Name: AI-900T00 Microsoft Azure AI Fundamentals
Price: 595 GBP
Availability: InStock

<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>

Nexus Human

Learning & Development

Managed Learning Programme (MLP)

Machine Learning (ML)

NLP Practitioner

Azure Machine Learning

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> Access to course contents that are designed and prepared by industry professionals</span>
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										<span>No formal qualifications required, anyone from any academic background can take this course.</span>
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										<span>Access to any internet-enabled smart device.</span>
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                                <h2>Course Curriculum</h2>
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					<td colspan="4"><strong> Section 01: Introduction</strong></td>
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					<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>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>Installing PostgreSQL Database Server Part 1</td><td><span>🕐 00:03:00</span></td>
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					<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>Adding the settings.py Code</td><td><span>🕐 00:07:00</span></td>
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					<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 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 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>Creating a URL Endpoint for our Prediction Dataset</td><td><span>🕐 00:06:00</span></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><i></i></td>
					<td>Resource - Machine Learning for Predictive Maps in Python and Leaflet</td><td><span>🕐 00:00:00</span></td>
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					<td colspan="4"><strong>Assignment</strong></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

Learning To Learn

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

General Admission

Career Development

IT Skills

Data Science

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


Packt

TensorFlow

Are you fascinated with Netflix and YouTube recommendations and how they accurately recommend content that you would like to watch? Are you looking for a practical course that will teach you how to build intelligent recommendation systems? This course will show you how to build accurate recommendation systems in Python using real-world examples.

Building Recommender Systems with Machine Learning and AI

Study Plex

Online Course Marketing

Institute of Leadership & Management (ILM)

Machine Learning Masterclass

<strong>The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R</strong>
        <p>This course is your complete guide to both supervised and unsupervised learning using R. This course covers all the main aspects of practical data science; if you take this course, there is no need to take other courses or buy books on R-based data science. In this age of big data, companies across the Globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in R, you can give your company a competitive edge and take your career to the next level.

Over the course of research, the author realized that almost all the R data science courses and books out there do take account of the multidimensional nature of the topic. This course will give you a robust grounding in the main aspects of machine learning: clustering and classification. Unlike other R instructors, the author digs deep into R's machine learning features and give you a one-of-a-kind grounding in data science! You will go all the way from carrying out data reading & cleaning to machine learning, to finally implementing powerful machine learning algorithms and evaluating their performance via R.
The following topics will be covered: - 
&bull; A full introduction to the R Framework for data science 
&bull; Data structures and reading in R, including CSV, Excel, and HTML data 
&bull; How to pre-process and clean data by removing NAs/No data, visualization 
&bull; Machine learning, supervised learning, and unsupervised learning in R 
&bull; Model building and selection and much more!

The course will help you implement methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R-based data science in real life. After taking this course, you'll easily use data science packages such as Caret to work with real data in R. You'll even understand concepts such as unsupervised learning, dimension reduction, and supervised learning. 

