Top 8 Recommended Machine Learning Self-Study Materials! [November 2024]

This course provides the essential foundations for any beginner who truly wants to master AI and machine learning. Crucial, foundational AI concepts, all bundled into one course. These concepts will be relevant for years to come. Mastering any craft, requires that you have solid foundations. Anyone who is thinking about starting a career in AI and machine learning will benefit from this. Non-technical professionals such as marketers, business analysts, etc. will be able to effectively converse and work with data scientists, machine learning engineers, or even data scientists if they apply themselves to understanding the concepts in this course.

Many misconceptions about artificial intelligence and machine learning are clarified in this course. After completing this course, you will understand the difference between AI, machine learning, deep learning, reinforcement learning, deep reinforcement learning, etc.

The fundamental concepts that govern how machines learn, and how machine learning uses mathematics in the background, are clearly explained. I only reference high school math concepts in this course. This is because neural networks, which are used extensively in all spheres of machine learning, are mathematical function approximators. I therefore cover the basics of functions, and how functions can be approximated, as part of the explanation of neural networks.

This course does not get into any coding, or complex mathematics. This course is intended to be a baseline stepping stone for more advanced courses in AI and machine learning.

1. AI and Machine Learning for Beginners
2. Introduction and Course Outline
3. Download The *Amazing* +100 Page Workbook For this Course
4. Let's Celebrate Your Progress In This Course: 25% > 50% > 75% > 100%!!
5. Introduce Yourself To Your Fellow Students And Tell Us What You Want To Learn
6. Section Showdown: Student to Star!
7. What is Artificial Intelligence?
8. What is Artificial Intelligence? How intelligent is AI and ChatGPT really?
9. Traditional Software Programmes vs AI systems vs?
10. Section Showdown: Student to Star!
11. What is Machine Learning?
12. Math and Data Science replaces Traditional Programming. A regression example.
13. Introducing Function Approximation, Neural Networks, Encoding and Decoding
14. Supervised, Unsupervised and Reinforcement Machine Learning Models & Algorithms
15. Section Showdown: Student to Star!
16. Deep Learning and Neural Networks
17. The Basics of Deep Learning and Neural Networks
18. Section Showdown: Student to Star!
19. Introducing the next part of this course: Practical AI with Model Builder.
20. Introduction, Prerequisites and Learning Outcomes
21. Introducing Model Builder and the Approach for this Course
22. You've Achieved 25% >> Let's Celebrate Your Progress And Keep Going To 50% >>
23. Section Showdown: Student to Star!
24. Visual Studio and Model Builder
25. Download, Install and Configure Visual Studio
26. Launch Visual Studio and Start a Coding Project
27. Section Showdown: Student to Star!
28. Model Builder and the Machine Learning Process
29. Introducing Model Builder and the Machine Learning Process
30. Model Builder Tasks
31. Preparing Data for Machine Learning
32. Machine Learning - Training a Model
33. Evaluating the performance of a trained model
34. Section Showdown: Student to Star!
35. Machine Learning Demo with Model Builder
36. Machine Learning in Action Part 1: Getting training data
37. Machine Learning in Action Part 2: Preparing the training data
38. Demo Part 3
39. Demo Part 4
40. Understand and Interpret Model Performance
41. Consuming a Model and Checking for Overfitting
42. Section Showdown: Student to Star!
43. Introduction to AI and Leveraging it in Cybersecurity
44. Introduction and Learning Outcomes PLUS download full workbook
45. Gen AI Limits in Cybersecurity
46. Section Showdown: Student to Star!
47. Introduction to the Transformer Architecture and Large Language Models(LLMs)
48. Transformers
49. Showcasing LLMs
50. You've Achieved 50% >> Let's Celebrate Your Progress And Keep Going To 75% >>
51. Section Showdown: Student to Star!
52. LLM Limits and Output Variability
53. LLM Limits in Cybersecurity
54. LLM Output Variability
55. Introducing Text Embeddings and Vectors
56. Introducing Text Embeddings
57. Embeddings and Vectors
58. Test your knowledge now to achieve your goals!
59. You can do it! Maximise your score and boost your learning!
60. Featurized Representation and 3D Representations
61. Featurized Representations
62. 3D Representations
63. Word Math and Higher Dimensions
64. Word Math
65. Higher Dimensions
66. Web Search and Intro to the Code
67. Web Search and Spam Detection
68. Intro to the Code Section
69. You've Achieved 75% >> Let's Celebrate Your Progress And Keep Going To 100% >>
70. Code-Walkthrough
71. Code-Walkthrough Intro
72. Code-Walkthrough Lesson Outline
73. Code-Walkthrough - Environment Setup
74. 1. Code-Walkthrough - Environment Setup
75. 2. Code-Walkthrough - Environment Setup
76. 3. Code-Walkthrough - Environment Setup
77. Code-Walkthrough - Data Prep
78. 1. Code-Walkthrough - Data Prep
79. 2. Code-Walkthrough - Data Prep
80. 3. Code-Walkthrough - Data Prep
81. 4. Code-Walkthrough - Data Prep
82. Code-Walkthrough - Embeddings
83. 1. Code-Walkthrough - Embeddings
84. 2. Code-Walkthrough - Embeddings
85. 3. Code-Walkthrough - Embeddings
86. Code-Walkthrough - Model Training and Evaluation
87. 1. Code-Walkthrough - Model Training and Evaluation
88. 2. Code-Walkthrough - Model Training and Evaluation
89. 3. Code-Walkthrough - Model Training and Evaluation
90. 4. Code-Walkthrough - Model Training and Evaluation
91. Conclusion
92. Congrats and Conclusion
93. You've Achieved 100% >> Let's Celebrate! Remember To Share Your Certificate!!
94. International Financial Services; Live Webinar Presentation by Irlon Terblanch
95. 1. Prompt Engineering - Introduction
96. 2. Prompt Engineering - Techniques
97. 3. Prompt Engineering - Use Cases
98. 4. Prompt Engineering - Q&A
99. Test your knowledge now to achieve your goals!
100. You can do it! Maximise your score and boost your learning!
101. Introduction to Artificial Intelligence Fundamentals
102. Course Description
103. Explanation of Artificial Intelligence
104. Download The *Amazing* +100 Page Workbook For this Course
105. Introduce Yourself To Your Fellow Students And Tell Us What You Want To Learn
106. Machine Learning vs. AI
107. Applications of AI in Business
108. AI in Digital Transformation
109. AI Ethics and Governance
110. Let's Celebrate Your Progress In This Course: 25% > 50% > 75% > 100%!!
111. Section Showdown: Student to Star!
112. Understanding Data and Information
113. Data Types and Sources
114. Data Collection and Management
115. Data Preprocessing Techniques
116. Data Representation for AI
117. Data Quality and Governance
118. Section Showdown: Student to Star!
119. Exploring Machine Learning Basics
120. Machine Learning Overview
121. Types of Machine Learning
122. Supervised Learning Fundamentals
123. Unsupervised Learning Concepts
124. Reinforcement Learning Introduction
125. Section Showdown: Student to Star!
126. Supervised Learning Techniques
127. Linear Regression in Machine Learning
128. Logistic Regression for Classification
129. Decision Tree Models
130. Support Vector Machines (SVM)
131. Neural Networks Fundamentals
132. Section Showdown: Student to Star!
133. Unsupervised Learning Approaches
134. Clustering Techniques
135. K-Means Clustering
136. Hierarchical Clustering
137. Dimensionality Reduction Methods
138. Anomaly Detection in Unsupervised Learning
139. Section Showdown: Student to Star!
140. Deep Dive into Neural Networks
141. Neuron and Activation Functions
142. Feedforward Neural Networks
143. Backpropagation Algorithm
144. Convolutional Neural Networks (CNN)
145. Recurrent Neural Networks (RNN)
146. You've Achieved 25% >> Let's Celebrate Your Progress And Keep Going To 50% >>
147. Section Showdown: Student to Star!
148. Natural Language Processing Fundamentals
149. Introduction to NLP
150. Text Preprocessing Techniques
151. Text Tokenization and Word Embeddings
152. Sentiment Analysis with NLP
153. Named Entity Recognition (NER) in NLP
154. Section Showdown: Student to Star!
155. Computer Vision Basics
156. Overview of Computer Vision
157. Image Processing Techniques
158. Feature Extraction in CV
159. Object Detection
160. Image Classification Using CNNs
161. Section Showdown: Student to Star!
162. Reinforcement Learning Concepts
163. Agent-Environment Interaction
164. Markov Decision Processes (MDPs)
165. Q-Learning Algorithm
166. Deep Q Networks (DQN)
167. Applications of Reinforcement Learning
168. Section Showdown: Student to Star!
169. Unleashing the Power of AI in Business
170. AI for Business Process Automation
171. Customer Insights with AI
172. Predictive Analytics in Business
173. AI-driven Decision Making
174. AI Integration in Business Strategies
175. Section Showdown: Student to Star!
176. Implementing AI in Digital Transformation
177. AI for Digital Customer Experience
178. AI in E-commerce
179. Data-driven Marketing Strategies
180. AI-enhanced Digital Operations
181. Success Metrics in Digital Transformation
182. You've Achieved 50% >> Let's Celebrate Your Progress And Keep Going To 75% >>
183. Section Showdown: Student to Star!
184. Case Studies on AI and Business Innovation
185. AI Success Stories in Business
186. AI Failure Cases and Lessons Learned
187. Impact of AI on Business Models
188. Analysis of AI Implementation in Organizations
189. Best Practices in AI-driven Business Innovation
190. Section Showdown: Student to Star!
191. Ethical Considerations in AI Adoption
192. AI Bias and Fairness Issues
193. Privacy Concerns in AI
194. Transparency in AI Algorithms
195. Regulatory Frameworks for AI
196. Responsible AI Practices
197. Section Showdown: Student to Star!
198. AI and Machine Learning in Healthcare
199. AI Applications in Healthcare
200. Disease Diagnosis with AI

