Name: Level 7 Data Science & Machine Learning (Python, R, SQL & Microsoft Azure) - - QLS Endorsed
Price: 139 GBP
Availability: InStock

<strong>This AWS SageMaker Canvas course will help you become a machine learning expert and will enhance your skills by offering you comprehensive knowledge and the required hands-on experience on this newly launched cloud-based ML tool, by solving real-time industry-based projects, without needing any complex coding expertise.</strong>
        <p>AWS is the #1 cloud-based tool used industry-wide for machine learning projects. You do not need advanced coding expertise that is generally required in the field of machine learning. Nowadays, machine learning models that usually take many days to build are available very quickly in just a few minutes with the help of SageMaker Canvas.

SageMaker Canvas has a small learning curve and you can pick up even advanced concepts very quickly. It is also available as part of AWS Free Tier.

This course is created with students from various backgrounds in mind. Therefore, we start with the fundamentals and work our way up to more advanced topics. In addition, we will work on four projects in the course itself, from beginning to end. By providing you with in-depth knowledge and the necessary hands-on experience on this newly introduced cloud-based ML tool, this course will help you become a machine learning expert and enhance your skills. You won't need any advanced coding knowledge to complete projects that are based on real-world industry problems. Guidance is offered beyond the tool-you will not only learn the software but also important machine learning principles.

By the end of this course, you will be able to build your own machine learning model and get accurate predictions without writing any code using AWS SageMaker Canvas.

All the resources and datasets for this course are available at:  https://github.com/PacktPublishing/No-Code-Machine-Learning-Using-Amazon-AWS-SageMaker-Canvas</p>
        <h2>What You Will Learn</h2><p>Learn the basics of machine learning<br>Get introduced to AWS SageMaker<br>Use the Banknote Authentication dataset to predict data<br>Use the SMS Spam Collection dataset to predict data<br>Use the Customer Churn Prediction 2020 dataset to predict data<br>Use the Wine Quality dataset to predict data</p>
        <h2>Audience</h2><p>This course is designed for students who want to enter the machine learning domain but don't have coding expertise, or anybody in general who wants to know what machine learning is and how to use it professionally. You won't need any high-configuration computer to learn this tool. All you need is any system with internet connectivity and a basic understanding of machine learning.</p>
        <h2>Approach</h2><p>This course is designed keeping in mind students from all backgrounds. Hence, we cover everything from the basics and gradually progress toward advanced topics. We will also take live industry projects and take every step from start to end in the course.</p>
        <h2>Key Features</h2><p>Machine learning on Amazon's AWS SageMaker Canvas without writing any code * Learn how to train and test the ML models and get accurate predictions * Work on four live projects with a sample dataset</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/No-Code-Machine-Learning-Using-Amazon-AWS-SageMaker-Canvas</p>
        <h2>About the Author</h2><h3>Prince Patni</h3><p>Prince Patni is a software developer who specializes in business intelligence and data science. He has studied engineering, worked in four multinational corporations, and has been all over the world for business. He is currently working as an analyst/developer in a reputable organization. He enjoys both teaching and learning. He is here to offer his knowledge of data analytics, data visualization, business intelligence, data science, and other aspects of software development.</p>
        <h2>Course Outline</h2><h4>1. Introduction to Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Introduction</p><p style='margin: 0'>Welcome to the course! In this video, we will get introduced to the course and its learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Is Machine Learning?</p><p style='margin: 0'>In this video, we will understand machine learning.</p></td></tr></tbody></table><br><h4>2. Introduction to AWS</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is Amazon Web Services (AWS)?</p><p style='margin: 0'>In this video, we will understand Amazon Web Services (AWS).</p></td></tr><tr><td style='padding: 15px;'><p>2. Signing into AWS Console</p><p style='margin: 0'>In this video, you will learn how to sign into the AWS Console.</p></td></tr></tbody></table><br><h4>3. Introduction to SageMaker</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is SageMaker and How Is It Used for Machine Learning?</p><p style='margin: 0'>In this video, we will understand SageMaker and how it is used for machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Is SageMaker Canvas?</p><p style='margin: 0'>In this video, we will understand SageMaker Canvas.</p></td></tr></tbody></table><br><h4>4. Setup</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. SageMaker Domain and User Setup</p><p style='margin: 0'>In this video, we will work on the SageMaker domain and user setup.</p></td></tr><tr><td style='padding: 15px;'><p>2. Set Up Data in S3 Buckets for Use in SageMaker</p><p style='margin: 0'>In this video, you will learn how to set up data in S3 Buckets for use in SageMaker.</p></td></tr></tbody></table><br><h4>5. SageMaker Canvas Interface Walkthrough</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Navigating in SageMaker Canvas</p><p style='margin: 0'>In this video, we will explore SageMaker Canvas.</p></td></tr></tbody></table><br><h4>6. Project 1 - Banknote Authentication</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Adding Training Data</p><p style='margin: 0'>In this video, you will learn how to add training data.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building and Using the Model for Prediction</p><p style='margin: 0'>In this video, you will learn how to build and use the model for prediction.</p></td></tr><tr><td style='padding: 15px;'><p>3. Predict Single and Batch Dataset</p><p style='margin: 0'>In this video, you will learn how to predict single and batch dataset.</p></td></tr><tr><td style='padding: 15px;'><p>4. Validating Accuracy of Batch Predictions</p><p style='margin: 0'>In this video, you will learn how to validate the accuracy of batch predictions.</p></td></tr></tbody></table><br><h4>7. Project 2 - Spam SMS Detection</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Adding Train and Test Data</p><p style='margin: 0'>In this video, you will learn how to add train and test data.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building and Using the Model for Prediction</p><p style='margin: 0'>In this video, you will learn how to build and use the model for prediction.</p></td></tr><tr><td style='padding: 15px;'><p>3. Predicting Data and Validating Accuracy</p><p style='margin: 0'>In this video, you will learn how to predict data and validate the accuracy.</p></td></tr></tbody></table><br><h4>8. Project 3 - Customer Churn Prediction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Adding Data</p><p style='margin: 0'>In this video, you will learn how to add the data.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building the Model</p><p style='margin: 0'>In this video, you will learn how to build the model.</p></td></tr><tr><td style='padding: 15px;'><p>3. Performing and Validating Predictions</p><p style='margin: 0'>In this video, you will learn how to perform and validate predictions.</p></td></tr></tbody></table><br><h4>9. Project 4 - Wine Quality Prediction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Adding and Joining Datasets</p><p style='margin: 0'>In this video, you will learn how to add and join datasets.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building the Model</p><p style='margin: 0'>In this video, you will learn how to build the model.</p></td></tr><tr><td style='padding: 15px;'><p>3. Predicting Test Data</p><p style='margin: 0'>In this video, you will learn how to predict test data.</p></td></tr></tbody></table><br><h4>10. Assignment</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. White Wine Quality Prediction</p><p style='margin: 0'>In this video, you need to work on a self-practice assignment using the Wine Quality dataset and perform white wine quality prediction.</p></td></tr></tbody></table><br><h4>11. Other Important Features in SageMaker Canvas</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Versioning</p><p style='margin: 0'>In this video, we will cover versioning.</p></td></tr></tbody></table><br><h4>12. Congratulations and Next Steps</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Getting Datasets for Practice</p><p style='margin: 0'>In this video, you will learn how to get datasets for practice.</p></td></tr><tr><td style='padding: 15px;'><p>2. Getting Help on SageMaker Canvas</p><p style='margin: 0'>In this video, you will learn how to get help on SageMaker Canvas.</p></td></tr><tr><td style='padding: 15px;'><p>3. Congratulations and Thank You</p><p style='margin: 0'>Congratulations, you did it! Thank you for taking this course. Happy learning!</p></td></tr></tbody></table>

