WHAT IS THIS COURSE ABOUT?

Machine learning is the science of making computer machine self learn from the past experiences to improve on the task's outcome. There are many exciting real-life examples of the application of machine learning such as robotic vacuum cleaner,
self-driving cars, face or speech recognition, effective web search and many more. In this course, you will learn important machine learning concepts, types of machine learning algorithms, steps in the model building, testing and scoring
and learn various packages and functionalities in Python. You will get introduced to various supervised and unsupervised learning algorithms, you learn more complex linear, nonlinear as well as ensemble machine learning techniques. Enough time is spent on understanding the concept behind each algorithm
within case studies. What’s more? This course will also introduce deep learning.

WHO SHOULD ATTEND?

This is level two program. If you already familiar with basic math concepts like linear algebra, matrices and stats concepts like probability, estimation, this course would be easier for you to grasp. If you are interested to know more on what machine is learning, how to use machine learning, types of machine learning, and implementation of various algorithms to solve some of the business problems then, you should attend this two days course. This course
expects some Python knowledge. 

PREPARATION

You must bring your own laptop, 64 bit, at least i3 core processor (i5 preferred) with NVIDIA GPU. Instructions on installation of anaconda for Python would be
sent you before the workshop. You need to follow those instructions to make your laptop ready before the class.

COURSE OUTLINE:

Introduction to Machine Learning
•Intro to machine learning
•Types of machine learning algorithms
•Understanding the basics 
• Steps in the model  building, testing and scoring
•Model Evaluation Metrics
Supervised learning algorithms 
•Understanding Linear and non-linear  algorithms
•Understanding Regression and  Classification

•Linear  Algorithms
•Simple linear regression
•Multiple linear regression
•Logistic regression
Non-linear Algorithms 
•k nearest neighbors
•Classification and regression trees
•Naïve byes
•Neural networks

•Ensemble Algorithms 
•Random forest
•Gradient boosting
•Unsupervised learning algorithms 
•Understanding Clustering and Association
*k -means Clustering
*MBA using Word2Vec
•Introduction to Deep Learning