***** Feedback from Students ********

An excellent introduction to the topic. the lessons flowed logically and the course material was well presented. A very good course and a pleasure to take. Sam D.

"Good course for anyone who wants to make some sense of all the proper terminology and basic methodology of AI. Idan's explanations are very clear and to the point, no fluff and no distractions!" Grace H.

"The course was actually amazing, giving me much more insight into AI. " Patrick A

"best ML course ever. " Parmanand S.

"Good and simple enough to start learning ML." Cogent Systems.

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Machine Learning - Next Terminator is Here...?

The concept of Artificial Intelligence is used in sci-fiction movies to describe a virtual entity that crossed some critical threshold point and developed self-awareness. And like any good Hollywood movie, this entity will turn against humankind. OMG! It’s a great concept to fuel our basic survival fear; otherwise, no one will buy a ticket to the next Terminator movie ;-)

As you may guess, things, in reality, are completely different. Artificial Intelligence is one of the biggest revolutions in the software industry. It is a mind-shift on how to develop software applications. Instead of using hard-coded rules for performing something, we let the machines learn things from data, decipher the complex patterns automatically, and then use it for multiple use cases.

AI-Powered Applications

There are growing amounts of AI-powered applications in a variety of practical use cases. Websites are using AI to better recommend visitors about products and services. The ability to recognize objects in real-time video streams is driven by machine learning. It is a game-changing technology, and the game just started.

Simplifying Things

The concept of AI and ML can be a little bit intimidating for beginners, and specifically for people without a substantial background in complex math and programming. This training is a soft starting point to walk you through the fundamental theoretical concepts.

We are going to open the mysterious AI/ML black box, and take a look inside, get more familiar with the terms being used in the industry. It is going to be a super interesting story. It is important to mention that there are no specific prerequisites for starting this training, and it is designed for absolute beginners.

Would you like to join the upcoming Machine Learning revolution?

