COURSE OBJECTIVE

Machine learning (ML) is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.

Machine learning is the ability to reflex and improve the performance by its experience without being explicitly programmed. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

This course provides a broad introduction to machine learning, natural language processing, reinforcement learning, deep learning among many others.

COURSE OUTCOME

• Make accurate predictions and powerful data analysis
• Make robust Machine Learning models
• Handle specific topics like Reinforcement Learning, NLP and Deep Learning
• Know which Machine Learning model to choose for each type of problem
• Build an army of powerful Machine Learning models and know how to combine them to solve any problem

COURSE TOPICS

• Introduction
• Basics of Python
• Linear Regression
• Feature Extraction
• Regression Training and Testing
• Tutorial Problems
• Regression Forecasting and Predicting
• Exercise Problems
• Trouble shooting and problem-solving approach
• Practical applications of linear regression

APPLICATIONS OF MACHINE LEARNING

• Virtual Personal Assistants
• Predictions while Commuting
• Videos Surveillance
• Social Media Services
• Email Spam and Malware Filtering
• Online Customer Support
• Search Engine Result Refining
• Exercise Problems
• Product Recommendations