All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Clustering-and-Classification-with-Machine-Learning-in-R</p>
        <h2>What You Will Learn</h2><p>Read-in data into the R environment from different sources<br>Carry out basic data pre-processing and wrangling in R Studio<br>Implement unsupervised/clustering techniques such as K-means clustering<br>Implement dimensional reduction techniques (PCA) and feature selection<br>Implement supervised learning techniques/classification such as Random Forests<br>Evaluate model performance and learn the best practices for evaluating machine learning model accuracy</p>
        <h2>Audience</h2><p>This course is for students interested in getting started with data science applications in the R Studio environment. Students wishing to learn how to implement unsupervised learning on real data. Anyone with prior exposure to R who wants to get started with practical data science.</p>
        <h2>Approach</h2><p>Every video is packed with hands-on instructions and clear explanations. Real data has been used to demonstrate how to implement these techniques in real life, on your data.</p>
        <h2>Key Features</h2><p>Provides in-depth training in everything you need to know to get started with practical R data science * Jargon-free and suitable for people who have a non-mathematical background * In-depth coverage of the latest unsupervised and supervised techniques </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/clustering-and-classification-with-machine-learning-in-r</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome to Clustering & Classification with Machine Learning in R</p><p style='margin: 0'>Introduction to the Course: Welcome to Clustering & Classification with Machine Learning in R</p></td></tr><tr><td style='padding: 15px;'><p>2. Installing R and R Studio</p><p style='margin: 0'>Introduction to the Course: Installing R and R Studio</p></td></tr></tbody></table><br><h4>2. Read in Data from Different Sources in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Read in CSV & Excel Data</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in CSV & Excel Data</p></td></tr><tr><td style='padding: 15px;'><p>2. Read in Unzipped Folder</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Unzipped Folder</p></td></tr><tr><td style='padding: 15px;'><p>3. Read in Online CSV</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Online CSV</p></td></tr><tr><td style='padding: 15px;'><p>4. Read in Googlesheets</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Googlesheets</p></td></tr><tr><td style='padding: 15px;'><p>5. Read in Data from Online HTML Tables-Part 1</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Data from Online HTML Tables-Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Read in Data from Online HTML Tables-Part 2</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Data from Online HTML Tables-Part 2</p></td></tr><tr><td style='padding: 15px;'><p>7. Read Data from a Database</p><p style='margin: 0'>Read in Data from Different Sources in R: Read Data from a Database</p></td></tr></tbody></table><br><h4>3. Data Pre-processing and Visualization</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Remove Missing Values</p><p style='margin: 0'>Data Pre-processing and Visualization: Remove Missing Values</p></td></tr><tr><td style='padding: 15px;'><p>2. More Data Cleaning</p><p style='margin: 0'>Data Pre-processing and Visualization: More Data Cleaning</p></td></tr><tr><td style='padding: 15px;'><p>3. Introduction to dplyr for Data Summarizing-Part 1</p><p style='margin: 0'>Data Pre-processing and Visualization: Introduction to dplyr for Data Summarizing-Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Introduction to dplyr for Data Summarizing-Part 2</p><p style='margin: 0'>Data Pre-processing and Visualization: Introduction to dplyr for Data Summarizing-Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Exploratory Data Analysis (EDA): Basic Visualizations with R</p><p style='margin: 0'>Data Pre-processing and Visualization: Exploratory Data Analysis (EDA): Basic Visualizations with R</p></td></tr><tr><td style='padding: 15px;'><p>6. More Exploratory Data Analysis with xda</p><p style='margin: 0'>Data Pre-processing and Visualization: More Exploratory Data Analysis with xda</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Exploration & Visualization With dplyr & ggplot2</p><p style='margin: 0'>Data Pre-processing and Visualization: Data Exploration & Visualization With dplyr & ggplot2</p></td></tr><tr><td style='padding: 15px;'><p>8. Associations Between Quantitative Variables- Theory</p><p style='margin: 0'>Data Pre-processing and Visualization: Associations Between Quantitative Variables- Theory</p></td></tr><tr><td style='padding: 15px;'><p>9. Testing for Correlation</p><p style='margin: 0'>Data Pre-processing and Visualization: Testing for Correlation</p></td></tr><tr><td style='padding: 15px;'><p>10. Evaluate the Relation Between Nominal Variables</p><p style='margin: 0'>Data Pre-processing and Visualization: Evaluate the Relation Between Nominal Variables</p></td></tr><tr><td style='padding: 15px;'><p>11. Cramer's V for Examining the Strength of Association Between Nominal Variable</p><p style='margin: 0'>Data Pre-processing and Visualization: Cramer's V for Examining the Strength of Association Between Nominal Variable</p></td></tr></tbody></table><br><h4>4. Machine Learning for Data Science</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How is Machine Learning Different from Statistical Data Analysis?</p><p style='margin: 0'>Machine Learning for Data Science: How is Machine Learning Different from Statistical Data Analysis?</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Machine Learning (ML) About? Some Theoretical Pointers</p><p style='margin: 0'>Machine Learning for Data Science: What is Machine Learning (ML) About? Some Theoretical Pointers</p></td></tr></tbody></table><br><h4>5. Unsupervised Learning in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. K-Means Clustering</p><p style='margin: 0'>Unsupervised Learning in R: K-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>2. Other Ways of Selecting Cluster Numbers</p><p style='margin: 0'>Unsupervised Learning in R: Other Ways of Selecting Cluster Numbers</p></td></tr><tr><td style='padding: 15px;'><p>3. Fuzzy K-Means Clustering</p><p style='margin: 0'>Unsupervised Learning in R: Fuzzy K-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>4. Weighted k-means</p><p style='margin: 0'>Unsupervised Learning in R: Weighted k-means</p></td></tr><tr><td style='padding: 15px;'><p>5. Partitioning Around Meloids (PAM)</p><p style='margin: 0'>Unsupervised Learning in R: Partitioning Around Meloids (PAM)</p></td></tr><tr><td style='padding: 15px;'><p>6. Hierarchical Clustering in R</p><p style='margin: 0'>Unsupervised Learning in R: Hierarchical Clustering in R</p></td></tr><tr><td style='padding: 15px;'><p>7. Expectation-Maximization (EM) in R</p><p style='margin: 0'>Unsupervised Learning in R: Expectation-Maximization (EM) in R</p></td></tr><tr><td style='padding: 15px;'><p>8. DBSCAN Clustering in R</p><p style='margin: 0'>Unsupervised Learning in R: DBSCAN Clustering in R</p></td></tr><tr><td style='padding: 15px;'><p>9. Cluster a Mixed Dataset</p><p style='margin: 0'>Unsupervised Learning in R: Cluster a Mixed Dataset</p></td></tr><tr><td style='padding: 15px;'><p>10. Should We Even Do Clustering?</p><p style='margin: 0'>Unsupervised Learning in R: Should We Even Do Clustering?</p></td></tr><tr><td style='padding: 15px;'><p>11. Assess Clustering Performance</p><p style='margin: 0'>Unsupervised Learning in R: Assess Clustering Performance</p></td></tr><tr><td style='padding: 15px;'><p>12. Which Clustering Algorithm to Choose?</p><p style='margin: 0'>Unsupervised Learning in R: Which Clustering Algorithm to Choose?</p></td></tr></tbody></table><br><h4>6. Feature/Dimension Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Dimension Reduction-theory</p><p style='margin: 0'>Feature/Dimension Reduction: Dimension Reduction-theory</p></td></tr><tr><td style='padding: 15px;'><p>2. Principal Component Analysis (PCA)</p><p style='margin: 0'>Feature/Dimension Reduction: Principal Component Analysis (PCA)</p></td></tr><tr><td style='padding: 15px;'><p>3. More on PCA</p><p style='margin: 0'>Feature/Dimension Reduction: More on PCA</p></td></tr><tr><td style='padding: 15px;'><p>4. Multidimensional Scaling</p><p style='margin: 0'>Feature/Dimension Reduction: Multidimensional Scaling</p></td></tr><tr><td style='padding: 15px;'><p>5. Singular Value Decomposition (SVD)</p><p style='margin: 0'>Feature/Dimension Reduction: Singular Value Decomposition (SVD)</p></td></tr></tbody></table><br><h4>7. Feature Selection to Select the Most Relevant Predictors</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Removing Highly Correlated Predictor Variables</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Removing Highly Correlated