1. Getting Started with Level 1!
2. Welcome!
3. Before you start....
4. The Rise of Artificial Intelligence
5. AI is Coming...
6. Artificial Intelligence
7. Classical Programming
8. Machine Learning
9. Deep Learning
10. Applied vs. Generalized AI
11. Why Now?
12. Quick Check-Point #1
13. Introduction to Machine Learning
14. Overview - ML Terminology
15. The “Black Box” Metaphor
16. Features and Labels
17. Training a Model
18. Aiming for Generalization
19. Quick Check-Point #2
20. Classification of ML Systems
21. The Degree of Supervision
22. #1 - Supervised Learning
23. Classification
24. Regression
25. Quick Check-Point #3
26. #2 - Unsupervised Learning
27. Clustering
28. Dimension Reduction
29. Quick Check-Point #4
30. #3 - Reinforcement Learning
31. Decision-Making Agent
32. Quick Check-Point #5
33. **NEW** - The Magic Behind Generative AI
34. Introduction
35. Artificial Neural Networks
36. Deep Learning Architectures
37. Foundation Models
38. Large Language Models (LLMs)
39. Model Types
40. Prompt and Tokens
41. Total Tokens and Context Window
42. Next Token Please!
43. Self-Supervised Learning
44. Improving and Adapting LLMs
45. Summary
46. **NEW** - Generative AI - Key Challenges and Limitations
47. Introduction
48. Prompt Sensitivity
49. Knowledge Cutoff
50. It is not Deterministic
51. Structured Data
52. Hallucinations
53. Lack of Common Sense
54. Bias and Fairness
55. Data Privacy, Security, and Misuse
56. Summary
57. **NEW** - Unleash the Power of Generative AI
58. Introduction
59. Text-Image-Video-Audio Generation
60. Web-Based vs Application-Based (APIs)
61. Use Case - Brainstorm Assistant
62. Use Case - Summarization
63. Use Case – Text Enhancement
64. Use Case - Code Generation
65. Use Case – Content as a Framework
66. Use Case – Images on Demand
67. Use Case – Boosting AI-Based Apps
68. Best Practices for Prompts
69. Summary
70. Course Summary
71. Let's Recap and Thank You!
72. ** BONUS **

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

Over 1 Million students world-wide trust this course.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course can be completed by either doing either the Python tutorials, or R tutorials, or both - Python & R. Pick the programming language that you need for your career.

This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:

• Part 1 - Data Preprocessing

• Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

• Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

• Part 4 - Clustering: K-Means, Hierarchical Clustering

• Part 5 - Association Rule Learning: Apriori, Eclat

• Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

• Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP

• Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

• Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA

• Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And last but not least, this course includes both Python and R code templates which you can download and use on your own projects.

