Artificial Intelligence (AI) and Machine Learning are some of the new fields which provide higher exposure to help the candidate build their carrier as professionals in AI and as Data Scientists. The entire syllabus is designed to enhance the knowledge in algorithms, languages, and statistics. The overall program focuses on;

Module 1 – Pre Program Preparation

• Introduction to Python
• Python for Data Science
• Data Visualisation in Python
• Data Analysis using SQL
• Advanced SQL
• Math for Machine Learning

Module 2 – Statistics and Exploratory Data Analytics

• Analytics Problem Solving
• Inferential Statistics
• Investment Case Study
• Exploratory Data Analysis
• Hypothesis Testing
• Group Project

Module 3 – Machine Learning 1

• Linear Regression
• Linear Regression Assignment
• Investment Case Study
• Naive Bayes
• Logistic Regression
• Model Selection

Module 4 – Machine Learning 2

• Advanced Regression
• Tree Models
• Support Vector Machine (Optional)
• Boosting
• Unsupervised Learning: Principal Component Analysis
• Model Selection – Practical Considerations
• Unsupervised Learning: Clustering
• Telecom Churn Case Study

Module 5- Natural Language Processing

• Semantic Processing
• Building Chatbots with Rasa
• Lexical Processing
• Syntactic Processing
• Syntactic Processing – Assignment

Module 6- Deep Learning

• Introduction to Neural Networks
• Neural Networks – Assignment
• Recurrent Neural Networks
• Convolutional Neural Networks – Industry Applications
• Neural Networks Project – Gesture Recognition

Module 7- Reinforcement Learning

• Classical Reinforcement Learning
• Assignment – Classical Reinforcement Learning
• Deep Reinforcement Learning
• Reinforcement Learning Project

Module 8- Capstone

• Capstone
• Deployment