COURSE NAME
Data Science & Machine Learning Complete
1. Data Science & Machine Learning Complete Course 
Introduction To Python
Conditions and Loops
Patterns
More on Loops
Functions
Tuples, Dictionary And Sets
Object Oriented Programming Systems(OOPs)
Pandas
Plotting Graphs
Structured Query Language(SQL) - Basic
Structured Query Language(SQL) - Advance
Indexing And SQLite
Application Programming Interfaces(APIs) - II
Web Scraping - BeautifulSoup
Web Scraping - Selenium
Web Scraping : Advanced Selenium
Statistics
Descriptive Statistics
Introduction to Machine Learning
Linear Regression
MultiVariable Regression And Gradient Descent
Feature Scaling
Logistic Regression
Classification Measures
Decision Trees - I
Decision Trees - II
Random Forests
Naive Bayes
Project: Text Classification
K-Nearest Neighbours(K-NN)
Support Vector Machine(SVM)
Principal Component Analysis(PCA) - I
Principal Component Analysis(PCA) - II
Natural Language Processing(NLP) - I
Natural Language Processing(NLP) - II
Neural Networks - I
Neural Networks - II
Tensor Flow
Keras
Convolutional Neural Network(CNN) - I
Convolutional Neural Network(CNN) - II
Recurrent Neural Network(RNN)
Long Short-Term Memory(LSTM)
Unsupervised Learning - I
Unsupervised Learning - II
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