1. Welcome to the course! Here we will help you get started in the best conditions.
2. Welcome Challenge!
3. Machine Learning Demo - Get Excited!
4. Get all the Datasets, Codes and Slides here
5. How to use the ML A-Z folder & Google Colab
6. Installing R and R Studio (Mac, Linux & Windows)
7. EXTRA: Use ChatGPT to Boost your ML Skills
8. -------------------- Part 1: Data Preprocessing --------------------
9. Welcome to Part 1 - Data Preprocessing
10. The Machine Learning process
11. Splitting the data into a Training and Test set
12. Feature Scaling
13. Data Preprocessing in Python
14. Getting Started - Step 1
15. Getting Started - Step 2
16. Importing the Libraries
17. Importing the Dataset - Step 1
18. Importing the Dataset - Step 2
19. Importing the Dataset - Step 3
20. For Python learners, summary of Object-oriented programming: classes & objects
21. Coding Exercise 1: Importing and Preprocessing a Dataset for Machine Learning
22. Taking care of Missing Data - Step 1
23. Taking care of Missing Data - Step 2
24. Coding Exercise 2: Handling Missing Data in a Dataset for Machine Learning
25. Encoding Categorical Data - Step 1
26. Encoding Categorical Data - Step 2
27. Encoding Categorical Data - Step 3
28. Coding Exercise 3: Encoding Categorical Data for Machine Learning
29. Splitting the dataset into the Training set and Test set - Step 1
30. Splitting the dataset into the Training set and Test set - Step 2
31. Splitting the dataset into the Training set and Test set - Step 3
32. Coding Exercise 4: Dataset Splitting and Feature Scaling
33. Feature Scaling - Step 1
34. Feature Scaling - Step 2
35. Feature Scaling - Step 3
36. Feature Scaling - Step 4
37. Coding exercise 5: Feature scaling for Machine Learning
38. Data Preprocessing in R
39. Getting Started
40. Dataset Description
41. Importing the Dataset
42. Taking care of Missing Data
43. Encoding Categorical Data
44. Splitting the dataset into the Training set and Test set - Step 1
45. Splitting the dataset into the Training set and Test set - Step 2
46. Feature Scaling - Step 1
47. Feature Scaling - Step 2
48. Data Preprocessing Template
49. Data Preprocessing Quiz
50. -------------------- Part 2: Regression --------------------
51. Welcome to Part 2 - Regression
52. Simple Linear Regression
53. Simple Linear Regression Intuition
54. Ordinary Least Squares
55. Simple Linear Regression in Python - Step 1a
56. Simple Linear Regression in Python - Step 1b
57. Simple Linear Regression in Python - Step 2a
58. Simple Linear Regression in Python - Step 2b
59. Simple Linear Regression in Python - Step 3
60. Simple Linear Regression in Python - Step 4a
61. Simple Linear Regression in Python - Step 4b
62. Simple Linear Regression in Python - Additional Lecture
63. Simple Linear Regression in R - Step 1
64. Simple Linear Regression in R - Step 2
65. Simple Linear Regression in R - Step 3
66. Simple Linear Regression in R - Step 4a
67. Simple Linear Regression in R - Step 4b
68. Simple Linear Regression in R - Step 4c
69. Simple Linear Regression Quiz
70. Multiple Linear Regression
71. Dataset + Business Problem Description
72. Multiple Linear Regression Intuition
73. Assumptions of Linear Regression
74. Multiple Linear Regression Intuition - Step 3
75. Multiple Linear Regression Intuition - Step 4
76. Understanding the P-Value
77. Multiple Linear Regression Intuition - Step 5
78. Multiple Linear Regression in Python - Step 1a
79. Multiple Linear Regression in Python - Step 1b
80. Multiple Linear Regression in Python - Step 2a
81. Multiple Linear Regression in Python - Step 2b
82. Multiple Linear Regression in Python - Step 3a
83. Multiple Linear Regression in Python - Step 3b
84. Multiple Linear Regression in Python - Step 4a
85. Multiple Linear Regression in Python - Step 4b
86. Multiple Linear Regression in Python - Backward Elimination
87. Multiple Linear Regression in Python - EXTRA CONTENT
88. Multiple Linear Regression in R - Step 1a
89. Multiple Linear Regression in R - Step 1b
90. Multiple Linear Regression in R - Step 2a
91. Multiple Linear Regression in R - Step 2b
92. Multiple Linear Regression in R - Step 3
93. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
94. Multiple Linear Regression in R - Backward Elimination - Homework Solution
95. Multiple Linear Regression in R - Automatic Backward Elimination
96. Multiple Linear Regression Quiz
97. Polynomial Regression
98. Polynomial Regression Intuition
99. Polynomial Regression in Python - Step 1a
100. Polynomial Regression in Python - Step 1b
101. Polynomial Regression in Python - Step 2a
102. Polynomial Regression in Python - Step 2b
103. Polynomial Regression in Python - Step 3a
104. Polynomial Regression in Python - Step 3b
105. Polynomial Regression in Python - Step 4a
106. Polynomial Regression in Python - Step 4b
107. Polynomial Regression in R - Step 1a
108. Polynomial Regression in R - Step 1b
109. Polynomial Regression in R - Step 2a
110. Polynomial Regression in R - Step 2b
111. Polynomial Regression in R - Step 3a
112. Polynomial Regression in R - Step 3b
113. Polynomial Regression in R - Step 3c
114. Polynomial Regression in R - Step 4a
115. Polynomial Regression in R - Step 4b
116. R Regression Template - Step 1
117. R Regression Template - Step 2
118. Polynomial Regression Quiz
119. Support Vector Regression (SVR)
120. SVR Intuition (Updated!)
121. Heads-up on non-linear SVR
122. SVR in Python - Step 1a
123. SVR in Python - Step 1b
124. SVR in Python - Step 2a
125. SVR in Python - Step 2b
126. SVR in Python - Step 2c
127. SVR in Python - Step 3
128. SVR in Python - Step 4
129. SVR in Python - Step 5a
130. SVR in Python - Step 5b
131. SVR in R - Step 1
132. SVR in R - Step 2
133. SVR Quiz
134. Decision Tree Regression
135. Decision Tree Regression Intuition
136. Decision Tree Regression in Python - Step 1a
137. Decision Tree Regression in Python - Step 1b
138. Decision Tree Regression in Python - Step 2
139. Decision Tree Regression in Python - Step 3
140. Decision Tree Regression in Python - Step 4
141. Decision Tree Regression in R - Step 1
142. Decision Tree Regression in R - Step 2
143. Decision Tree Regression in R - Step 3
144. Decision Tree Regression in R - Step 4
145. Decision Tree Regression Quiz
146. Random Forest Regression
147. Random Forest Regression Intuition
148. Random Forest Regression in Python - Step 1
149. Random Forest Regression in Python - Step 2
150. Random Forest Regression in R - Step 1
151. Random Forest Regression in R - Step 2
152. Random Forest Regression in R - Step 3
153. Random Forest Regression Quiz
154. Evaluating Regression Models Performance
155. R-Squared Intuition
156. Adjusted R-Squared Intuition
157. Evaluating Regression Models Performance Quiz
158. Regression Model Selection in Python
159. Make sure you have this Model Selection folder ready
160. Preparation of the Regression Code Templates - Step 1
161. Preparation of the Regression Code Templates - Step 2
162. Preparation of the Regression Code Templates - Step 3
163. Preparation of the Regression Code Templates - Step 4
164. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 1
165. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION! - STEP 2
166. Conclusion of Part 2 - Regression
167. Regression Model Selection in R
168. Evaluating Regression Models Performance - Homework's Final Part
169. Interpreting Linear Regression Coefficients
170. Conclusion of Part 2 - Regression
171. -------------------- Part 3: Classification --------------------
172. Welcome to Part 3 - Classification
173. What is Classification?
174. Logistic Regression
175. Logistic Regression Intuition
176. Maximum Likelihood
177. Logistic Regression in Python - Step 1a
178. Logistic Regression in Python - Step 1b
179. Logistic Regression in Python - Step 2a
180. Logistic Regression in Python - Step 2b
181. Logistic Regression in Python - Step 3a
182. Logistic Regression in Python - Step 3b
183. Logistic Regression in Python - Step 4a
184. Logistic Regression in Python - Step 4b
185. Logistic Regression in Python - Step 5
186. Logistic Regression in Python - Step 6a
187. Logistic Regression in Python - Step 6b
188. Logistic Regression in Python - Step 7a
189. Logistic Regression in Python - Step 7b
190. Logistic Regression in Python - Step 7c
191. Logistic Regression in Python - Step 7 (Colour-blind friendly image)
192. Logistic Regression in R - Step 1
193. Logistic Regression in R - Step 2
194. Logistic Regression in R - Step 3
195. Logistic Regression in R - Step 4
196. Warning - Update
197. Logistic Regression in R - Step 5a
198. Logistic Regression in R - Step 5b
199. Logistic Regression in R - Step 5c
200. Logistic Regression in R - Step 5 (Colour-blind friendly image)

Get certified by Amazon for your knowledge of machine learning on AWS! Prepare to ace one of the most challenging certifications in the cloud domain—the AWS Certified Machine Learning Engineer Associate Exam! Whether you're a backend developer, data engineer, or data scientist, this comprehensive course is your gateway to success.

Why This Course?

This course is expertly crafted by industry veterans Frank Kane and Stephane Maarek, who have collectively educated over 3 million students on Udemy. Frank Kane, with over 9 years of experience at Amazon, has specialized in machine learning and AI, and Stephane Maarek is an AWS expert and renowned instructor. Together, they bring an unparalleled depth of knowledge to guide you through every aspect of the exam.

