09:</strong> User Input and Error Handling</li><li><strong>Module 10:</strong> Python Advanced Functions</li><li><strong>Module 11:</strong> Python Scripting and Libraries</li><li><strong>Module 12:</strong> NumPy</li><li><strong>Module 13:</strong> Pandas</li><li><strong>Module 14:</strong> Introduction to OOP</li><li><strong>Module 15:</strong> Advanced OOP</li><li><strong>Module 16:</strong> Starting a Career in Python</li></ul><p><strong>________________________________________________________________________</strong></p><p>Complete Career Guide for <strong>Python Programming: Beginner To Expert</strong><strong>(A to Z)</strong></p><p>This isn't just a course; it's your ticket to thriving in the sector and your roadmap to the <strong>Programming</strong>. In addition to mastering the essentials of <strong>Programming</strong>, you'll also gain valuable skills in CV writing, job searching, communication, leadership, and project management. These 9 complementary courses are designed to empower you at every stage of your journey.</p><p>Stand out in your career, from crafting a winning CV to excelling in interviews. Develop the leadership skills to inspire your team and efficiently manage projects. This holistic approach ensures you're not just job-ready but career-ready. Enrol today, and let's build your success story together in <strong>Programming</strong>. Your dream career starts here!</p><p>List of career guide courses included in <strong>Python Programming: Beginner To Expert With Complete Career Guide:</strong></p><ul><li><strong>Course 01:</strong> Professional CV Writing and Job Searching</li><li><strong>Course 02:</strong> Communication Skills Training</li><li><strong>Course 03:</strong> Career Development Training</li><li><strong>Course 04:</strong> Time Management</li><li><strong>Course 05:</strong> Returning to Work Training</li><li><strong>Course 06:</strong> Level 3 Diploma in Project Management</li><li><strong>Course 07:</strong> Leadership Skills</li><li><strong>Course 08:</strong> Body Language</li><li><strong>Course 09:</strong> Interview and Recruitment</li></ul><p><strong>________________________________________________________________________</strong></p><p><strong>Learning Outcomes:</strong></p><ul><li>Understand Python programming fundamentals comprehensively.</li><li>Apply Python operators effectively in program development.</li><li>Demonstrate proficiency in handling basic and advanced data types.</li><li>Implement control flow structures for efficient program execution.</li><li>Develop and utilize Python functions, including advanced concepts.</li><li>Gain practical skills in scripting, using libraries, and mastering OOP.</li></ul><p><strong>________________________________________________________________________</strong></p><p><strong>Accreditation</strong></p><p>All of our courses, including the <strong>Python Programming: Beginner To Expert With Complete Career Guide</strong>, are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.</p><p><strong>Certification</strong></p><p>Once you've successfully completed your <strong>Python Programming: Beginner To Expert With Complete Career Guide</strong>, you will immediately be sent your digital certificates. Also, you can have your printed certificate delivered by post (shipping cost £3.99). Our certifications have no expiry dates, although we recommend renewing them every 12 months.</p><p><strong>Assessment</strong></p><p>At the end of the courses, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven't, there's no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        100 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>Individuals new to programming seeking a thorough Python foundation.</li><li>Students or professionals aiming to enhance data handling skills.</li><li>Aspiring developers interested in scripting and library usage.</li><li>Those pursuing a career in Python and object-oriented programming.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Python Developer - £35K to 55K/year.</li><li>Data Analyst - £30K to 45K/year.</li><li>Software Engineer - £40K to 65K/year.</li><li>Machine Learning Engineer - £45K to 75K/year.</li><li>Data Scientist - £50K to 80K/year.</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>10 CPD Accredited e-Certificates</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>10 CPD Accredited Hard Copy Certificates</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Training Express

Python Programming: Beginner To Expert With Complete Career Guide Course

<strong>Start your journey in the cloud computing revolution-what, why, and how!</strong>
        <p>Cloud computing is becoming mainstream in the IT world as a growing number of companies around the globe are in the throes of a digital transformation by using the power of cloud-based services.