Packt

Testing

Model

Data Science

This AWS SageMaker Canvas course will help you become a machine learning expert and will enhance your skills by offering you comprehensive knowledge and the required hands-on experience on this newly launched cloud-based ML tool, by solving real-time industry-based projects, without needing any complex coding expertise.

No-Code Machine Learning Using Amazon AWS SageMaker Canvas

General Admission

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts</p>Overview<p>This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.<br />Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below<br />Getting Started &amp; Optional Python Quick Refresher<br />Statistics and Probability Refresher and Python Practice<br />Probability Density Function; Probability Mass Function; Naive Bayes<br />Predictive Models<br />Machine Learning with Python<br />Recommender Systems<br />KNN and PCA<br />Reinforcement Learning<br />Dealing with Real-World Data<br />Experimental Design / ML in the Real World<br />Time Permitting: Deep Learning and Neural Networks</p><p>Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices.  This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning.  Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then  explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms</p></div><div id="course-pills-2"><h4>Getting Started</h4><ul><li>Installation: Getting Started and Overview</li><li>LINUX jump start: Installing and Using Anaconda &amp; Course Materials (or reference the default container)</li><li>Python Refresher</li><li>Introducing the Pandas, NumPy and Scikit-Learn Library</li></ul><h4>Statistics and Probability Refresher and Python Practice</h4><ul><li>Types of Data</li><li>Mean, Median, Mode</li><li>Using mean, median, and mode in Python</li><li>Variation and Standard Deviation</li></ul><h4>Probability Density Function; Probability Mass Function; Naive Bayes</h4><ul><li>Common Data Distributions</li><li>Percentiles and Moments</li><li>A Crash Course in matplotlib</li><li>Advanced Visualization with Seaborn</li><li>Covariance and Correlation</li><li>Conditional Probability</li><li>Naive Bayes: Concepts</li><li>Bayes? Theorem</li><li>Naive Bayes</li><li>Spam Classifier with Naive Bayes</li></ul><h4>Predictive Models</h4><ul><li>Linear Regression </li><li>Polynomial Regression</li><li>Multiple Regression, and Predicting Car Prices </li><li>Logistic Regression</li><li>Logistic Regression</li></ul><h4>Machine Learning with Python</h4><ul><li>Supervised vs. Unsupervised Learning, and Train/Test</li><li>Using Train/Test to Prevent Overfitting</li><li>Understanding a Confusion Matrix</li><li>Measuring Classifiers (Precision, Recall, F1, AUC, ROC)</li><li>K-Means Clustering</li><li>K-Means: Clustering People Based on Age and Income</li><li>Measuring Entropy</li><li>LINUX: Installing GraphViz</li><li>Decision Trees: Concepts</li><li>Decision Trees: Predicting Hiring Decisions</li><li>Ensemble Learning</li><li>Support Vector Machines (SVM) Overview</li><li>Using SVM to Cluster People using scikit-learn</li></ul><h4>Recommender Systems</h4><ul><li>User-Based Collaborative Filtering</li><li>Item-Based Collaborative Filtering</li><li>Finding Similar Movie</li><li>Better Accuracy for Similar Movies</li><li>Recommending movies to People</li><li>Improving your recommendations</li></ul><h4>KNN and PCA</h4><ul><li>K-Nearest-Neighbors: Concepts</li><li>Using KNN to Predict a Rating for a Movie</li><li>Dimensionality Reduction; Principal Component Analysis (PCA)</li><li>PCA with the Iris Data Set</li></ul><h4>Reinforcement Learning</h4><ul><li>Reinforcement Learning with Q-Learning and Gym</li></ul><h4>Dealing with Real-World Data</h4><ul><li>Bias / Variance Tradeoff</li><li>K-Fold Cross-Validation</li><li>Data Cleaning and Normalization</li><li>Cleaning Web Log Data</li><li>Normalizing Numerical Data</li><li>Detecting Outliers</li><li>Feature Engineering and the Curse of Dimensionality</li><li>Imputation Techniques for Missing Data</li><li>Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE</li><li>Binning, Transforming, Encoding, Scaling, and Shuffling</li></ul><h4>Experimental Design / ML in the Real World</h4><ul><li>Deploying Models to Real-Time Systems</li><li>A/B Testing Concepts</li><li>T-Tests and P-Values</li><li>Hands-on With T-Tests</li><li>Determining How Long to Run an Experiment</li><li>A/B Test Gotchas</li></ul><h4>Capstone Project</h4><ul><li>Group Project &amp; Presentation or Review</li></ul><h4>Deep Learning and Neural Networks</h4><ul><li>Deep Learning Prerequisites</li><li>The History of Artificial Neural Networks</li><li>Deep Learning in the TensorFlow Playground</li><li>Deep Learning Details</li><li>Introducing TensorFlow</li><li>Using TensorFlow</li><li>Introducing Keras</li><li>Using Keras to Predict Political Affiliations</li><li>Convolutional Neural Networks (CNN?s)</li><li>Using CNN?s for Handwriting Recognition</li><li>Recurrent Neural Networks (RNN?s)</li><li>Using an RNN for Sentiment Analysis</li><li>Transfer Learning</li><li>Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters</li><li>Deep Learning Regularization with Dropout and Early Stopping</li><li>The Ethics of Deep Learning</li><li>Learning More about Deep Learning</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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

Learning & Development

Managed Learning Programme (MLP)

Machine Learning (ML)

NLP Practitioner

Learning To Learn

Azure Machine Learning

Machine Learning Essentials with Python (TTML5506-P)

<strong>In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.</strong>
        <p>The course covers practical issues in statistical computing that include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R programming to mastery.