What You’ll Learn:

• Master AWS ML Services: Dive deep into Amazon SageMaker, Amazon Bedrock, and a host of other AWS services like Comprehend, Rekognition, and Translate, which are crucial for the exam.

• Hands-on Labs: Gain practical experience with hands-on activities, labs, and demos that reinforce your understanding and help you build confidence.

• Practice Exam and Practice Questions: A 20-question practice exam and 110 quiz questions throughout the course test your knowledge, in a style similar to the exam

• Data Preparation & Feature Engineering: Learn how to ingest, transform, and validate data for ML modeling, ensuring data integrity and model readiness.

• Model Development & Deployment: Explore hyperparameter tuning, model performance analysis, and best practices for deploying scalable ML solutions on AWS.

• Monitoring & Security: Discover how to monitor ML models and infrastructure, optimize costs, and secure your AWS environment, ensuring compliance and performance.

Why Choose Us?

• Proven Track Record: Our instructors have helped millions of students achieve their AWS certification goals.

• Real-World Experience: Learn from experts who have worked at Amazon and have extensive experience with AWS services.

• Comprehensive Coverage: This course covers everything you need to pass the exam—from AWS service knowledge to advanced machine learning topics that the exam will test you on.

Who Should Enroll?

This course is perfect for anyone preparing to take the AWS Certified Machine Learning Engineer Associate Exam. If you're serious about your certification and want to ensure you walk into the exam center with confidence, this course is for you.

Don’t Leave Your Success to Chance

This certification is tough, and the stakes are high. Don't risk hundreds of dollars on an exam until you're fully prepared. Enroll now and take the first step towards becoming an AWS Certified Machine Learning Engineer!

Enroll Today and Start Your Journey to Certification Success!

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Instructor

My name is Stéphane Maarek, I am passionate about Cloud Computing, and I will be your instructor in this course. I teach about AWS certifications, focusing on helping my students improve their professional proficiencies in AWS.

I have already taught 2,500,000+ students and gotten 800,000+ reviews throughout my career in designing and delivering these certifications and courses!

With AWS becoming the centerpiece of today's modern IT architectures, I have decided it is time for students to learn how to be an AWS Data Analytics Professional. So, let’s kick start the course! You are in good hands!

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Instructor

Hey, I'm Frank Kane, and I'm also co-instructing this course. I've successfully passed MLA-C01 myself and have ensured everything you need to know is in here. I spent nine years working for Amazon from the inside as a senior engineer and senior manager, and I'm best known for my top-selling courses in "big data", data analytics, machine learning, AI, Apache Spark, system design, and Elasticsearch. I hold 26 issued patents in the field of machine learning.

I've been teaching on Udemy since 2015, where I've reached over 850,000 students all around the world!

I've worked hard to keep this course up to date with the latest developments in AWS machine learning, and to make sure you're prepared for the latest version of this exam. Let's dive in and get you ready!

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This course also comes with:

• Lifetime access to all future updates

• A responsive instructor in the Q&A Section

• Udemy Certificate of Completion Ready for Download

• A 30 Day "No Questions Asked" Money Back Guarantee!

Join us in this course if you want to pass the AWS Certified Machine Learning Engineer Associate MLA-C01 / ME1-C01 exam and master the AWS platform!