Evolution leading to Revolution - traditional IT already uses a variety of virtualization technologies to better utilize physical IT resources. Moving to private clouds is another step in the data center evolution, where existing infrastructures will be utilized and upgraded to provide cloud-based services. Also, public cloud services are gaining huge market momentum as more and more companies migrate their applications to third-party cloud providers. Public clouds are now taking companies to new business frontiers.

Basic Skills for Everyone - cloud computing and cloud services touch almost everyone, whether you are a software developer, IT professional, system administrator, IT manager, solution architect, project manager, marketing manager, product manager, sales engineer, security expert, network expert, students entering the IT world, and so on.

Level 1 - we need to start with something so this is the first course in a larger training program (Getting Started with Cloud Computing) dedicated to the important subject of cloud computing. The training program is divided into several levels, enabling students to start from scratch and grow their knowledge step-by-step, or maybe select a specific level according to their knowledge and experience.

All the code files are placed at https://github.com/PacktPublishing/Getting-Started-with-Cloud-Computing---Level-1</p>
        <h2>What You Will Learn</h2><p>Gain a fundamental understanding of what cloud computing is<br>The evolution from traditional IT to cloud services<br>Learn 5 basic characteristics of cloud computing<br>Understand 3 main service models: SaaS, PaaS, and IaaS<br>Study 4 cloud deployment models: private, public, hybrid, community<br>Understand the key benefits of public cloud services</p>
        <h2>Audience</h2><p>This course is for IT Professionals who are keen to gain a strong understanding of Cloud Computing and want to enhance their service models and deployment models. Cloud administrators, software developers, product/project/marketing managers, and security professionals will hugely benefit from this course.</p>
        <h2>Approach</h2><p>This exhaustive course is packed with step-by-step instructions, practical examples, and helpful advice.</p>
        <h2>Key Features</h2><p>No specific background is required to take this course * The content is well organized and easy to follow for beginners * This course will spark your interest in cloud computing so you can continue to explore the topic in multiple directions</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/getting-started-with-cloud-computing---level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