Predictor Variables</p></td></tr><tr><td style='padding: 15px;'><p>2. Variable Selection Using LASSO Regression</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Variable Selection Using LASSO Regression</p></td></tr><tr><td style='padding: 15px;'><p>3. Variable Selection with FSelector</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Variable Selection with FSelector</p></td></tr><tr><td style='padding: 15px;'><p>4. Boruta Analysis for Feature Selection</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Boruta Analysis for Feature Selection</p></td></tr></tbody></table><br><h4>8. Supervised Learning Theory</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Some Basic Supervised Learning Concepts</p><p style='margin: 0'>Supervised Learning Theory: Some Basic Supervised Learning Concepts</p></td></tr><tr><td style='padding: 15px;'><p>2. Pre-processing for Supervised Learning</p><p style='margin: 0'>Supervised Learning Theory: Pre-processing for Supervised Learning</p></td></tr></tbody></table><br><h4>9. Supervised Learning: Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What are GLMs?</p><p style='margin: 0'>Supervised Learning: Classification: What are GLMs?</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression Models as Binary Classifiers</p><p style='margin: 0'>Supervised Learning: Classification: Logistic Regression Models as Binary Classifiers</p></td></tr><tr><td style='padding: 15px;'><p>3. Binary Classifier with PCA</p><p style='margin: 0'>Supervised Learning: Classification: Binary Classifier with PCA</p></td></tr><tr><td style='padding: 15px;'><p>4. Some Pointers on Evaluating Accuracy</p><p style='margin: 0'>Supervised Learning: Classification: Some Pointers on Evaluating Accuracy</p></td></tr><tr><td style='padding: 15px;'><p>5. Obtain Binary Classification Accuracy Metrics</p><p style='margin: 0'>Supervised Learning: Classification: Obtain Binary Classification Accuracy Metrics</p></td></tr><tr><td style='padding: 15px;'><p>6. More on Binary Accuracy Measures</p><p style='margin: 0'>Supervised Learning: Classification: More on Binary Accuracy Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. Linear Discriminant Analysis</p><p style='margin: 0'>Supervised Learning: Classification: Linear Discriminant Analysis</p></td></tr><tr><td style='padding: 15px;'><p>8. Our Multi-class Classification Problem</p><p style='margin: 0'>Supervised Learning: Classification: Our Multi-class Classification Problem</p></td></tr><tr><td style='padding: 15px;'><p>9. Classification Trees</p><p style='margin: 0'>Supervised Learning: Classification: Classification Trees</p></td></tr><tr><td style='padding: 15px;'><p>10. More on Classification Tree Visualization</p><p style='margin: 0'>Supervised Learning: Classification: More on Classification Tree Visualization</p></td></tr><tr><td style='padding: 15px;'><p>11. Classification with Party Package</p><p style='margin: 0'>Supervised Learning: Classification: Classification with Party Package</p></td></tr><tr><td style='padding: 15px;'><p>12. Decision Trees</p><p style='margin: 0'>Supervised Learning: Classification: Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>13. Random Forest (RF) Classification</p><p style='margin: 0'>Supervised Learning: Classification: Random Forest (RF) Classification</p></td></tr><tr><td style='padding: 15px;'><p>14. Examine Individual Variable Importance for Random Forests</p><p style='margin: 0'>Supervised Learning: Classification: Examine Individual Variable Importance for Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>15. GBM Classification</p><p style='margin: 0'>Supervised Learning: Classification: GBM Classification</p></td></tr><tr><td style='padding: 15px;'><p>16. Support Vector Machines (SVM) for Classification</p><p style='margin: 0'>Supervised Learning: Classification: Support Vector Machines (SVM) for Classification</p></td></tr><tr><td style='padding: 15px;'><p>17. More SVM for Classification</p><p style='margin: 0'>Supervised Learning: Classification: More SVM for Classification</p></td></tr><tr><td style='padding: 15px;'><p>18. Variable Importance in SVM Modelling with rminer</p><p style='margin: 0'>Supervised Learning: Classification: Variable Importance in SVM Modelling with rminer</p></td></tr></tbody></table><br><h4>10. Additional Lectures</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Fuzzy C-Means Clustering</p><p style='margin: 0'>Additional Lectures: Fuzzy C-Means Clustering</p></td></tr></tbody></table>

