COURSE NAME
Machine Learning & Deep Learning Course
1. Machine Learning Course 
Python Basics
Conditionals, Loops and Functions
Lists and Dictionaries
2D Lists and Numpy
Pandas
Plotting Graphs
Introduction to ML
Linear Regression
MultiVariable Regression and Gradient Descent
Logistic Regression
Classification Measures
Decision Trees - 1
Decision Trees - 2
Feature Scaling
Random Forests
Naive Bayes
Project: Text Classification
KNN
SVM
PCA
PCA - 2
NLP - 1
NLP - 2
Git
Neural Networks - 1
Neural Networks - 2
Tensor Flow
Keras
CNN - 1
CNN - 2
RNN
LSTM
Unsupervised Learning - 1
Unsupervised Learning - 2
Git
Topics
SVM Cost Function
Decision Boundary & the C Parameter Using SVM from Sklearn
Using SVM from Sklearn
Finding Non Linear Decision Boundary
Choosing Landmark Points
Various Similarity Functions
More about Gaussian Similarity Function
How to move to new Dimensions
Mulitclass Classification
Using Sklearn SVM on Iris
Choosing Parameters Using Grid Search