We understand that theory is important to build a solid foundation, we also understand that theory alone isn't going to get the job done so that's why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you!

R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

By the end of the course, you'll be a professional data scientist with R and confidently apply for jobs and will feel good knowing that you have the skills and knowledge to back it up.

All resources are placed here: https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-</p>
        <h2>What You Will Learn</h2><p>Learn data cleaning, processing, wrangling, and manipulation<br>Learn plotting in R (graphs, charts, plots, histograms, and more)<br>How to create a resume and land your first job as a data scientist<br>Learn machine learning and its various practical applications<br>Learn data and file management in R<br>Use R to clean, analyze, and visualize data</p>
        <h2>Audience</h2><p>This course is designed for beginners who want to learn about data science and machine learning. No prior knowledge of R is required.</p>
        <h2>Approach</h2><p>This course takes you through data analysis in R, data visualization in R, and machine learning with hands-on step-by-step training.</p>
        <h2>Key Features</h2><p>Learn data extraction and web scraping * Learn to build custom data solutions * Learn automating dynamic report generation</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-</p>
        <h2>About the Author</h2><h3>Juan E. Galvan</h3><p>Juan E. Galvan has been an entrepreneur since grade school. His background is in the tech space from digital marketing, e-commerce, web development to programming. He believes in continuous education with the best of a university degree without all the downsides of burdensome costs and inefficient methods. He looks forward to helping people expand their skillsets.</p>
        <h2>Course Outline</h2><h4>1. Data Science and Machine Leaning Course Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Science and Machine Learning Introduction Section Overview</p><p style='margin: 0'>This video explains data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Data Science?</p><p style='margin: 0'>This video explains the concept of data science.</p></td></tr><tr><td style='padding: 15px;'><p>3. Machine Learning Overview</p><p style='margin: 0'>This video gives an overview of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Science + Machine Learning Marketplace</p><p style='margin: 0'>This video explains the various trends and job opportunities in data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>5. Who is this Course For?</p><p style='margin: 0'>This video explains the audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Data Science and Machine Learning Job Opportunities</p><p style='margin: 0'>This video explains the various job opportunities in data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Science Job Roles</p><p style='margin: 0'>This video explains the various job roles for data science.</p></td></tr></tbody></table><br><h4>2. Getting Started with R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Getting Started with R</p><p style='margin: 0'>This video explains the concept of R.</p></td></tr><tr><td style='padding: 15px;'><p>2. R Basics</p><p style='margin: 0'>This video explains the R basics.</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Files</p><p style='margin: 0'>This video explains working with files in R.</p></td></tr><tr><td style='padding: 15px;'><p>4. R Studio</p><p style='margin: 0'>This video explains R studio.</p></td></tr><tr><td style='padding: 15px;'><p>5. Tidyverse Overview</p><p style='margin: 0'>This video provides an overview of tidyverse.</p></td></tr><tr><td style='padding: 15px;'><p>6. Additional Resources</p><p style='margin: 0'>This video shares some additional resources that will help you get along with the course.</p></td></tr></tbody></table><br><h4>3. Data Types and Structures in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Types and Structures in R Section Overview</p><p style='margin: 0'>This video explains data types and structures in R.</p></td></tr><tr><td style='padding: 15px;'><p>2. Basic Types</p><p style='margin: 0'>This video explains basic types in R.</p></td></tr><tr><td style='padding: 15px;'><p>3. Vectors - Part One</p><p style='margin: 0'>This video introduces you to vectors.</p></td></tr><tr><td style='padding: 15px;'><p>4. Vectors - Part Two</p><p style='margin: 0'>This video explains how to interact with R using vectors.</p></td></tr><tr><td style='padding: 15px;'><p>5. Vectors: Missing Values</p><p style='margin: 0'>This video explains vector missing values.</p></td></tr><tr><td style='padding: 15px;'><p>6. Vectors: Coercion</p><p style='margin: 0'>This video explains coercion.</p></td></tr><tr><td style='padding: 15px;'><p>7. Vectors: Naming</p><p style='margin: 0'>This video explains vector naming.</p></td></tr><tr><td style='padding: 15px;'><p>8. Vectors: Miscellaneous</p><p style='margin: 0'>This video explains miscellaneous yet important concepts in vectors.</p></td></tr><tr><td style='padding: 15px;'><p>9. Working with Matrices</p><p style='margin: 0'>This video explains working with matrices.</p></td></tr><tr><td style='padding: 15px;'><p>10. Working with Lists</p><p style='margin: 0'>This video explains working with lists.</p></td></tr><tr><td style='padding: 15px;'><p>11. Introduction to Data Frames</p><p style='margin: 0'>This video explains introduction to data frames.</p></td></tr><tr><td style='padding: 15px;'><p>12. Creating Data Frames</p><p style='margin: 0'>This video explains creating data frames.</p></td></tr><tr><td style='padding: 15px;'><p>13. Data Frames: Helper Functions</p><p style='margin: 0'>This video explains data frames helper functions.</p></td></tr><tr><td style='padding: 15px;'><p>14. Data Frames: Tibbles</p><p style='margin: 0'>This video explains Tibbles in data frames.</p></td></tr></tbody></table><br><h4>4. Intermediate R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Intermedia R Section Introduction</p><p style='margin: 0'>This video introduces you to R section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Relational Operators</p><p style='margin: 0'>This video explains relational operators.</p></td></tr><tr><td style='padding: 15px;'><p>3. Logical Operators</p><p style='margin: 0'>This video explains logical operators,</p></td></tr><tr><td style='padding: 15px;'><p>4. Conditional Statements</p><p style='margin: 0'>This video explains conditional statements.</p></td></tr><tr><td style='padding: 15px;'><p>5. Working with Loops</p><p style='margin: 0'>This video explains working with loops.</p></td></tr><tr><td style='padding: 15px;'><p>6. Working with Functions</p><p style='margin: 0'>This video explains working with functions in R.</p></td></tr><tr><td style='padding: 15px;'><p>7. Working with Packages</p><p style='margin: 0'>This video explains working with packages.</p></td></tr><tr><td style='padding: 15px;'><p>8. Working with Factors</p><p style='margin: 0'>This video explains working with factors in R.</p></td></tr><tr><td style='padding: 15px;'><p>9. Dates and Times</p><p style='margin: 0'>This video explains dates and times in R.