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The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R

Clustering and Classification with Machine Learning in R

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>Software Engineers</p>Overview<p>The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML.</p><p>This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.</p></div><div id="course-pills-2">This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.</div>

Apache Spark

Cloudera Introduction to Machine Learning with Spark ML and MLlib

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				<h3><span id="docs-internal-guid-8c7677fd-7fff-3dba-9e42-22d2785cca3c"><p><span>Experience the future of </span><span>geographical analysis</span><span> with our </span><span>Machine Learning for Predictive Maps in Python and Leaflet</span><span> course. Master the unique blend of programming, machine learning, and geographic information systems, all while honing your ability to predict and visualise spatial data in a powerful and effective way. This course offers you an unparalleled understanding of </span><span>modern map creation</span><span>, combined with the magic of prediction using </span><span>machine learning models</span><span>.</span></p><br /><p><span>Starting from the ground up, you'll be introduced to all the necessary setups and installations. After that, you will be diving into the depth of </span><span>Django server-side code</span><span> and front-end application code writing. The heart of the course lies in learning how to automate the machine learning pipeline, leading you to </span><span>easily create predictive models</span><span>. </span></p><br /><p><span>Improve your maps with </span><span>Leaflet programming</span><span>, making your predictions accurate and also </span><span>visually striking</span><span>. By the end of this course, you will be armed with experience furnished by our comprehensive project </span><span>source code and assignments</span><span>, empowering you to drive data-driven decisions and insightful spatial analysis. </span><span>Join us</span><span> and </span><span>map your way to success</span><span>! Sign up today.</span></p><br /><br /><p><span>Learning Outcomes:</span></p><p><span>Upon completion of the Machine Learning course, you will be able to:</span></p><br /><ul><li><p><span>Understand how to set up and install relevant software and libraries.</span></p></li><li><p><span>Master Django server-side and application front-end code writing.</span></p></li><li><p><span>Gain proficiency in the concepts and implementation of Machine Learning.</span></p></li><li><p><span>Learn to automate Machine Learning pipelines for efficient workflows.</span></p></li><li><p><span>Acquire skills in Leaflet programming for enhanced map visuals.</span></p></li><li><p><span>Handle project source code effectively for real-world projects.</span></p></li><li><p><span>Apply knowledge practically via assignments and gain experience.</span></p></li></ul><br /><br /><p><span>Who is this course for?</span></p><p><span>This Machine Learning course is ideal for:</span></p><br /><ul><li><p><span>Aspiring Data Scientists keen on harnessing geographical data.</span></p></li><li><p><span>GIS professionals aiming to integrate Machine Learning into their skill set.</span></p></li><li><p><span>Software Developers interested in creating geographically-focused applications.</span></p></li><li><p><span>Analysts keen on enhancing their data visualisation skills with mapping.</span></p></li></ul><br /><br /><p><span>Certification</span></p><p><span>After studying the course materials of the </span><span>Machine Learning for Predictive Maps in Python and Leaflet</span><span> course, there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test, you have a </span><span>range of certification options</span><span> to choose from. You can claim a CPD Accredited PDF Certificate for</span><span> £4.99</span><span>, a CPD Accredited Hardcopy Certificate at</span><span> £8.00</span><span>, or you may choose to receive a PDF Transcript for</span><span> £4.99</span><span> or a</span><span> </span><span>Hardcopy Transcript for</span><span> £9.99</span><span>. Select according to your needs, and we assure </span><span>timely delivery</span><span> of your chosen certificate.</span></p><br /><br /><p><span>Requirements</span></p><p><span>This professionally designed </span><span>Machine Learning for Predictive Maps in Python and Leaflet </span><span>course does not require you to have any prior qualifications or experience. It is open to everyone. You will be able to access the course from anywhere at any time. Just enrol and start learning!</span></p><br /><br /><p><span>Career Path:</span></p><p><span>Our Machine Learning course will help you to </span><span>pursue a range of career paths</span><span>, such as:</span></p><br /><ul><li><p><span>Junior Data Analyst: £25,000 - £35,000 annually.</span></p></li><li><p><span>Data Scientist: £40,000 - £60,000 annually.</span></p></li><li><p><span>GIS Analyst: £30,000 - £45,000 annually.</span></p></li><li><p><span>Geospatial Software Developer: £35,000 - £55,000 annually.</span></p></li><li><p><span>Machine Learning Engineer: £50,000 - £80,000 annually.</span></p></li><li><p><span>Lead Data Scientist (GIS speciality): £70,000 - £100,000+ annually.</span></p></li></ul></span></h3>					</div>
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	<span>Course Curriculum</span>
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					<td colspan="4">Section 01: Introduction</td>
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					<td>Introduction</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td colspan="4">Section 02: Setup and Installations</td>
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					<td><i></i></td>
					<td>Python Installation</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
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					<td>Creating a Python Virtual Environment</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
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					<td>Installing Django</td>
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					<td><span><i></i> 00:09:00</span></td>
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					<td>Installing Visual Studio Code IDE</td>
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					<td><span><i></i> 00:06:00</span></td>
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					<td>Installing PostgreSQL Database Server Part 1</td>
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					<td><span><i></i> 00:03:00</span></td>
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					<td>Installing PostgreSQL Database Server Part 2</td>
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					<td><span><i></i> 00:09:00</span></td>
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					<td colspan="4">Section 03: Writing the Django Server-Side Code</td>
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					<td>Adding the settings.py Code</td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td>Creating a Django Model</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Adding the admin.py Code</td>
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					<td><span><i></i> 00:21:00</span></td>
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					<td colspan="4">Section 04: Writing the Application Front-end Code</td>
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					<td><i></i></td>
					<td>Creating Template Files</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td><i></i></td>
					<td>Creating Django Views</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Creating URL Patterns for the REST API</td>
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					<td><span><i></i> 00:09:00</span></td>
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					<td>Adding the index.html code</td>
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					<td><span><i></i> 00:04:00</span></td>
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					<td>Adding the layout.html code</td>
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					<td><span><i></i> 00:19:00</span></td>
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					<td>Creating our First Map</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Adding Markers</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
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					<td colspan="4">Section 05: Machine Learning</td>
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					<td><i></i></td>
					<td>Installing Jupyter Notebook</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
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					<td>Data Pre-processing</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
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					<td><i></i></td>
					<td>Model Selection</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
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					<td><i></i></td>
					<td>Model Evaluation and Building a Prediction Dataset</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
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					<td colspan="4">Section 06: Automating the Machine Learning Pipeline</td>
				</tr>
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					<td><i></i></td>
					<td>Creating a Django Model</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
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					<td><i></i></td>
					<td>Embedding the Machine Learning Pipeline in the Application</td>
					<td> </td>
					<td><span><i></i> 00:42:00</span></td>
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					<td><i></i></td>
					<td>Creating a URL Endpoint for our Prediction Dataset</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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					<td colspan="4">Section 07: Leaflet Programming</td>
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					<td><i></i></td>
					<td>Creating Multiple Basemaps</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
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					<td><i></i></td>
					<td>Creating the Marker Layer Group</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
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					<td><i></i></td>
					<td>Creating the Point Layer Group</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
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					<td><i></i></td>
					<td>Creating the Predicted Point Layer Group</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
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					<td><i></i></td>
					<td>Creating the Predicted High Risk Point Layer Group</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
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					<td><i></i></td>
					<td>Creating the Legend</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating the Prediction Score Legend</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
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					<td colspan="4">Section 08: Project Source Code</td>
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					<td><i></i></td>
					<td>Resource</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
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								<tr>
					<td colspan="4">Assignment</td>
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					<td><i></i></td>
					<td>Assignment - Machine Learning for Predictive Maps in Python and Leaflet</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
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				</tbody></table></div>

