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Master in Data Science & Machine Learning 2024 - Basic to Advanced

1. Introduction

1. Course Outline (5:59)
2. Join Our Online Classroom!
3. Exercise Meet The Community
4. Your First Day (3:48)

2. Machine Learning 101

1. What Is Machine Learning (6:52)
2. AIMachine LearningData Science (4:51)
3. Exercise Machine Learning Playground (6:16)
4. How Did We Get Here (6:03)
5. Exercise YouTube Recommendation Engine (4:24)
6. Types of Machine Learning (4:41)
7. Are You Getting It Yet
8. What Is Machine Learning Round 2 (4:44)
9. Section Review (1:48)
10. Monthly Coding Challenges, Free Resources and Guides

3. Machine Learning and Data Science Framework

2. Introducing Our Framework (2:38)
3. 6 Step Machine Learning Framework (4:58)
3.1 A 6 Step Field Guide for Machine Learning Modelling (blog post)
4. Types of Machine Learning Problems (10:32)
5. Types of Data (4:50)
6. Types of Evaluation (3:31)
7. Features In Data (5:22)
8. Modelling - Splitting Data (5:58)
9. Modelling - Picking the Model (4:35)
10. Modelling - Tuning (3:17)
11. Modelling - Comparison (9:32)
12. Overfitting and Underfitting Definitions
13. Experimentation (3:35)
14. Tools We Will Use (3:59)
15. Optional Elements of AI

4. The 2 Paths

1. The 2 Paths (3:27)
2. Python + Machine Learning Monthly
3. Endorsements On LinkedIN

6. Pandas Data Analysis

1. Section Overview (2:27)
2. Downloading Workbooks and Assignments
3. Pandas Introduction (4:29)
4. Series, Data Frames and CSVs (13:21)
5. Data from URLs
6. Describing Data with Pandas (9:48)
7. Selecting and Viewing Data with Pandas (11:08)
8. Selecting and Viewing Data with Pandas Part 2 (13:06)
9. Manipulating Data (13:56)
10. Manipulating Data 2 (9:56)
11. Manipulating Data 3 (10:12)
12. Assignment Pandas Practice
13. How To Download The Course Assignments (7:43)

7. NumPy

1. Section Overview (2:40)
2. NumPy Introduction (5:17)
3. Quick Note Correction In Next Video
4. NumPy DataTypes and Attributes (14:05)
5. Creating NumPy Arrays (9:22)
6. NumPy Random Seed (7:17)
7. Viewing Arrays and Matrices (9:35)
8. Manipulating Arrays (11:31)
9. Manipulating Arrays 2 (9:44)
10. Standard Deviation and Variance (7:10)
11. Reshape and Transpose (7:26)
12. Dot Product vs Element Wise (11:45)
13. Exercise Nut Butter Store Sales (13:04)
14. Comparison Operators (3:33)
15. Sorting Arrays (6:19)
16. Turn Images Into NumPy Arrays (7:37)
17. Assignment NumPy Practice
18. Optional Extra NumPy resources

8. Matplotlib Plotting and Data Visualization

1. Section Overview (1:50)
2. Matplotlib Introduction (5:16)
3. Importing And Using Matplotlib (11:36)
4. Anatomy Of A Matplotlib Figure (9:19)
5. Scatter Plot And Bar Plot (10:09)
6. Histograms And Subplots (8:40)
7. Subplots Option 2 (4:15)
8. Quick Tip Data Visualizations (1:48)
9. Plotting From Pandas DataFrames (5:58)
10. Quick Note Regular Expressions
11. Plotting From Pandas DataFrames 2 (10:33)
12. Plotting from Pandas DataFrames 3 (8:32)
13. Plotting from Pandas DataFrames 4 (6:36)
14. Plotting from Pandas DataFrames 5 (8:28)
15. Plotting from Pandas DataFrames 6 (8:27)
16. Plotting from Pandas DataFrames 7 (11:20)
17. Customizing Your Plots (10:09)
18. Customizing Your Plots 2 (9:41)
19. Saving And Sharing Your Plots (4:14)
20. Assignment Matplotlib Practice