</p></td></tr><tr><td style='padding: 15px;'><p>10. Functional Programming</p><p style='margin: 0'>This video explains functional programming.</p></td></tr><tr><td style='padding: 15px;'><p>11. Data Import/Export</p><p style='margin: 0'>This video explains how to import and export data in R.</p></td></tr><tr><td style='padding: 15px;'><p>12. Working with Databases</p><p style='margin: 0'>This video explains working with databases.</p></td></tr></tbody></table><br><h4>5. Data Manipulation in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Manipulation Section Introduction</p><p style='margin: 0'>This video introduces you to data manipulation.</p></td></tr><tr><td style='padding: 15px;'><p>2. Tidy Data</p><p style='margin: 0'>This video explains tidy data.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Pipe Operator</p><p style='margin: 0'>This video explains pipe operator.</p></td></tr><tr><td style='padding: 15px;'><p>4. {dplyr}: The Filter Verb</p><p style='margin: 0'>This video explains the filter verb.</p></td></tr><tr><td style='padding: 15px;'><p>5. {dplyr}: The Select Verb</p><p style='margin: 0'>This video explains the select verb</p></td></tr><tr><td style='padding: 15px;'><p>6. {dplyr}: The Mutate Verb</p><p style='margin: 0'>This video explains the mutate verb.</p></td></tr><tr><td style='padding: 15px;'><p>7. {dplyr}: The Arrange Verb</p><p style='margin: 0'>This video explains the purpose of arrange verb.</p></td></tr><tr><td style='padding: 15px;'><p>8. {dplyr}: The Summarize Verb</p><p style='margin: 0'>This video explains the summarize verb.</p></td></tr><tr><td style='padding: 15px;'><p>9. Data Pivoting: {tidyr}</p><p style='margin: 0'>This video explains data pivoting.</p></td></tr><tr><td style='padding: 15px;'><p>10. String Manipulation: {stringr}</p><p style='margin: 0'>This video explains string manipulation.</p></td></tr><tr><td style='padding: 15px;'><p>11. Web Scraping: {rvest}</p><p style='margin: 0'>This video explains web scraping.</p></td></tr><tr><td style='padding: 15px;'><p>12. JSON Parsing: {jsonlite}</p><p style='margin: 0'>This video explains JSON parsing.</p></td></tr></tbody></table><br><h4>6. Data Visualization in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Visualization in R Section Introduction</p><p style='margin: 0'>This video explains data visualization in R.</p></td></tr><tr><td style='padding: 15px;'><p>2. Getting Started with Data Visualization in R</p><p style='margin: 0'>This video explains getting started with data visualization in R.</p></td></tr><tr><td style='padding: 15px;'><p>3. Aesthetics Mappings</p><p style='margin: 0'>This vide explains aesthetics mappings.</p></td></tr><tr><td style='padding: 15px;'><p>4. Single Variable Plots</p><p style='margin: 0'>This video focuses on single variable plots.</p></td></tr><tr><td style='padding: 15px;'><p>5. Two Variable Plots</p><p style='margin: 0'>This video explains two variable plots.</p></td></tr><tr><td style='padding: 15px;'><p>6. Facets, Layering, and Coordinate Systems</p><p style='margin: 0'>This video explains facets, layering, and coordinate systems.</p></td></tr><tr><td style='padding: 15px;'><p>7. Styling and Saving</p><p style='margin: 0'>This video explains styling and saving.</p></td></tr></tbody></table><br><h4>7. Creating Reports with R Markdown</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to R Markdown</p><p style='margin: 0'>This video is an introduction to R.</p></td></tr></tbody></table><br><h4>8. Building Webapps with R Shiny</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to R Shiny</p><p style='margin: 0'>This video is an introduction to R Shiny</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating a Basic R Shiny App</p><p style='margin: 0'>This video helps you to create a basic R shiny app.</p></td></tr><tr><td style='padding: 15px;'><p>3. Other Examples with R Shiny</p><p style='margin: 0'>This video gives various examples with R shiny.</p></td></tr></tbody></table><br><h4>9. Introduction to Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Machine Learning Part One</p><p style='margin: 0'>This video is an introduction to machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Machine Learning Part Two</p><p style='margin: 0'>This video explains the different approaches in machine learning.</p></td></tr></tbody></table><br><h4>10. Data Preprocessing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Preprocessing Introduction</p><p style='margin: 0'>This video introduces you to data preprocessing.</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Preprocessing</p><p style='margin: 0'>This video explains some of the practical ways of data preprocessing.</p></td></tr></tbody></table><br><h4>11. Linear Regression: A Simple Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression: A Simple Model Introduction</p><p style='margin: 0'>This video explains a simple linear regression model.</p></td></tr><tr><td style='padding: 15px;'><p>2. A Simple Model</p><p style='margin: 0'>This video demonstrates a practical application of machine learning.</p></td></tr></tbody></table><br><h4>12. Exploratory Data Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Exploratory Data Analysis Introduction</p><p style='margin: 0'>This video gives an introduction to exploratory data analysis.</p></td></tr><tr><td style='padding: 15px;'><p>2. Hands-on Exploratory Data Analysis</p><p style='margin: 0'>This video explains hands-on exploratory data analysis.</p></td></tr></tbody></table><br><h4>13. Linear Regression - a Real Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression - Real Model Section Introduction</p><p style='margin: 0'>This video explains linear regression - a real model.</p></td></tr><tr><td style='padding: 15px;'><p>2. Linear Regression in R - Real Model</p><p style='margin: 0'>This video explains linear regression in R.</p></td></tr></tbody></table><br><h4>14. Logistic Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Logistic Regression</p><p style='margin: 0'>This video introduces you to logistic regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression in R</p><p style='margin: 0'>This video explains logistic regression in R.</p></td></tr></tbody></table><br><h4>15. Starting a Career in Data Science</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Starting a Data Science Career Section Overview</p><p style='margin: 0'>This video explains section overview of data science and career.</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating a Data Science Resume</p><p style='margin: 0'>This video explains creating a data science resume.</p></td></tr><tr><td style='padding: 15px;'><p>3. Getting Started with Freelancing</p><p style='margin: 0'>This vide explains how to get started with freelancing.</p></td></tr><tr><td style='padding: 15px;'><p>4. Top Freelance Websites</p><p style='margin: 0'>This video focuses on top freelancing websites.</p></td></tr><tr><td style='padding: 15px;'><p>5. Personal Branding</p><p style='margin: 0'>This video explains personal branding.</p></td></tr><tr><td style='padding: 15px;'><p>6. Networking Do's and Don'ts</p><p style='margin: 0'>This video explains networking do's and don'ts.</p></td></tr><tr><td style='padding: 15px;'><p>7. Setting Up a Website</p><p style='margin: 0'>This video explains how to set up a website.</p></td></tr></tbody></table>

