Machine Learning coursera

1. Supervised and Unsupervised Learning

2. Univariate Linear Regression

3. Multivariate Linear Regression

4. Feature Normalization

5. Polynomial Regression

6. Normal Equation in Linear Regression

7. Logistic Regression

8. Overfitting, Underfitting, and Regularization

9. Regularized Linear Regression and Logistic Regression Cheatsheet

10. Neural Networks

11. Model Selection and Bias(Underfitting)/Variance(Overfitting)

12. Evaluation Metrics for Machine Learning Models

13. Support Vector Machine (SVM) and Kernels

14. Clustering and K-Means Algorithm

15. Dimensionality Reduction and Principle Component Analysis (PCA)

16. Anomaly Detection and Gaussian Distribution

17. Recommender System - Collaborative Filtering

18. Stochastic Gradient Descent and Mini-Batch Gradient Descent

Deep Learning coursera

1. Neural Networks Basics

1. Forward and Backward Propagation in Binary Logistic Regression

2. Forward and Backward Propagation in Neural Networks

3. Activation Functions

4. Initialization of Weights

2. Improving Deep Neural Networks

5. Bias/Variance and Regularization/Dropout

6. Vanishing/Exploding Gradients

7. Gradient Check

8. Mini-Batch Gradient Descent

9. Exponentially Weighted Moving Average

10. Gradient Descent Optimization Algorithms with Momentum, RMSProp, and Adam

11. Hyperparameter Tuning

12. Batch Normalization

3. Structuring Machine Learning Projects

13. Orthogonalization in Machine Learning

14. Setting Metric for Machine Learning

15. Setting Development Set and Test Set

16. Deciding Which Way to Prioritize - Avoidable Bias and Variance

17. Error Analysis

18. Data Mismatch of Training Set and Real World Examples

19. Transfer Learning

20. Multi-task Learning

21. End-to-End Deep Learning

4. Convolutional Neural Networks

22. Intro to Convolutional Neural Network

23. Convolution

24. Pooling Layer

25. Classic CNNs - LeNet-5, AlexNet, VGG-16

26. ResNet

27. Inception Network

28. Practical Advices for Using ConvNets

29. Object Localization and Landmark Detection

30. Sliding Windows Detection and Convolutional Wayt ot Implement It

31. YOLO Algorithm

32. Face Recognition

33. Neural Style Transfer

5. Recurrent Neural Networks

34. What is a Recurrent Neural Network?

35. Language Model with RNN

36. GRU(Gated Recurrent Unit) and LSTM(Long Short Term Memory)

37. Bidirectional RNN and Deep RNN

38. Word Embedding

39. Learning Word Embedding - Word2Vec, Negative Sampling, GloVE

40. Sentiment Classification

41. Debiasing Word Embeddings

42. Sequence to Sequence Model

44. Bleu Score

45. Attention Model

46. Speech Recognition and Trigger Word Detection

Applied Data Science with Python coursera

1. Introduction to Data Science in Python

1. Data Processing with Pandas

2. Advanced Python Pandas (Merging, Apply, Groupby, Pivot, Date)

3. Statistical Analysis in Python (Distribution, Hypothesis Testing)

2. Applied Data Plotting in Python

4. Principles of Information Visualization (Visualization Wheel, Data-Ink Ratio, Chart Junk, Lie Factor, Truthful Art)

5. Basic Charting with Matplotlib (Scatterplot, Barchart, Lineplot)

6. Applied Charting with Matplotlib (Subplots, Histogram, Box and Whisker Plot, Heatmap, Animation)

7. Plotting from Pandas and with Seaborn

3. Applied Machine Learning in Python

8. Introduction to Machine Learning

9. Supervised Learning

10. Supervised Learning with scikit-learn

11. Unsupervised Learning with scikit-learn

4. Applied Text Mining in Python

12. Working with text in Python

13. Regular Expressions (Regex)

14. Basic Natural Language Processing

15. Classification of Text

16. Topic Modeling

5. Applied Social Network Analysis in Python

17. Intro to Networks and Basics on NetworkX

18. Network Connectivity

19. Influence Measures and Network Centrality