9. Scikit-learn Creating Machine Learning Models

1. Section Overview (2:29)
2. Scikit-learn Introduction (6:41)
3. Quick Note Upcoming Video
4. Refresher What Is Machine Learning (5:40)
5. Quick Note Upcoming Videos
6. Scikit-learn Cheatsheet (6:12)
7. Typical scikit-learn Workflow (23:14)
8. Optional Debugging Warnings In Jupyter (18:57)
9. Getting Your Data Ready Splitting Your Data (8:37)
10. Quick Tip Clean, Transform, Reduce (5:03)
11. Getting Your Data Ready Convert Data To Numbers (16:54)
12. Getting Your Data Ready Handling Missing Values With Pandas (12:22)
13. Extension Feature Scaling
14. Note Correction in the upcoming video (splitting data)
15. Getting Your Data Ready Handling Missing Values With Scikit-learn (17:29)
16. Choosing The Right Model For Your Data (14:54)
17. Choosing The Right Model For Your Data 2 (Regression) (8:41)
18. Quick Note Decision Trees
19. Quick Tip How ML Algorithms Work (1:25)
20. Choosing The Right Model For Your Data 3 (Classification) (12:45)
21. Fitting A Model To The Data (6:45)
22. Making Predictions With Our Model (8:24)
23. predict() vs predict_proba() (8:33)
24. Making Predictions With Our Model (Regression) (6:49)
25. Evaluating A Machine Learning Model (Score) (8:57)
26. Evaluating A Machine Learning Model 2 (Cross Validation) (13:15)
27. Evaluating A Classification Model 1 (Accuracy) (4:46)
28. Evaluating A Classification Model 2 (ROC Curve) (9:04)
29. Evaluating A Classification Model 3 (ROC Curve) (7:44)
30. Reading Extension ROC Curve + AUC
31. Evaluating A Classification Model 4 (Confusion Matrix) (11:01)
32. Evaluating A Classification Model 5 (Confusion Matrix) (8:07)
33. Evaluating A Classification Model 6 (Classification Report) (10:16)
34. Evaluating A Regression Model 1 (R2 Score) (9:12)
35. Evaluating A Regression Model 2 (MAE) (4:17)
36. Evaluating A Regression Model 3 (MSE) (6:34)
37. Machine Learning Model Evaluation
38. Evaluating A Model With Cross Validation and Scoring Parameter (14:04)
39. Evaluating A Model With Scikit-learn Functions (12:14)
40. Improving A Machine Learning Model (11:16)
41. Tuning Hyperparameters (23:15)
42. Tuning Hyperparameters 2 (14:23)
43. Tuning Hyperparameters 3 (14:59)
44. Note Metric Comparison Improvement
45. Quick Tip Correlation Analysis (2:28)
46. Saving And Loading A Model (7:28)
47. Saving And Loading A Model 2 (6:20)
48. Putting It All Together (20:19)
49. Putting It All Together 2 (11:34)
50. Scikit-Learn Practice

11. Milestone Project 1 Supervised Learning (Classification)

1. Section Overview (2:09)
2. Project Overview (6:09)
3. Project Environment Setup (10:58)
4. Optional Windows Project Environment Setup (4:52)
5. Step 1~4 Framework Setup (12:06)
6. Getting Our Tools Ready (9:04)
7. Exploring Our Data (8:33)
8. Finding Patterns (10:02)
9. Finding Patterns 2 (16:47)
10. Finding Patterns 3 (13:36)
11. Preparing Our Data For Machine Learning (8:51)
12. Choosing The Right Models (10:15)
13. Experimenting With Machine Learning Models (6:31)
14. TuningImproving Our Model (13:49)
15. Tuning Hyperparameters (11:27)
16. Tuning Hyperparameters 2 (11:49)
17. Tuning Hyperparameters 3 (7:06)
18. Quick Note Confusion Matrix Labels
19. Evaluating Our Model (10:59)
20. Evaluating Our Model 2 (5:54)
21. Evaluating Our Model 3 (8:49)
22. Finding The Most Important Features (16:07)
23. Reviewing The Project (9:13)