In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

Data Science and Machine Learning with R from A-Z Course [Updated for 2021]

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course</p>Overview<p>Working in a hands-on learning environment led by our expert instructor you'll:<br />Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations.<br />Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects.<br />Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models<br />Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition.<br />Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer.<br />Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease.<br />Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation.</p><p>Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions.</p></div><div id="course-pills-2"><h4>Introduction to Machine</h4><ul><li>Learning and Scala </li><li>Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. </li><li>What is Machine Learning? </li><li>Machine Learning with Scala: Advantages and Use Cases</li></ul><h4>Supervised Learning in Scala</h4><ul><li>Learn the basics of supervised learning and how to apply it using Scala. </li><li>Supervised Learning: Regression and Classification</li><li>Linear Regression in Scala </li><li>Logistic Regression in Scala </li></ul><h4>Unsupervised Learning in Scala</h4><ul><li>Understand unsupervised learning and how to apply it using Scala. </li><li>Unsupervised Learning:Clustering and Dimensionality Reduction </li><li>K-means Clustering in Scala </li><li>Principal Component Analysis in Scala </li></ul><h4>Neural Networks and Deep Learning in Scala</h4><ul><li>Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. </li><li>Introduction to Neural Networks </li><li>Feedforward Neural Networks in Scala </li><li>Deep Learning and Convolutional Neural Networks</li></ul><h4>Introduction to Generative AI and GPT in Scala</h4><ul><li>Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. </li><li>Generative AI: Overview and Use Cases </li><li>Introduction to GPT (Generative Pre-trained Transformer) </li><li>GPT-Scala: A Library for GPT in Scala</li></ul><h4>Reinforcement Learning in Scala</h4><ul><li>Understand the basics of reinforcement learning and its implementation in Scala. </li><li>Introduction to Reinforcement Learning </li><li>Q-learning and Value Iteration </li><li>Reinforcement Learning with Scala</li></ul><h4>Time Series Analysis using Scala</h4><ul><li>Learn time series analysis techniques and how to apply them in Scala. </li><li>Introduction to Time Series Analysis </li><li>Autoregressive Integrated Moving Average (ARIMA) Models </li><li>Time Series Analysis in Scala </li></ul><h4>Natural Language Processing (NLP) with Scala </h4><ul><li>Gain an understanding of natural language processing techniques and their application in Scala. </li><li>Introduction to NLP: Techniques and Applications </li><li>Text Processing and Feature Extraction </li><li>NLP Libraries and Tools for Scala</li></ul><h4>Image Processing and Computer Vision with Scala </h4><ul><li>Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. </li><li>Introduction to Image Processing and Computer Vision </li><li>Feature Extraction and Image Classification </li><li>Image Processing Libraries for Scala</li></ul><h4>Model Evaluation and Validation</h4><ul><li>Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. </li><li>Model Evaluation Metrics </li><li>Cross-Validation Techniques </li><li>Model Selection and Tuning in Scala</li></ul><h4>Scalable Machine Learning with Scala</h4><ul><li>Learn how to handle large-scale machine learning problems using Scala. </li><li>Challenges of Large-Scale Machine Learning </li><li>Data Partitioning and Parallelization </li><li>Distributed Machine Learning with Scala</li></ul><h4>Machine Learning Deployment and Production</h4><ul><li>Understand the process of deploying machine learning models into production using Scala. </li><li>Deployment Challenges and Best Practices </li><li>Model Serialization and Deserialization </li><li>Monitoring and Updating Models in Production</li></ul><h4>Ensemble Learning Techniques in Scala</h4><ul><li>Discover ensemble learning techniques and their implementation in Scala. </li><li>Introduction to Ensemble Learning </li><li>Bagging and Boosting Techniques </li><li>Implementing Ensemble Models in Scala</li></ul><h4>Feature Engineering for Machine Learning in Scala</h4><ul><li>Learn advanced feature engineering techniques to improve machine learning model performance in Scala. </li><li>Importance of Feature Engineering in Machine Learning </li><li>Feature Scaling and Normalization Techniques </li><li>Handling Missing Data and Categorical Features</li></ul><h4>Advanced Optimization Techniques for Machine Learning</h4><ul><li>Understand advanced optimization techniques for machine learning models and their application in Scala. </li><li>Gradient Descent and Variants </li><li>Regularization Techniques (L1 and L2) </li><li>Hyperparameter Tuning Strategies</li></ul></div>

Unsupervised Machine Learning

Deep Learning

Machine Learning Essentials for Scala Developers (TTML5506-S)

