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Course content
Sections:
3
•
Activities:
0
•
Resources:
65
- 1. Introduction to Course
- 2. What is Machine Learning
- 3. Life Cycle
- 4. Introduction to Numpy Library
- 5. Creating Arrays from Scratch
- 6. Creating Arrays from Scratch Continued
- 7. Array Indexing and Slicing
- 8. Numpy Array Functions and Shape Modification
- 9. Mathematical Operations on Numpy Arrays
- 10. Introduction to Pandas Library
- 11. Working with Pandas DataFrames
- 12. Slicing and Indexing with Pandas
- 13. Create DataFrame and Explore Dataset
- 14. Data Analysis with Pandas DataFrame
- 15. Other Useful Methods in Pandas Library
- 16. Introduction to Matplotlib
- 17. Customizing Line Plots
- 18. Create Plot Using DataFrame
- 19. Standard Scaler to Scale the Data
- 20. Encoding Categorical Data
- 21. Sklearn Pipeline and Column Transformer
- 22. Evaluation Metrics in Sklearn
- 23. Linear Regression
- 24. Evaluation of Linear Regression Model
- 25. Polynomial Regression
- 26. Polynomial Regression Continued
- 27. Sklearn Pipeline Polynomial Regression
- 28. Decision Tree Classifier
- 29. Decision Tree Evaluation
- 30. Random Forest
- 31. Support Vector Machines
- 32. Kmeans Clustering
- 33. KMeans Clustering - Hands On
- 34. Data Loading and Analysis
- 35. Dimensionality Reduction with PCA
- 36. Hyper Parameter Tuning
- 37. Summary
- 1. Introduction to Course
- 2. Getting System Ready
- 3. Read and Write Images
- 4. Resize and Crop
- 5. Working with Shapes.
- 6. Working with Text
- 7. Pre-Requisite for Face Detection
- 8. Detect the Face
- 9. Introduction to Deep Learning with Tensorflow
- 10. Model Building
- 11. Training the Mask Detector
- 12. Saving the Best Model
- 13. Basic Front End Design of App
- 14. File Upload Interface for App
- 15. App Prep
- 16. App Build and Testing
- 17. AWS Deployment
- 18. AWS Deployment Continued
- 1. Introduction to Pima Indians Diabetes Using Machine Learning
- 2. Installation of Anaconda
- 3. Installation of Libraries
- 4. Steps in Machine Learning
- 5. Dataset and Logistic Regression
- 6. Pima Classification
- 7. Exclude the Header
- 8. Conversion of String into Number
- 9. Split the Dataset
- 10. Check the ROC