1.Sentiment analysis
2.Logistic regression
3.Data pre-processing
4.Calculating word frequencies
5.Feature extraction
6.Vocabulary creation
7.Supervised learning
8.Error analysis
9.Naive Bayes inference
10.Log likelihood
11.Laplacian smoothing
12.Tokenization/Stop words/Stemming/Lematization
13.Bayes rule
14.Vocabulary creation
1.Regression Algorithms.
2.Classification Algorithms.
2.Decision Trees
3.Random Forests
4.Boosting Algorithms.
5.Numpy,Pandas,Sci-kit,Matplotlib,Tensorflow.
6.Gridsearch
7.Hyperparameter Tuning
8.Cross-Validation
9.Evaluation Metrics
1.Tensor Operations
2.Neural Networks for Regression
3.Activation function,Optimizers and Multi-class Classification.
4.CNN
5.Basics of forward and back propogation.