1. Introduction
2. Introduction and Course Overview
3. Udemy 101
4. Get the Course Materials and Slides
5. Setting Up an AWS Billing Alarm
6. Data Ingestion and Storage
7. Intro: Data Ingestion and Storage
8. Types of Data
9. Properties of Data (The Three V's)
10. Data Warehouses, Lakes, and Lakehouses
11. Data Mesh
12. ETL & ETL Pipelines and Orchestration
13. Common Data Sources and Data Formats
14. Amazon S3
15. Amazon S3 - Hands On
16. Amazon S3 Security - Bucket Policy
17. Amazon S3 Security - Bucket Policy - Hands On
18. Amazon S3 - Versioning
19. Amazon S3 - Versioning - Hands On
20. Amazon S3 - Replication
21. Amazon S3 - Replication - Notes
22. Amazon S3 - Replication - Hands On
23. Amazon S3 - Storage Classes
24. Amazon S3 - Storage Classes - Hands On
25. Amazon S3 - Lifecycle Rules
26. Amazon S3 - Lifecycle Rules - Hands On
27. Amazon S3 - Event Notifications
28. Amazon S3 - Event Notifications - Hands On
29. Amazon S3 - Performance
30. Amazon S3 - Encryption
31. About DSSE-KMS
32. Amazon S3 - Encryption - Hands On
33. Amazon S3 - Default Encryption
34. Amazon S3 - Access Points
35. Amazon S3 - Object Lambda
36. Amazon EBS
37. Amazon EBS - Hands On
38. Amazon EBS Elastic Volumes
39. Amazon EFS
40. Amazon EFS - Hands On
41. Amazon EFS vs. Amazon EBS
42. Amazon FSx
43. Amazon FSx - Hands On
44. Amazon Kinesis Data Streams
45. Amazon Kinesis Data Streams - Producers
46. Amazon Kinesis Data Streams - Consumers
47. Amazon Kinesis Data Streams - Hands On
48. Amazon Kinesis Data Streams - Enhanced Fan Out
49. Amazon Kinesis Data Streams - Scaling
50. Amazon Kinesis Data Streams - Handling Duplicates
51. Amazon Kinesis Data Streams - Security
52. Amazon Kinesis Data Firehose
53. Kinesis Tuning and Troubleshooting
54. Amazon Managed Service for Apache Flink
55. Kinesis Analytics Costs; RANDOM_CUT_FOREST
56. Amazon MSK
57. Amazon MSK - Connect
58. Amazon MSK - Serverless
59. Amazon Kinesis vs. Amazon MSK
60. Quiz: Data Ingestion and Storage
61. Data Transformation, Integrity, and Feature Engineering
62. Intro: Data Transformation, Integrity, and Feature Engineering
63. Elastic MapReduce (EMR) and Hadoop Overview
64. Apache Spark on EMR
65. Feature Engineering and the Curse of Dimensionality
66. Lab: Preparing Data for TF-IDF with Spark and EMR Studio, Part 1
67. Lab: Preparing Data for TF-IDF with Spark and EMR Studio, Part 2
68. Imputing Missing Data
69. Dealing with Unbalanced Data
70. Handling Outliers
71. Binning, Transforming, Encoding, Scaling, and Shuffling
72. SageMaker Overview
73. Data Processing, Training, and Deployment with SageMaker
74. Amazon SageMaker Ground Truth and Label Generation
75. Amazon Mechanical Turk
76. SageMaker Data Wrangler
77. Demo: SageMaker Studio, Canvas, and Data Wrangler
78. SageMaker Model Monitor and SageMaker Clarify
79. Partial Dependence Plots (PDPs), Shapley values, and SHAP
80. SageMaker Feature Store
81. SageMaker Canvas
82. AWS Glue
83. AWS Glue Studio
84. AWS Glue Data Quality
85. AWS Glue DataBrew
86. Demo: Glue DataBrew
87. Handling PII in DataBrew Transformations
88. Quiz: Data Transformation, Integrity, and Feature Engineering
89. AWS Managed AI Services
90. Intro: AWS Managed AI Services
91. Why AWS Managed Services?
92. Amazon Comprehend
93. Amazon Comprehend - Hands On
94. Amazon Translate
95. Amazon Translate - Hands On
96. Amazon Transcribe
97. Amazon Transcribe - Hands On
98. Amazon Polly
99. Amazon Polly - Hands On
100. Amazon Rekognition
101. Amazon Rekognition - Hands On
102. Amazon Forecast
103. Amazon Lex
104. Amazon Lex - Hands On
105. Amazon Personalize
106. Amazon Textract
107. Amazon Textract - Hands On
108. Amazon Kendra
109. Amazon Augmented AI
110. Amazon Augmented AI - Hands On
111. Amazon's Hardware for AI
112. Amazon's Hardware for AI - Hands On
113. Amazon Lookout
114. Amazon Fraud Detector
115. Amazon Q Business
116. Amazon Q Business - Hands On
117. Amazon Q Apps
118. Amazon Q Apps - Hands On
119. Amazon Q Business - Hands On - Cleanup
120. Amazon Q Developer
121. Amazon Q Developer - Hands On
122. Quiz: AWS Managed AI Services
123. SageMaker Built-In Algorithms
124. Intro: SageMaker Built-In Algorithms
125. Introducing Amazon SageMaker
126. SageMaker Input Modes
127. Linear Learner in SageMaker
128. XGBoost in SageMaker
129. LightGBM in SageMaker
130. Seq2Seq in SageMaker
131. DeepAR in SageMaker
132. BlazingText in SageMaker
133. Object2Vec in SageMaker
134. Object Detection in SageMaker
135. Image Classification in SageMaker
136. Semantic Segmentation in SageMaker
137. Random Cut Forest in SageMaker
138. Neural Topic Model in SageMaker
139. Latent Dirichlet Allocation (LDA) in SageMaker
140. K-Nearest-Neighbors (KNN) in SageMaker
141. K-Means Clustering in SageMaker
142. Principal Component Analysis (PCA) in SageMaker
143. Factorization Machines in SageMaker
144. IP Insights in SageMaker
145. Quiz: SageMaker Built-In Algorithms
146. Model Training, Tuning, and Evaluation
147. Intro: Model Training, Tuning, and Evaluation
148. Introduction to Deep Learning
149. Activation Functions
150. Convolutional Neural Networks
151. Recurrent Neural Networks
152. Tuning Neural Networks
153. Regularization Techniques for Neural Networks (Dropout, Early Stopping)
154. L1 and L2 Regularization
155. The Vanishing Gradient Problem
156. The Confusion Matrix
157. Precision, Recall, F1, AUC, and more
158. RMSE, R-squared, MAE
159. Ensemble Methods: Bagging and Boosting
160. Automatic Model Tuning (AMT) in SageMaker
161. Hyperparameter Tuning in AMT
162. SageMaker Autopilot / AutoML
163. SageMaker Studio, SageMaker Experiments
164. SageMaker Debugger
165. SageMaker Model Registry
166. Analyzing Training Jobs with TensorBoard
167. SageMaker Training at Large Scale: Training Compiler, Warm Pools
168. SageMaker Checkpointing, Cluster Health Checks, Automatic Restarts
169. SageMaker Distributed Training Libraries and Distributed Data Parallelism
170. SageMaker Model Parallelism Library
171. Elastic Fabric Adapter (EFA) and MiCS
172. Quiz: Model Tuning, Training, and Evaluation
173. Generative AI Model Fundamentals
174. Intro: Generative AI Model Fundamentals
175. The Transformer Architecture
176. Self-Attention and Attention-Based Neural Networks
177. Applications of Transformers
178. Generative Pre-Trained Transformers: How they Work, Part 1
179. Generative Pre-Trained Transformers: How they Work, Part 2
180. LLM Key Terms and Controls (tokens, embeddings, temperature, etc.)
181. Fine-Tuning and Transfer Learning with Transformers
182. Lab: Tokenization and Positional Encoding with SageMaker Notebooks
183. Lab: Multi-Headed, Masked Self-Attention in SageMaker
184. Lab: Using GPT within a SageMaker Notebook
185. AWS Foundation Models and SageMaker JumpStart with Generative AI
186. Lab: Using Amazon SageMaker JumpStart with Huggingface
187. Quiz: Generative AI Model Fundamentals
188. Building Generative AI Applications with Bedrock
189. Intro: Building Generative AI Applications with Bedrock
190. Building Generative AI with Amazon Bedrock and Foundation Models
191. Lab: Chat, Text, and Image Foundation Models in the Bedrock Playground
192. Fine-Tuning Custom Models and Continuous Pre-Training with Bedrock
193. Retrieval-Augmented Generation (RAG) Fundamentals with Bedrock
194. Vector Stores and Embeddings with Amazon Bedrock Knowledge Bases
195. Implementing RAG with Amazon Bedrock Knowledge Bases
196. Lab: Building and Querying a RAG System with Amazon Bedrock Knowledge Bases
197. Addendum: New chunking strategies in Bedrock
198. Content Filtering with Amazon Bedrock Guardrails
199. Lab: Building and Testing Guardrails with Amazon Bedrock
200. Building LLM Agents / Agentic AI with Amazon Bedrock Agents

Unlock the Power of Machine Learning & AI: Master the Art of Turning Data into Insight

Discover the Future of Technology with Our Comprehensive Machine Learning & AI Course - Featuring Generative AI, Deep Learning, and Beyond!

