Python for Machine Learning

Lessons

1. Module 1: Python Exercise on Decision Tree and Linear Regression
2. Module 2: Tutorial I
3. Module 3: Python Exercise on KNN and PCA
4. Module 4: Tutorial II
5. Module 5: Python Exercise on Naive Bayes
6. Module 6: Tutorial III
7. Module 7: Python Exercise on SVM
8. Module 8: Python Exercise on Neural Network
9. Module 9: Tutorial IV
10. Module 10: Python Exercise on K-means Clustering
11. Module 11: Tutorial V
12. Python exercise on linear regression
13. Python exercise on logistic regression
14. Python exercise on decision tree regression
15. How to solve a sample problem in Linear Regression?
16. How to solve problems related to Decision Trees?
17. How to find the entropy of a set and use in decision trees?
18. What is information gain?
19. How do we use K-Neighbors Classifier in Python?
20. How do we use Randomized PCA in Python?
21. How can we do Face recognition using PCA and KNN?
22. What is the curse of dimensionality?
23. What is feature selection?
24. What is feature reduction and PCA? (principal component analysis)
25. How do you calculate the eigenvalues and Eigen vector of a matrix?
26. What is K-NN (K Nearest Neighbour) Classification?
27. How to use the Naive Bayes classifier?
28. What is Naive Bayes Classifier?
29. How is Naive Bayes Classifier relevant in the context of email spam classification?
30. How do we estimate the probabilities using the frequency distribution of probability
31. How do we use Bayes rule
32. What is MAP inference
33. What is Naive Bayes assumption
34. What is Bayesian networks (the structures), inference and marginalization?
35. Support vector classification
36. Visualize the decision boundaries
37. Load data
38. How can we create a artificial neural network using TensorFlow and TFLearn to recognize handwritten digits?
39. How do we Load dependencies (to recognize handwritten digits)?
40. How do we Load the data (to recognize handwritten digits)?
41. How do we make the model (to recognize handwritten digits)?
42. How do we train the model (to recognize handwritten digits)?
43. What is our takeaway from this exercise (to recognize handwritten digits)?
44. What is a Perceptron?
45. What is Perceptron learning rule?
46. How do we represent a Boolean function using a Perceptron?
47. What is forward and backward pass algorithm or backpropagation algorithm?
48. Stochastic gradient descent and Batch gradient descent
49. Quick overview of some deep learning algorithms
50. Can we look at python code for K Means algorithm?
51. Can we look at python code for Gaussian mixture model?
52. Hierarchical Agglomerative Clustering
53. What is K-means clustering?
54. Solving a sample problem n K-means clustering?
55. What is Agglomerative Hierarchical clustering?
56. What is Gaussian Mixture Model?