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

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Introduction</p><p style='margin: 0'>Getting Started: Course Introduction</p></td></tr></tbody></table><br><h4>2. Traffic Load Balancing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview</p><p style='margin: 0'>The Evolution into the Cloud Revolution: Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Traditional IT - Plan, Buy and Operate the Data Center</p><p style='margin: 0'>The Evolution into the Cloud Revolution: Traditional IT - Plan, Buy and Operate the Data Center</p></td></tr><tr><td style='padding: 15px;'><p>3. The First Wave started with SaaS</p><p style='margin: 0'>The Evolution into the Cloud Revolution: The First Wave started with SaaS</p></td></tr><tr><td style='padding: 15px;'><p>4. The Digital Transformation</p><p style='margin: 0'>The Evolution into the Cloud Revolution: The Digital Transformation</p></td></tr><tr><td style='padding: 15px;'><p>5. The Birth of Hyper-scale Public Data Centers</p><p style='margin: 0'>The Evolution into the Cloud Revolution: The Birth of Hyper-scale Public Data Centers</p></td></tr><tr><td style='padding: 15px;'><p>6. A Catalyst for Innovation</p><p style='margin: 0'>The Evolution into the Cloud Revolution: A Catalyst for Innovation</p></td></tr><tr><td style='padding: 15px;'><p>7. The Rise of Artificial Intelligence</p><p style='margin: 0'>The Evolution into the Cloud Revolution: The Rise of Artificial Intelligence</p></td></tr></tbody></table><br><h4>3. The Five Characteristics of Cloud Computing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview</p><p style='margin: 0'>The Five Characteristics of Cloud Computing: Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. #1 - On Demand Self-Service</p><p style='margin: 0'>The Five Characteristics of Cloud Computing: #1 - On Demand Self-Service</p></td></tr><tr><td style='padding: 15px;'><p>3. #2 - Broad Network Access</p><p style='margin: 0'>The Five Characteristics of Cloud Computing: #2 - Broad Network Access</p></td></tr><tr><td style='padding: 15px;'><p>4. #3 - Resource Pooling</p><p style='margin: 0'>The Five Characteristics of Cloud Computing: #3 - Resource Pooling</p></td></tr><tr><td style='padding: 15px;'><p>5. #4 - Rapid Elasticity</p><p style='margin: 0'>The Five Characteristics of Cloud Computing: #4 - Rapid Elasticity</p></td></tr><tr><td style='padding: 15px;'><p>6. #5 - Measured Service</p><p style='margin: 0'>The Five Characteristics of Cloud Computing: #5 - Measured Service</p></td></tr></tbody></table><br><h4>4. Cloud Service Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview</p><p style='margin: 0'>Cloud Service Models: Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. The Cloud Computing Stack</p><p style='margin: 0'>Cloud Service Models: The Cloud Computing Stack</p></td></tr><tr><td style='padding: 15px;'><p>3. IaaS - Infrastructure as a Service</p><p style='margin: 0'>Cloud Service Models: IaaS - Infrastructure as a Service</p></td></tr><tr><td style='padding: 15px;'><p>4. PaaS - Platform as a Service</p><p style='margin: 0'>Cloud Service Models: PaaS - Platform as a Service</p></td></tr><tr><td style='padding: 15px;'><p>5. SaaS - Software as a Service</p><p style='margin: 0'>Cloud Service Models: SaaS - Software as a Service</p></td></tr></tbody></table><br><h4>5. Cloud Deployment Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview</p><p style='margin: 0'>Cloud Deployment Models: Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Public Cloud - Definition</p><p style='margin: 0'>Cloud Deployment Models: Public Cloud - Definition</p></td></tr><tr><td style='padding: 15px;'><p>3. Public Cloud - Global Infrastructure</p><p style='margin: 0'>Cloud Deployment Models: Public Cloud - Global Infrastructure</p></td></tr><tr><td style='padding: 15px;'><p>4. Public Cloud - Products and Services</p><p style='margin: 0'>Cloud Deployment Models: Public Cloud - Products and Services</p></td></tr><tr><td style='padding: 15px;'><p>5. Private Cloud - Definition</p><p style='margin: 0'>Cloud Deployment Models: Private Cloud - Definition</p></td></tr><tr><td style='padding: 15px;'><p>6. Private Cloud - Key Benefits</p><p style='margin: 0'>Cloud Deployment Models: Private Cloud - Key Benefits</p></td></tr><tr><td style='padding: 15px;'><p>7. Hybrid Cloud</p><p style='margin: 0'>Cloud Deployment Models: Hybrid Cloud</p></td></tr><tr><td style='padding: 15px;'><p>8. Community Cloud</p><p style='margin: 0'>Cloud Deployment Models: Community Cloud</p></td></tr><tr><td style='padding: 15px;'><p>9. Cloud Computing Management Platforms</p><p style='margin: 0'>Cloud Deployment Models: Cloud Computing Management Platforms</p></td></tr></tbody></table><br><h4>6. So, why using a Public Cloud Service?</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview</p><p style='margin: 0'>So, why using a Public Cloud Service?: Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. #1 - Easy and Dynamic Scalability</p><p style='margin: 0'>So, why using a Public Cloud Service?: #1 - Easy and Dynamic Scalability</p></td></tr><tr><td style='padding: 15px;'><p>3. #2 - Economies of Scale</p><p style='margin: 0'>So, why using a Public Cloud Service?: #2 - Economies of Scale</p></td></tr><tr><td style='padding: 15px;'><p>4. #3 - Capital and Operational Costs</p><p style='margin: 0'>So, why using a Public Cloud Service?: #3 - Capital and Operational Costs</p></td></tr><tr><td style='padding: 15px;'><p>5. #4 - Increased Reliability</p><p style='margin: 0'>So, why using a Public Cloud Service?: #4 - Increased Reliability</p></td></tr><tr><td style='padding: 15px;'><p>6. #5 - Business Agility and Speed</p><p style='margin: 0'>So, why using a Public Cloud Service?: #5 - Business Agility and Speed</p></td></tr><tr><td style='padding: 15px;'><p>7. #6 - Yes, Security</p><p style='margin: 0'>So, why using a Public Cloud Service?: #6 - Yes, Security</p></td></tr><tr><td style='padding: 15px;'><p>8. Public Cloud Demo - Microsoft Azure - IaaS</p><p style='margin: 0'>So, why using a Public Cloud Service?: Public Cloud Demo - Microsoft Azure - IaaS</p></td></tr></tbody></table><br><h4>7. Course Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Quick Recap</p><p style='margin: 0'>Course Summary: Quick Recap</p></td></tr></tbody></table>