12. Milestone Project 2 Supervised Learning (Time Series Data)

1. Section Overview (1:07)
2. Project Overview (4:24)
3. Project Environment Setup (10:52)
4. Step 1~4 Framework Setup (8:36)
5. Downloading the data for the next two projects
6. Exploring Our Data (14:16)
7. Exploring Our Data 2 (6:16)
8. Feature Engineering (15:24)
9. Turning Data Into Numbers (15:38)
10. Filling Missing Numerical Values (12:49)
11. Filling Missing Categorical Values (8:27)
12. Fitting A Machine Learning Model (7:16)
13. Splitting Data (10:00)
14. Challenge What's wrong with splitting data after filling it
15. Custom Evaluation Function (11:13)
16. Reducing Data (10:36)
17. RandomizedSearchCV (9:32)
18. Improving Hyperparameters (8:11)
19. Preproccessing Our Data (13:15)
20. Making Predictions (9:17)
21. Feature Importance (13:50)

13. Data Engineering

1. Data Engineering Introduction (3:23)
2. What Is Data (6:42)
3. What Is A Data Engineer (4:20)
4. What Is A Data Engineer 2 (5:35)
5. What Is A Data Engineer 3 (5:03)
6. What Is A Data Engineer 4 (3:22)
7. Types Of Databases (6:50)
8. Quick Note Upcoming Video
9. Optional OLTP Databases (10:54)
10. Optional Learn SQL
11. Hadoop, HDFS and MapReduce (4:22)
12. Apache Spark and Apache Flink (2:07)
13. Kafka and Stream Processing (4:33)

15. Storytelling + Communication How To Present Your Work

1. Section Overview (2:19)
2. Communicating Your Work (3:21)
3. Communicating With Managers (2:58)
4. Communicating With Co-Workers (3:42)
5. Weekend Project Principle (6:32)
6. Communicating With Outside World (3:28)
7. Storytelling (3:05)
8. Communicating and sharing your work Further reading

18. Learn Python Part 2

1. Breaking The Flow (2:34)
2. Conditional Logic (13:17)
3. Indentation In Python (4:38)
4. Truthy vs Falsey (5:17)
5. Ternary Operator (4:14)
6. Short Circuiting (4:02)
7. Logical Operators (6:56)
8. Exercise Logical Operators (7:47)
9. is vs == (7:36)
10. For Loops (7:01)
11. Iterables (6:43)
12. Exercise Tricky Counter (3:23)
13. range() (5:38)
14. enumerate() (4:37)
15. While Loops (6:28)
16. While Loops 2 (5:49)
17. break, continue, pass (4:15)
18. Our First GUI (8:48)
19. DEVELOPER FUNDAMENTALS IV (6:34)
20. Exercise Find Duplicates (3:54)
21. Functions (7:41)
22. Parameters and Arguments (4:24)
23. Default Parameters and Keyword Arguments (5:40)
24. return (13:11)
25. Exercise Tesla
26. Methods vs Functions (4:33)
27. Docstrings (3:47)
28. Clean Code (4:38)
29. args and kwargs (7:56)
30. Exercise Functions (4:18)
31. Scope (3:37)
32. Scope Rules (6:55)
33. global Keyword (6:13)
34. nonlocal Keyword (3:20)
35. Why Do We Need Scope (3:38)
36. Pure Functions (9:23)
37. map() (6:30)
38. filter() (4:23)
39. zip() (3:28)
40. reduce() (7:31)
41. List Comprehensions (8:37)
42. Set Comprehensions (6:26)
43. Exercise Comprehensions (4:36)
44. Python Exam Testing Your Understanding
45. Modules in Python (10:54)
46. Quick Note Upcoming Videos
47. Optional PyCharm (8:19)
48. Packages in Python (10:45)
49. Different Ways To Import (7:03)
50. Next Steps

19. Bonus Learn Advanced Statistics and Mathematics for FREE!

1. Statistics and Mathematics

21. BONUS SECTION

1. Bonus Lecture

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