			</div>

Studyhub UK

<p>In many industries, machine learning (ML) for predictive maps using Python and Leaflet is practical. So, it is highly valued in the UK and worldwide. These lessons are available in one convenient location with our QLS-endorsed course, 'Machine Learning for Predictive Maps in Python and Leaflet.'</p><p>The curriculum for the course 'Machine Learning for Predictive Maps in Python and Leaflet' is organised methodically, beginning with an introduction that acquaints students with the goals of the course. The steps for creating the entire application are provided in several parts, which start from writing the Django Server-Side code to the project source code. After that, the course moves via leaflet programming, allowing students to make dynamic, eye-catching maps. Ultimately, students receive a concrete resource for practice and future reference in the project source code.</p><p>This premium QLS-endorsed Machine Learning for Predictive Maps in Python and Leaflet course has intensive study modules, dedicated tutor assistance, customer support, flexible learning opportunities, and a globally recognised certification. Thus, completing our Machine Learning for Predictive Maps in Python and Leaflet course will give you limitless opportunities anywhere and everywhere since the value of QLS-endorsed courses is relatively high!!</p><p>Enrol in this Python and Leaflet course on machine learning for predictive maps now to transform decision-making procedures and influence industry trends through predictive mapping solutions.</p><p><strong>Learning Outcome of the 'Machine Learning for Predictive Maps in Python and Leaflet' Course</strong><strong>:</strong></p><ul><li>Learn to use predictive mapping techniques based on Python to make informed decisions across a range of industries</li><li>Gain a thorough understanding of Leaflet programming to create dynamic and eye-catching maps</li><li>Build a Django server-side framework to lay a solid basis for developing applications and handling data</li><li>Simplify and improve efficiency by automating machine learning pipelines</li><li>Develop your application deployment skills by applying what you have learned to real-world situations</li><li>Access project source code for continued learning and reference, empowering further exploration and refinement of skills</li></ul><p><strong>Why Learn </strong><strong>Machine Learning for Predictive Maps in Python and Leaflet </strong><strong>with us:</strong></p><p>As one of the leading e-learning service providers globally, we strive to provide all our learners with the best e-learning experience possible that can make a real difference in their career progression.</p><p><strong>Our Service for Machine Learning for Predictive Maps in Python and Leaflet</strong><strong>:</strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Teaching Assistant Assignment Help</li><li>QLS Endorsed Teaching Assistant Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Teaching Assistant Study Materials</li><li>14 Days Refund Guarantee</li></ul><p><strong>Course Curriculum</strong></p><p><strong>Machine Learning for Predictive Maps in Python and Leaflet</strong></p><ul><li>Section 01: Introduction</li><li>Section 02: Setup and Installations</li><li>Section 03: Writing the Django Server-Side Code</li><li>Section 04: Writing the Application Front-end Code</li><li>Section 05: Machine Learning</li><li>Section 06: Automating the Machine Learning Pipeline</li><li>Section 07: Leaflet Programming</li><li>Section 08: Project Source Code</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Students must finish and turn in a thorough assignment covering all topics in our curriculum. Our expert tutor will review the assignment to ensure it meets Quality Learning Systems (QLS) standards. Once the assessment is successful and passes a quality check, students will be awarded a certificate for completing the Machine Learning for Predictive Maps in Python and Leaflet Course.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme.</strong></p><p>After the course, participants will receive an endorsed certificate as proof of successful completion. The learner may request a certificate if they complete all course assessments.</p><p><strong>Endorsement:</strong></p><p>After the course, participants will receive an endorsed certificate as proof of successful completion. The learner may request a certificate if they complete all course assessments.</p><h2>
        