Cloud Computing

Start your journey in the cloud computing revolution-what, why, and how!

Getting Started with Cloud Computing - Level 1

<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>

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

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

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

Machine Learning (ML)

Machine Learning for Apps Level 4

<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 - Secure Azure AI services</h3> <ul> <li>Consider authentication</li> <li>Implement network security</li> </ul> <h3>4 - Monitor Azure AI services</h3> <ul> <li>Monitor cost</li> <li>Create alerts</li> <li>View metrics</li> <li>Manage diagnostic logging</li> </ul> <h3>5 - Deploy Azure AI services in containers</h3> <ul> <li>Understand containers</li> <li>Use Azure AI services containers</li> </ul> <h3>6 - Analyze images</h3> <ul> <li>Provision an Azure AI Vision resource</li> <li>Analyze an image</li> <li>Generate a smart-cropped thumbnail</li> </ul> <h3>7 - Classify images</h3> <ul> <li>Provision Azure resources for Azure AI Custom Vision</li> <li>Understand image classification</li> <li>Train an image classifier</li> </ul> <h3>8 - Detect, analyze, and recognize faces</h3> <ul> <li>Identify options for face detection analysis and identification</li> <li>Understand considerations for face analysis</li> <li>Detect faces with the Azure AI Vision service</li> <li>Understand capabilities of the face service</li> <li>Compare and match detected faces</li> <li>Implement facial recognition</li> </ul> <h3>9 - Read Text in images and documents with the Azure AI Vision Service</h3> <ul> <li>Explore Azure AI Vision options for reading text</li> <li>Use the Read API</li> </ul> <h3>10 - Analyze video</h3> <ul> <li>Understand Azure Video Indexer capabilities</li> <li>Extract custom insights</li> <li>Use Video Analyzer widgets and APIs</li> </ul> <h3>11 - Analyze text with Azure AI Language</h3> <ul> <li>Provision an Azure AI Language resource</li> <li>Detect language</li> <li>Extract key phrases</li> <li>Analyze sentiment</li> <li>Extract entities</li> <li>Extract linked entities</li> </ul> <h3>12 - Build a question answering solution</h3> <ul> <li>Understand question answering</li> <li>Compare question answering to Azure AI Language understanding</li> <li>Create a knowledge base</li> <li>Implement multi-turn conversation</li> <li>Test and publish a knowledge base</li> <li>Use a knowledge base</li> <li>Improve question answering performance</li> </ul> <h3>13 - Build a conversational language understanding model</h3> <ul> <li>Understand prebuilt capabilities of the Azure AI Language service</li> <li>Understand resources for building a conversational language understanding model</li> <li>Define intents, utterances, and entities</li> <li>Use patterns to differentiate similar utterances</li> <li>Use pre-built entity components</li> <li>Train, test, publish, and review a conversational language understanding model</li> </ul> <h3>14 - 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>

Microsoft AZ-104

Azure Cognitive Services

AI-102T00 Designing and Implementing an Azure AI Solution

<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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