In an era where Machine Learning (ML) and Artificial Intelligence (AI) are revolutionizing industries across the globe, understanding how giants like Google, Amazon, and Udemy leverage these technologies to extract meaningful insights from vast data sets is more critical than ever. Whether you're aiming to join the ranks of top-tier AI specialists—with an average salary of $159,000 as reported by Glassdoor—or you're driven by the fascinating challenges this field offers, our course is your gateway to an exciting new career trajectory.

Designed for individuals with programming or scripting backgrounds, this course goes beyond the basics, preparing you to stand out in the competitive tech industry. Our curriculum, enriched with over 145 lectures and 20+ hours of video content, is crafted to provide hands-on experience with Python, guiding you from the fundamentals of statistics to the cutting-edge advancements in generative AI.

Why Choose This Course?

• Updated Content on Generative AI: Dive into the latest in AI with modules on transformers, GPT, ChatGPT, the OpenAI API, Advanced Retrieval Augmented Generation (RAG), LLM agents, langchain, and self-attention based neural networks.

• Real-World Application: Learn through Python code examples based on real-life scenarios, making the abstract concepts of ML and AI tangible and actionable.

• Industry-Relevant Skills: Our curriculum is designed based on the analysis of job listings from top tech firms, ensuring you gain the skills most sought after by employers.

• Diverse Topics Covered: From neural networks, TensorFlow, and Keras to sentiment analysis and image recognition, our course covers a wide range of ML models and techniques, ensuring a well-rounded education.

• Accessible Learning: Complex concepts are explained in plain English, focusing on practical application rather than academic jargon, making the learning process straightforward and engaging.

Course Highlights:

• Introduction to Python and basic statistics, setting a strong foundation for your journey in ML and AI.

• Deep Learning techniques, including MLPs, CNNs, and RNNs, with practical exercises in TensorFlow and Keras.

• Extensive modules on the mechanics of modern generative AI, including transformers and the OpenAI API, with hands-on projects like fine-tuning GPT, Advanced RAG, langchain, and LLM agents.

• A comprehensive overview of machine learning models beyond GenAI, including SVMs, reinforcement learning, decision trees, and more, ensuring you have a broad understanding of the field.

• Practical data science applications, such as data visualization, regression analysis, clustering, and feature engineering, empowering you to tackle real-world data challenges.

• A special section on Apache Spark, enabling you to apply these techniques to big data, analyzed on computing clusters.

No previous Python experience? No problem! We kickstart your journey with a Python crash course to ensure you're well-equipped to tackle the modules that follow.

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Join a community of learners who have successfully transitioned into the tech industry, leveraging the knowledge and skills acquired from our course to excel in corporate and research roles in AI and ML.

"I started doing your course... and it was pivotal in helping me transition into a role where I now solve corporate problems using AI. Your course demystified how to succeed in corporate AI research, making you the most impressive instructor in ML I've encountered." - Kanad Basu, PhD

Are you ready to step into the future of technology and make a mark in the fields of machine learning and artificial intelligence? Enroll now and embark on a journey that transforms data into powerful insights, paving your way to a rewarding career in AI and ML.