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                                        150 CPD hours / points
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                                            Accredited by CPD Quality Standards
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        <div>Who is this course for?</div>
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                            <p>This Machine Learning for Predictive Maps in Python and Leaflet course is beneficial for a wide range of learners. It is especially suitable for the following learners:</p><ul><li><strong>Data scientists and analysts:</strong> Individuals seeking to broaden their expertise in predictive modelling and spatial analysis</li><li><strong>Professionals in Geographic Information Systems (GIS): </strong>Looking to incorporate machine learning into their workflows for mapping and spatial analysis</li><li><strong>Developers and Programmers:</strong> keen on integrating predictive mapping into their projects or applications.</li><li><strong>Urban Planners and Environmental Researchers:</strong> Investigating predictive mapping in the fields of planning, sustainability, and environmental impact</li><li><strong>Logistics and Supply Chain Analysts:</strong> Using predictive mapping to optimise routes and manage supply chains</li><li><strong>Pupils studying geography or computer science: </strong>Interested in finding out how machine learning and cartography are combining</li><li><strong>Decision-Making Roles Professionals:</strong> Looking for Tools and Insights to Support Strategic Decision-Making with Predictive Mapping</li></ul>
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        <div>Requirements</div>
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                            <p>No specific requirements exist for enrolling in the Machine Learning for Predictive Maps in Python and Leafletcourse. Therefore, learners do not need previous qualifications to sign up for this course.</p>
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        <div>Career path</div>
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                            <p>'Machine Learning for Predictive Maps in Python and Leaflet' course is relevant to many trending professions as follows:</p><ul><li>Data Scientist/Spatial Analysis. £40,000-£70,000 per year.</li><li>Python developer (with GIS expertise). £35,000-£60,000 per year.</li><li>Machine learning engineer. £50,000-£80,000 per year.</li><li>Urban Planner/Analyst. £30,000-£50,000 per year.</li></ul>
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        <div>Certificates</div>
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                                    <h4>Diploma in Machine Learning for Predictive Maps in Python and Leaflet at QLS</h4>
        <p>
            Hard copy certificate -
            £75
        </p>

                        </div>

Kingston Open College

Learning Support Assistant (LSA)

ELS (Effective Learning Service)

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support

Machine Learning for Predictive Maps in Python and Leaflet - Level 5 (QLS Endorsed)

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

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Microsoft AI-900

AI-900T00 Microsoft Azure AI Fundamentals

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

Machine Learning for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

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

The Complete Machine Learning Course with Python

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Machine Learning - Use in Flutter

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

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

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

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

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

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

Deep Learning

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

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