<p>Welcome to the world of machine learning, Java, and Python programming! This exciting bundle offers a comprehensive learning experience with a variety of courses that will equip you with the knowledge and skills needed to excel in these fields. Within this bundle, you will find a total of eight courses, including three QLS-endorsed courses and five relevant CPD QS-accredited courses.</p><p>The three QLS-endorsed courses in this bundle are the Machine Learning with Python course, the Introduction to Java course, and the Diploma in Python Programming. These courses have been carefully curated and designed to provide you with a solid foundation in machine learning and programming using both Java and Python. Upon completion of these courses, you will receive hardcopy certificates to showcase your achievement.</p><p>In addition to the QLS-endorsed courses, this bundle also includes five CPD QS-accredited courses. These courses are highly relevant to the field and will further enhance your understanding and expertise. They include Azure Machine Learning, Complete Python Machine Learning &amp; Data Science Fundamentals, Machine Learning Model Using AWS SageMaker Canvas, Statistics &amp; Probability for Data Science &amp; Machine Learning, and 2021 Data Science &amp; Machine Learning with R from A-Z.</p><p>Key Features of the Machine Learning, Java and Python Programming Bundle:</p><ul><li><strong>3 QLS-Endorsed Courses:</strong> We proudly offer 3 QLS-endorsed courses within our Machine Learning, Java and Python Programming bundle, providing you with industry-recognized qualifications. Plus, you'll receive a <strong>free hardcopy certificate</strong> for each of these courses.</li><ul><li><strong>QLS Course 01: Machine Learning with Python</strong></li><li><strong> QLS Course 02: Introduction to Java</strong></li><li><strong> QLS Course 03: Diploma in Python Programming</strong></li></ul></ul><ul><li><strong>5 CPD QS Accredited Courses</strong>: Additionally, our bundle includes 5 relevant CPD QS accredited courses, ensuring that you stay up-to-date with the latest industry standards and practices.</li><ul><li><strong>Course 01:</strong> Azure Machine Learning</li><li><strong>Course 02:</strong> Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 03:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 04:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 05:</strong> 2021 Data Science &amp; Machine Learning with R from A-Z</li></ul></ul><ul><li>In Addition, you'll get <strong>Five Career Boosting Courses</strong> absolutely FREE with this Bundle.</li><ul><li><strong>Course 01:</strong> Professional CV Writing</li><li><strong>Course 02:</strong> Job Search Skills</li><li><strong>Course 03:</strong> Self-Esteem &amp; Confidence Building</li><li><strong>Course 04:</strong> Professional Diploma in Stress Management</li><li><strong>Course 05:</strong> Complete Communication Skills Master Class</li></ul><li>Convenient Online Learning: Our Machine Learning, Java and Python Programming courses are accessible online, allowing you to learn at your own pace and from the comfort of your own home.</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Gain a comprehensive understanding of machine learning principles and algorithms.</li><li>Develop proficiency in programming with Python and Java, and apply these skills to real-world projects.</li><li>Explore the fundamentals of data science and statistical analysis.</li><li>Learn how to leverage cloud-based platforms, such as Azure and AWS, for machine learning tasks.</li><li>Build and deploy machine learning models using various frameworks and tools.</li><li>Acquire practical knowledge in data preprocessing, feature selection, and model evaluation techniques.</li><li>Gain expertise in using R for data science and machine learning applications.</li></ul> <p>This bundle combines the power of machine learning, Java programming, and Python programming to provide you with a comprehensive skill set in these high-demand fields. With a total of eight courses, including three QLS-endorsed and five CPD QS-accredited courses, this bundle covers all the essential topics and techniques needed to succeed.</p><p>Whether you are a beginner looking to enter the world of machine learning and programming or an experienced professional seeking to expand your knowledge and skills, this bundle has something for everyone. From understanding the fundamentals to applying advanced algorithms, you will gain a deep understanding of machine learning principles and the ability to develop robust applications using Java and Python.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        250 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 interested in starting a career in machine learning, Java programming, or Python programming.</li><li>Professionals seeking to enhance their skills and stay updated with the latest trends in these fields.</li><li>Students pursuing degrees in computer science, data science, or related disciplines.</li><li>Anyone looking to broaden their knowledge and explore the exciting field of machine learning and programming.</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Machine Learning Engineer: £45,000 - £90,000 per year</li><li>Data Scientist: £40,000 - £80,000 per year</li><li>Java Developer: £30,000 - £70,000 per year</li><li>Python Developer: £35,000 - £75,000 per year</li><li>Cloud Solutions Architect: £50,000 - £100,000 per year</li><li>Statistical Analyst: £35,000 - £65,000 per year</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4> Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4> Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Academy for Health and Fitness

Machine Learning, Java and Python Programming

Thinking about learning Big Data?
**Until August 31st, the cost of this course has been reduced from £225 to only £15, as part of our summer offers **
The Big Data for Business course teaches learners to understand how to manage Big Data and its use with Machine Learning.
You will learn the terminology, general principles, concepts and approaches related to the management of Big Data. This includes the relationship between data and information, as well as an understanding of Big Data frameworks (including Hadoop) and applicable solutions.
Duco Digital Training has been awarded as one of the best training providers in Artificial Intelligence.
Online, self-study, global access to study in any country
COURSE FEATURES
Study time: 16 hours
Gain Industry Skills
12 months course access
No exam
Tutor support
Learn Online - Study at home or work
Free certificate to download and add to your LinkedIn profile and CV.
Big Data for Business
The Big Data for Business course will help develop your knowledge and understanding in these specific subjects:
Course Subjects You Learn
1. What is Big Data:
Define Big Data.
Describe the difference between data and Big Data.
Explain the different types of data structures and what can be included within each type.
Explain the need for a Big Data strategy
2. Working with a Big Data framework:
Explain the need for a Big Data framework/network.
Identify Big Data frameworks and the benefits of using them.
Explain the need for data architecture.
3. Big data analytics:
Identify Big Data analytics.
Explain why data analytics is important.
4. Big Data and Machine Learning:
Describe why Machine Learning needs Big Data.
Explain how Machine Learning uses Big Data.
Illustrate the challenges of Big Data.
Study Easily Online with Duco Digital
• Work through online course content which is easy to follow, understand and in a logical format• Login to continue your course and monitor your progression easily• No exam • Tutor support available 9 am - 5 pm weekdays GMT• Start today!
Meet Our Instructor

Darren is the Company Director of Duco Digital Training and a BCS Accredited Trainer in Artificial Intelligence. He has over 30 years of experience in international marketing, computing projects, training students for organisations and governments in AI, marketing and sales in Europe, SE Asia and the Middle East and is currently researching and studying a PhD in AI Ethics.If you have any questions about choosing the right course for your career, please contact us.
Why should I take this course?
This Foundation professional certification in Big Data for Business will help develop your career especially if you are planning to or are now working with data, or on an AI or Machine Learning project.