1. Getting Started
2. Introduction
3. Udemy 101: Getting the Most From This Course
4. Important note
5. Installation: Getting Started
6. [Activity] WINDOWS: Installing and Using Anaconda & Course Materials
7. [Activity] MAC: Installing and Using Anaconda & Course Materials
8. [Activity] LINUX: Installing and Using Anaconda & Course Materials
9. Python Basics, Part 1 [Optional]
10. [Activity] Python Basics, Part 2 [Optional]
11. [Activity] Python Basics, Part 3 [Optional]
12. [Activity] Python Basics, Part 4 [Optional]
13. Introducing the Pandas Library [Optional]
14. Statistics and Probability Refresher, and Python Practice
15. Types of Data (Numerical, Categorical, Ordinal)
16. Mean, Median, Mode
17. [Activity] Using mean, median, and mode in Python
18. [Activity] Variation and Standard Deviation
19. Probability Density Function; Probability Mass Function
20. Common Data Distributions (Normal, Binomial, Poisson, etc)
21. [Activity] Percentiles and Moments
22. [Activity] A Crash Course in matplotlib
23. [Activity] Advanced Visualization with Seaborn
24. [Activity] Covariance and Correlation
25. [Exercise] Conditional Probability
26. Exercise Solution: Conditional Probability of Purchase by Age
27. Bayes' Theorem
28. Predictive Models
29. [Activity] Linear Regression
30. [Activity] Polynomial Regression
31. [Activity] Multiple Regression, and Predicting Car Prices
32. Multi-Level Models
33. Machine Learning with Python
34. Supervised vs. Unsupervised Learning, and Train/Test
35. [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression
36. Bayesian Methods: Concepts
37. [Activity] Implementing a Spam Classifier with Naive Bayes
38. K-Means Clustering
39. [Activity] Clustering people based on income and age
40. Measuring Entropy
41. [Activity] WINDOWS: Installing Graphviz
42. [Activity] MAC: Installing Graphviz
43. [Activity] LINUX: Installing Graphviz
44. Decision Trees: Concepts
45. [Activity] Decision Trees: Predicting Hiring Decisions
46. Ensemble Learning
47. [Activity] XGBoost
48. Support Vector Machines (SVM) Overview
49. [Activity] Using SVM to cluster people using scikit-learn
50. Recommender Systems
51. User-Based Collaborative Filtering
52. Item-Based Collaborative Filtering
53. [Activity] Finding Movie Similarities using Cosine Similarity
54. [Activity] Improving the Results of Movie Similarities
55. [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering
56. [Exercise] Improve the recommender's results
57. More Data Mining and Machine Learning Techniques
58. K-Nearest-Neighbors: Concepts
59. [Activity] Using KNN to predict a rating for a movie
60. Dimensionality Reduction; Principal Component Analysis (PCA)
61. [Activity] PCA Example with the Iris data set
62. Data Warehousing Overview: ETL and ELT
63. Reinforcement Learning
64. [Activity] Reinforcement Learning & Q-Learning with Gym
65. Understanding a Confusion Matrix
66. Measuring Classifiers (Precision, Recall, F1, ROC, AUC)
67. Dealing with Real-World Data
68. Bias/Variance Tradeoff
69. [Activity] K-Fold Cross-Validation to avoid overfitting
70. Data Cleaning and Normalization
71. [Activity] Cleaning web log data
72. Normalizing numerical data
73. [Activity] Detecting outliers
74. Feature Engineering and the Curse of Dimensionality
75. Imputation Techniques for Missing Data
76. Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
77. Binning, Transforming, Encoding, Scaling, and Shuffling
78. Apache Spark: Machine Learning on Big Data
79. Warning about Java 21+ and Spark 3!
80. Spark installation notes for MacOS and Linux users
81. [Activity] Installing Spark
82. Spark Introduction
83. Spark and the Resilient Distributed Dataset (RDD)
84. Introducing MLLib
85. Introduction to Decision Trees in Spark
86. [Activity] K-Means Clustering in Spark
87. TF / IDF
88. [Activity] Searching Wikipedia with Spark
89. [Activity] Using the Spark DataFrame API for MLLib
90. Experimental Design / ML in the Real World
91. Deploying Models to Real-Time Systems
92. A/B Testing Concepts
93. T-Tests and P-Values
94. [Activity] Hands-on With T-Tests
95. Determining How Long to Run an Experiment
96. A/B Test Gotchas
97. Deep Learning and Neural Networks
98. Deep Learning Pre-Requisites
99. The History of Artificial Neural Networks
100. [Activity] Deep Learning in the Tensorflow Playground
101. Deep Learning Details
102. Introducing Tensorflow
103. [Activity] Using Tensorflow, Part 1
104. [Activity] Using Tensorflow, Part 2
105. [Activity] Introducing Keras
106. [Activity] Using Keras to Predict Political Affiliations
107. Convolutional Neural Networks (CNN's)
108. [Activity] Using CNN's for handwriting recognition
109. Recurrent Neural Networks (RNN's)
110. [Activity] Using a RNN for sentiment analysis
111. [Activity] Transfer Learning
112. Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
113. Deep Learning Regularization with Dropout and Early Stopping
114. The Ethics of Deep Learning
115. Generative Models
116. Variational Auto-Encoders (VAE's) - how they work
117. Variational Auto-Encoders (VAE) - Hands-on with Fashion MNIST
118. Generative Adversarial Networks (GAN's) - How they work
119. Generative Adversarial Networks (GAN's) - Playing with some demos
120. Generative Adversarial Networks (GAN's) - Hands-on with Fashion MNIST
121. Learning More about Deep Learning
122. Generative AI: GPT, ChatGPT, Transformers, Self Attention Based Neural Networks
123. The Transformer Architecture (encoders, decoders, and self-attention.)
124. Self-Attention, Masked Self-Attention, and Multi-Headed Self Attention in depth
125. Applications of Transformers (GPT)
126. How GPT Works, Part 1: The GPT Transformer Architecture
127. How GPT Works, Part 2: Tokenization, Positional Encoding, Embedding
128. Fine Tuning / Transfer Learning with Transformers
129. [Activity] Tokenization with Google CoLab and HuggingFace
130. [Activity] Positional Encoding
131. [Activity] Masked, Multi-Headed Self Attention with BERT, BERTViz, and exBERT
132. [Activity] Using small and large GPT models within Google CoLab and HuggingFace
133. [Activity] Fine Tuning GPT with the IMDb dataset
134. From GPT to ChatGPT: Deep Reinforcement Learning, Proximal Policy Gradients
135. From GPT to ChatGPT: Reinforcement Learning from Human Feedback and Moderation
136. The OpenAI API (Developing with GPT and ChatGPT)
137. [Activity] The OpenAI Chat Completions API
138. [Activity] Using Tools and Functions in the OpenAI Chat Completion API
139. [Activity] The Images (DALL-E) API in OpenAI
140. [Activity] The Embeddings API in OpenAI: Finding similarities between words
141. The Legacy Fine-Tuning API for GPT Models in OpenAI
142. [Demo] Fine-Tuning OpenAI's Davinci Model to simulate Data from Star Trek
143. The New OpenAI Fine-Tuning API; Fine-Tuning GPT-3.5 to simulate Commander Data!
144. [Activity] The OpenAI Moderation API
145. [Activity] The OpenAI Audio API (speech to text)
146. Retrieval Augmented Generation (RAG,) Advanced RAG, and LLM Agents
147. Retrieval Augmented Generation (RAG): How it works, with some examples.
148. Demo: Using Retrieval Augmented Generation (RAG) to simulate Data from Star Trek
149. RAG Metrics: The RAG Triad, relevancy, recall, precision, accuracy, and more
150. [Activity] Evaluating our RAG-based Cdr. Data using RAGAS and langchain
151. Advanced RAG: Pre-Retrieval; chunking; semantic chunking; data extraction.
152. Advanced RAG: Query Rewriting
153. Advanced RAG: Prompt Compression, and More Tuning Opportunities
154. [Activity] Simulating Cdr. Data with Advanced RAG and langchain
155. LLM Agents and Swarms of Agents
156. [Activity] Building a Cdr. Data chatbot with LLM Agents, web search & math tools
157. Final Project
158. Your final project assignment: Mammogram Classification
159. Final project review
160. You made it!
161. More to Explore
162. Don't Forget to Leave a Rating!
163. Bonus Lecture