Hands-on on python.
Basic syntax and data structures in python
Introduction to data frames, numpy, pandas and other libraries in python.
Introduction to ML
Types of problems solved by ML.
Real world scenarios where ML is applicable.
Basic Terminology used in ML via a case study.
Building Regression models like Linear Regression, its variants etc.
Evaluating regression models, evaluation metrics, cost function etc.
Introduction to classification techniques.
Logistic Regression, Decision Trees, Random Forests.
Evaluating Classification Models.
Clustering and Similarity techniques
Other clustering Algorithms :
Apriori Algorithm etc
Introduction to Deep Learning
Best Practices while designing Machine Learning Solutions