Machine Learning using Python Programming

Course Curriculum

Chapter 1: Introduction to Machine Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: 2. Features of Machine Learning

Lecture 3: 3. Traditional Programming vs Machine Learning

Chapter 2: Types of Machine Learning

Lecture 1: 4. Difference between Supervised and Unsupervised Learning

Lecture 2: 5. Algorithms in Supervised and Unsupervised Learning

Chapter 3: The Machine Learning Pipeline

Lecture 1: 6. The Machine Learning Pipeline – Data Collection

Lecture 2: 7. Importance of Data Prepocessing

Lecture 3: 8. Importance of Feature Selection and Feature Engineering

Lecture 4: 9. The Machine Learning Terminologies

Lecture 5: 10. Introduction to iPython Environment

Lecture 6: Important Libraries in Python

Chapter 4: Numpy Library

Lecture 1: 11.Creating a numpy array

Lecture 2: 12. Processing the numpy arrays

Lecture 3: 13. Accessing Columns from Numpy Matrices

Lecture 4: 14. Statistical methods in Numpy

Lecture 5: 15. Matrix Operations in Numpy

Lecture 6: 16. Iterating through the numpy array

Chapter 5: Pandas Library

Lecture 1: 17. An Intuition on Pandas Dataframe and Series

Lecture 2: 18. Using numpy arrays to create Pandas Series

Lecture 3: 19. Using dictionary to create Pandas Series

Lecture 4: 20. Using a scalar to create Pandas Series

Lecture 5: 21. Series Processing

Lecture 6: 22. Creating Pandas Dataframe from series

Lecture 7: 23. Using lists of data to create a Pandas Dataframe

Lecture 8: 24. Another approach to create Dataframes

Lecture 9: 25. Directly creating a pandas dataframe from numpy arrays

Chapter 6: Analysis of Datasets using Pandas and Matplotlib Library

Lecture 1: 26. Loading the dataset (Important)

Lecture 2: 27. Analysis of Datasets – I

Lecture 3: 28. Analysis of Datasets by Plotting – II

Chapter 7: The Scikit-learn Library and Preprocessing Techniques

Lecture 1: 29. Working with Iris Dataset from sklearn

Lecture 2: 30. Binarization

Lecture 3: 31. Feature Scaling

Chapter 8: Supervised Learning – Linear Regression

Lecture 1: 32. Analysis of Linear Regression

Lecture 2: Use of Gradient Descent Optimizer

Lecture 3: The Gradient Descent Optimizer Algorithm

Lecture 4: 33. Demand vs Price Problem to understand Linear Regression

Lecture 5: 34. Implementation of Linear Regression – I

Lecture 6: 35. Implementation of Linear Regression – II

Lecture 7: 36. Visualizing the LBF using matplotlib

Chapter 9: Logistic Regression for Classification Problems

Lecture 1: 37. Why does Linear Regression fail for a classification problem?

Lecture 2: 38. The Sigmoid function in Logistic Regression

Lecture 3: 39. The Confusion Matrix

Lecture 4: 40. Implementation of Logistic Regression – I

Lecture 5: 41. Creating an heatmap of the confusion matrix

Chapter 10: Support Vector Machines

Lecture 1: 42. Understanding Support Vector Machines and Hyperplanes

Lecture 2: 43. Understanding the Kernels of SVM

Lecture 3: 44. Implementing Support Vector Classifiers in Python

Chapter 11: K – Nearest Neighbors for Classification and Regression

Lecture 1: 45. Drawing the classification diagrams

Lecture 2: 46. Introduction to K-Nearest Neighbors

Lecture 3: 47. Steps in KNN Classification and KNN Regression

Lecture 4: 48. Implementing KNN Classification using sklearn

Lecture 5: 49. Implementing KNN Regression Algorithm in Python – I

Lecture 6: 50. Implementing KNN Regression Algorithm in Python – II

Chapter 12: Decision Tree Classifier Algorithm

Lecture 1: 51. Introduction to Decision Trees

Lecture 2: 52. Basic Tree Terminologies

Lecture 3: 53. Example 1 for Decision Tree

Lecture 4: 53.1 Example 2 for Decision Tree

Lecture 5: 54. Implementation of Decision Tree Algorithm – I

Lecture 6: 55. Implementation of Decision Tree Algorithm – II

Chapter 13: Random Forest Classifier Algorithm

Lecture 1: 56. Ensemble Techniques – Random Forest Classifier

Lecture 2: 57 . Implementing Random Classifier in Python

Chapter 14: Naive Bayes' Algorithm

Lecture 1: 59. Naive Bayes' Classifier

Lecture 2: 60. Implementing Naive Bayes' Classifier for wine dataset

Chapter 15: Resources

Lecture 1: Download all the notebooks and datasets here!

Chapter 16: K-Means Clustering

Lecture 1: The complete flow of K-Means Clustering

Lecture 2: The concept of Overfitting and Underfitting