With the Artificial Intelligence sector set to be worth over $15tn by 2030, this is a great time to learn about AI.
You can upgrade your course with a BCS exam to gain a BCS Professional Certification. This is optional and details are inside the course if you would like to do this.
Have a question? - contact us
If you have any questions, please contact us or message us on What'sApp. We're here to help you pass your course and grow your career; study with Duco Digital Training, the UK's leading online BCS accredited training provider.

Duco Digital Training

IT Certifications

IT Skills

Big Data Analytics

IT (Information Technology)

Computing & IT

Big Data

IT Literacy

Thinking about learning Big Data?

The Big Data for Business course teaches learners to understand how to manage Big Data and its use with Machine Learning. 93% off until August 31 2024.

Big Data for Business

<p><strong>Picture this</strong>: A vast cloud realm, brimming with data, all ready to weave tales of insight and wisdom. Welcome aboard our '<strong>Microsoft Azure Machine Learning</strong>' odyssey! This isn't just a course; it's a canvas where technology meets art. Unravel the mysteries of Azure Machine Learning Studio, dance with data modules, and play with predictions about incomes and the price tags of fancy cars. Ready for a whirlwind romance with data?</p>
<p><strong>Learning Outcomes</strong></p>
<ul>
<li>Become best pals with Microsoft Azure Machine Learning Studio - it's cooler than it sounds!</li>
<li>Play detective with a variety of puzzling machine learning modules.</li>
<li>Predict tomorrow! Well, not literally, but we'll dabble in forecasting incomes and car prices.</li>
<li>Revel in the drama of data as we churn stories from raw numbers.</li>
<li>Flip through the fascinating pages of notebooks in Azure, a bibliophile's techy delight.</li>
</ul>
<h3><b>Why choose this Microsoft Azure Machine Learning<span> course</span>?</b></h3>
<ul>
<li>Unlimited access to the course for a lifetime.</li>
<li>Opportunity to earn a certificate accredited by the <b>CPD Quality Standards</b><b> </b>after completing this course.</li>
<li>Structured lesson planning in line with industry standards.</li>
<li>Immerse yourself in innovative and captivating course materials and activities.</li>
<li>Assessments are designed to evaluate advanced cognitive abilities and skill proficiency.</li>
<li>Flexibility to complete the <b>Microsoft Azure Machine Learning</b> Course at your own pace, on your own schedule.</li>
<li>Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.</li>
</ul>
<h3><b>Who is this Microsoft Azure Machine Learning<span> </span></b><b><span>course </span></b><b>for?</b></h3>
<ul>
<li>Tech aficionados itching to ride the cloud wave.</li>
<li>Curious data wizards eager to add Azure spells to their grimoire.</li>
<li>Project champs on a quest for the perfect cloud platform.</li>
<li>Code artists seeking to stitch machine learning into their digital tapestry.</li>
<li>Future stars of tech, dreaming big and aiming higher.</li>
</ul>
<h3><b>Career path</b></h3>
<ul>
<li><strong>Azure Machine Learning Maestro</strong> - £45,000 - £65,000</li>
<li><strong>Data Alchemist</strong> - £50,000 - £72,000</li>
<li><strong>Cloud Castle Architect</strong> - £70,000 - £92,000</li>
<li><strong>Machine Learning Magician</strong> - £55,000 - £78,000</li>
<li><strong>Storyteller of Stats</strong> - £60,000 - £85,000</li>
<li><strong>Azure Artisan</strong> - £40,000 - £60,000</li>
</ul>
<h3><b>Prerequisites</b></h3>
<p><span>This </span><b>Microsoft Azure Machine Learning</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Microsoft Azure Machine Learning</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8.</span></p><div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4">Azure Machine Learning</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Introduction to Microsoft Azure Machine Learning Studio and Administration</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Various Modules in Machine Learning</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
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					<td>Module 03: Prediction of Income (Automated Tutorial)</td>
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					<td><span><i></i> 00:33:00</span></td>
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					<td>Module 04: Prediction of Automobile Price using Linear Regression Algorithm</td>
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					<td>Module 05: Dataset Processing and Analysis (Sample-1)</td>
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					<td>Module 06: Cross Validation for Regression (Sample-2)</td>
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					<td><span><i></i> 00:28:00</span></td>
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					<td>Module 07: Clustering Group Iris data (Sample-3)</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
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					<td>Module 08: Introduction on Notebook in Microsoft Azure Machine Learning Studio</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
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					<td colspan="4">Assignment</td>
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					<td><i></i></td>
					<td>Assignment - Microsoft Azure Machine Learning</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
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			</div>

Studyhub UK

Microsoft AZ-900

Microsoft Virtual Academy (MVA)

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Microsoft Azure Machine Learning

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This course is intended for:<br />Developers<br />Solutions Architects<br />Data Engineers<br />Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon<br />SageMaker</p>Overview<p>In this course, you will learn to:<br />Select and justify the appropriate ML approach for a given business problem<br />Use the ML pipeline to solve a specific business problem<br />Train, evaluate, deploy, and tune an ML model using Amazon SageMaker<br />Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS<br />Apply machine learning to a real-life business problem after the course is complete</p><p>This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.</p></div><div id="course-pills-2"><h4>Module 0: Introduction</h4><ul><li>Pre-assessment</li></ul><h4>Module 1: Introduction to Machine Learning and the ML Pipeline </h4><ul><li>Overview of machine learning, including use cases, types of machine learning, and key concepts<br />Overview of the ML pipeline<br />Introduction to course projects and approach</li></ul><h4>Module 2: Introduction to Amazon SageMaker</h4><ul><li>Introduction to Amazon SageMaker</li><li>Demo: Amazon SageMaker and Jupyter notebooks</li><li>Hands-on: Amazon SageMaker and Jupyter notebooks</li></ul><h4>Module 3: Problem Formulation</h4><ul><li>Overview of problem formulation and deciding if ML is the right solution</li><li>Converting a business problem into an ML problem</li><li>Demo: Amazon SageMaker Ground Truth</li><li>Hands-on: Amazon SageMaker Ground Truth</li><li>Practice problem formulation</li><li>Formulate problems for projects</li></ul><h4>Module 4: Preprocessing</h4><ul><li>Overview of data collection and integration, and techniques for data preprocessing and visualization<br />Practice preprocessing<br />Preprocess project data<br />Class discussion about projects</li></ul><h4>Module 5: Model Training</h4><ul><li>Choosing the right algorithm<br />Formatting and splitting your data for training<br />Loss functions and gradient descent for improving your model<br />Demo: Create a training job in Amazon SageMaker</li></ul><h4>Module 6: Model Evaluation</h4><ul><li>How to evaluate classification models</li><li>How to evaluate regression models</li><li>Practice model training and evaluation</li><li>Train and evaluate project models</li><li>Initial project presentations</li></ul><h4>Module 7: Feature Engineering and Model Tuning</h4><ul><li>Feature extraction, selection, creation, and transformation</li><li>Hyperparameter tuning</li><li>Demo: SageMaker hyperparameter optimization</li><li>Practice feature engineering and model tuning</li><li>Apply feature engineering and model tuning to projects</li><li>Final project presentations</li></ul><h4>Module 8: Deployment</h4><ul><li>How to deploy, inference, and monitor your model on Amazon SageMaker<br />Deploying ML at the edge<br />Demo: Creating an Amazon SageMaker endpoint<br />Post-assessment<br />Course wrap-up</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans The Machine Learning Pipeline on AWS 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 The Machine Learning Pipeline on AWS 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>

