Example Image with Text
Use this Image with Text block to balance out your text content with a complementary visual to strengthen messaging and help your students connect with your product, course, or coaching. You can introduce yourself with a profile picture and author bio, showcase a student testimonial with their smiling face, or highlight an experience with a screenshot.
Example Text
Use this Text block to tell your course or coaching’s story.
Write anything from one-liners to detailed paragraphs that tell your visitors more about what you’re selling.
This block - along with other blocks that contain text content - supports various text formatting such as header sizes, font styles, alignment, ordered and unordered lists, hyperlinks and colors.
Example Title
Use this block to showcase testimonials, features, categories, or more. Each column has its own individual text field. You can also leave the text blank to have it display nothing and just showcase an image.
Example Title
Use this block to showcase testimonials, features, categories, or more. Each column has its own individual text field. You can also leave the text blank to have it display nothing and just showcase an image.
Example Title
Use this block to showcase testimonials, features, categories, or more. Each column has its own individual text field. You can also leave the text blank to have it display nothing and just showcase an image.
Example Curriculum
- 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
- 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
- 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)
- 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
- 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
- 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
- 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)
- 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)
- 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)
- 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
Example Image with Text
Use this Image with Text block to balance out your text content with a complementary visual to strengthen messaging and help your students connect with your product, course, or coaching. You can introduce yourself with a profile picture and author bio, showcase a student testimonial with their smiling face, or highlight an experience with a screenshot.
Example Featured Products
Showcase other available courses, bundles, and coaching products you’re selling with the Featured Products block to provide alternatives to visitors who may not be interested in this specific product.