1. Introduction
   1.1 The origins of machin learning
   1.2 Machin learning and Human learning
   1.3 Types of machin learning
   1.4 Choosing an appropriate learning algorithem
   


2. Understanding data in Python
   2.1 Data types
   2.2 Data structure
   2.3 Preparation,Cleaning,Normalizing
   2.4 Visualization
   2.5 Finding missing data
   2.6 Replacing missing data
   


3. Probabilistic learning in Python
   3.1 Probability,Joint Probability,Conditional Probability
   3.2 Different Probability distribution and their plots
   3.3 The naive bayes algorithm
   3.4 Markov model
   3.5 Hidden markov model
   3.6 kalman filter algorithm


4. Unsupervised Learning in Python
   4.1 Hierarchical clustering
   4.2 K means clustering
   4.2 Self organizing maps



5. Supervised Learning in Python
   5.1 Regression analysis and Model building
   5.2 Logistic regression
   5.3 K nearest neighbor algorithm
   5.4 Artificial neural network
   5.5 Back propagation algorithm
   5.6 Support vector machine



6. Deep Learning in Python
   6.1 Convolutional neural network
   6.2 multilayer neural network
   6.3 Training and testing using deep learning algorithms

ELIGIBILITY

COURSE FEE AND OTHER DETAILS

The course duration for all above modules is 4 weeks (5 days in a week).

Daily routine for a 3 hour session,one hour lecturing and two hour hands on practice