The Machine Learning Pipeline on AWS

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					<div><p><span>Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Machine Learning for Predictive Maps in Python and Leaflet</span><span> has been specially designed to help learners gain a good command of Machine Learning for Predictive Maps in Python and Leaflet</span><span>, providing them with a solid foundation of knowledge to become a qualified professional.</span></p><p><span>Through this Machine Learning for Predictive Maps in Python and Leaflet</span><span>, you will gain both practical and theoretical understanding of Machine Learning for Predictive Maps in Python and Leaflet</span><span> that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.</span></p><p><span>Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.</span></p></div>
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										<span>No formal qualifications required, anyone from any academic background can take this course.</span>
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                                <h2>Course Curriculum</h2>
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<div>
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					<td colspan="4"><strong> Section 01: Introduction</strong></td>
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					<td>▶</td>
					<td>Introduction</td><td><span>🕐 00:10:00</span></td>
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					<td colspan="4"><strong>Section 02: Setup and Installations</strong></td>
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					<td>▶</td>
					<td>Python Installation</td><td><span>🕐 00:04:00</span></td>
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					<td>Creating a Python Virtual Environment</td><td><span>🕐 00:07:00</span></td>
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					<td>Installing Django</td><td><span>🕐 00:09:00</span></td>
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					<td>Installing Visual Studio Code IDE</td><td><span>🕐 00:06:00</span></td>
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					<td>▶</td>
					<td>Installing PostgreSQL Database Server Part 1</td><td><span>🕐 00:03:00</span></td>
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					<td>▶</td>
					<td>Installing PostgreSQL Database Server Part 2</td><td><span>🕐 00:09:00</span></td>
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					<td colspan="4"><strong>Section 03: Writing the Django Server-Side Code</strong></td>
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					<td>▶</td>
					<td>Adding the settings.py Code</td><td><span>🕐 00:07:00</span></td>
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					<td>▶</td>
					<td>Creating a Django Model</td><td><span>🕐 00:10:00</span></td>
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					<td>Adding the admin.py Code</td><td><span>🕐 00:21:00</span></td>
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					<td colspan="4"><strong>Section 04: Writing the Application Front-end Code</strong></td>
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					<td>Creating Template Files</td><td><span>🕐 00:10:00</span></td>
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					<td>Creating Django Views</td><td><span>🕐 00:10:00</span></td>
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					<td>Creating URL Patterns for the REST API</td><td><span>🕐 00:09:00</span></td>
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					<td>Adding the index.html code</td><td><span>🕐 00:04:00</span></td>
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					<td>Adding the layout.html code</td><td><span>🕐 00:19:00</span></td>
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					<td>Creating our First Map</td><td><span>🕐 00:10:00</span></td>
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					<td>Adding Markers</td><td><span>🕐 00:16:00</span></td>
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					<td colspan="4"><strong>Section 05: Machine Learning</strong></td>
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					<td>Installing Jupyter Notebook</td><td><span>🕐 00:07:00</span></td>
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					<td>Model Selection</td><td><span>🕐 00:20:00</span></td>
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					<td>Model Evaluation and Building a Prediction Dataset</td><td><span>🕐 00:11:00</span></td>
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					<td colspan="4"><strong>Section 06: Automating the Machine Learning Pipeline</strong></td>
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					<td>Creating a Django Model</td><td><span>🕐 00:04:00</span></td>
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					<td>Embedding the Machine Learning Pipeline in the Application</td><td><span>🕐 00:42:00</span></td>
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					<td>Creating a URL Endpoint for our Prediction Dataset</td><td><span>🕐 00:06:00</span></td>
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					<td colspan="4"><strong>Section 07: Leaflet Programming</strong></td>
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					<td>Creating Multiple Basemaps</td><td><span>🕐 00:09:00</span></td>
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					<td>Creating the Marker Layer Group</td><td><span>🕐 00:10:00</span></td>
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					<td>Creating the Point Layer Group</td><td><span>🕐 00:12:00</span></td>
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					<td>Creating the Predicted Point Layer Group</td><td><span>🕐 00:07:00</span></td>
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					<td>Creating the Predicted High Risk Point Layer Group</td><td><span>🕐 00:12:00</span></td>
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					<td>Creating the Legend</td><td><span>🕐 00:09:00</span></td>
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					<td>Creating the Prediction Score Legend</td><td><span>🕐 00:15:00</span></td>
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					<td colspan="4"><strong>Section 08: Project Source Code</strong></td>
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					<td>Resource - Machine Learning for Predictive Maps in Python and Leaflet</td><td><span>🕐 00:00:00</span></td>
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					<td>Assignment - Machine Learning for Predictive Maps in Python and Leaflet</td><td><span>🕐 00:00:00</span></td>
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Skill Up

Online Course Creation

Trainers Course

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Machine Learning for Predictive Maps in Python and Leaflet

Career Development

Python

Flat Discount: 52% OFF! QLS Endorsed| 40 Courses Diploma| 400 CPD Points| Free PDF+Transcript Certificate| Lifetime Access


Level 7 Data Science & Machine Learning (Python, R, SQL & Microsoft Azure) - - QLS Endorsed


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

Data Engineering

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

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 for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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

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

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

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

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

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

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

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

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