Eligibility Criteria for AI Bootcamp

9 months AI Bootcamp Program

Syllabus

Week 01

  • Introduction to Python & Google Colaboratory
  • Integers, Floats and Booleans
  • Introduction to Strings

Week 02

  • Introduction to Lists
  • Tuples, Sets and Dictionaries
  • Challenge: Python Basics - I

Week 03

  • Statements, Indentation and Conditionals
  • Loops and Iterations
  • List Comprehension

Week 04

  • Dream Premier League
  • Functions and Methods
  • Production Grade Programming

Week 05

  • Competitive Coding - I
  • Intersection of Lists
  • Space & Time Complexity

Week 06

  • Challenge: Python Basics - II
  • Introduction to Numpy
  • Numpy Operations

Week 07

  • Introduction to Pandas
  • Pandas Operations
  • Introduction to Data Visualization

Week 08

  • Data Wrangling
  • Data Wrangling on IMDB Dataset
  • Challenge: Data Analysis

Week 09

  • Overall Python Competency Test
  • SQL Fundamentals
  • SQL Basic Operations

Week 10

  • SQL Core Operations
  • Hands-on SQL Practice - I
  • Hands-on SQL Practice - II

Week 11

  • Data Analysis using SQL
  • Challenge - SQL Fundamentals
  • Getting Started with Tableau

Week 12

  • Visualizing Data with Tableau
  • Introduction to Business KPIs
  • Formulas & Functions in Excel

Week 13

  • Visualizing Data with Excel
  • Challenge - Excel Fundamentals
  • Overall Analytics Framework Competency Test

Week 14

  • Calculus for ML
  • Vector Algebra
  • Matrix Algebra

Week 15

  • Guided Project: Gradient Descent
  • Challenge: Math Fundamentals
  • Probability Theory

Week 16

  • Summarizing Data
  • Random Variables
  • Discrete Distributions

Week 17

  • Continuous Distributions
  • Joint Distributions
  • Advanced Joint Distributions

Week 18

  • Monte Carlo Simulation
  • Challenge: Descriptive Statistics
  • Sampling and Statistical Inference

Week 19

  • Confidence Intervals
  • Introduction to Hypothesis Testing
  • Hypothesis Testing Implementation

Week 20

  • A/B Testing
  • Challenge: Inferential Statistics
  • Overall Mathematics Competency Test

Week 21

  • Intro to ML
  • Linear Regression
  • Practical Linear Regression

Week 22

  • Bias - Variance Tradeoff
  • Regularized Linear Regression
  • Cross Validation and Hyperparameter Tuning

Week 23

  • Regression Analysis
  • Challenge: Supervised Learning Algorithms - I
  • Capstone Project: Supervised ML - Regression

Week 24

  • Logistic Regression
  • Decision Tree
  • Ensemble of Decision Trees

Week 25

  • Introduction to Model Explainability
  • Insurance Decisioning
  • Challenge: Supervised Learning Algorithms - II

Week 26

  • k-Nearest Neighbors
  • Naive Bayes Classifier
  • Support Vector Machines

Week 27

  • Neural Networks
  • Handling Class Imbalance
  • Anomaly Detection

Week 28

  • General Modeling Techniques
  • Principal Component Analysis
  • Challenge: Supervised Learning Algorithms - III

Week 29

  • Capstone Project: Supervised ML - Classification
  • K-Means Clustering
  • Hierarchical Clustering

Week 30

  • Clustering Analysis
  • Challenge: Unsupervised Learning Algorithms
  • Introduction to NLP

Week 31

  • Topic Modeling
  • Collaborative-Filtering Recommender Systems
  • Recommender Systems - Content Based Filtering
  • Introduction to Time Series Analysis
  • Modeling a Time Series Problem
  • Challenge - Advanced Machine Learning
  • End Course Assessment - Machine Learning

Week 32 (Introduction to Deep Learning)

  • Introduction to Neural Networks
  • Why Neural Networks?
  • Non Linear over Linear Functions
  • Building ANN using Tensorflow
  • Deep Neural Networks
  • Skill Mastery Challenge : Getting Started with Deep Learning

Week 33

  • How to Improve Neural Networks
  • Optimization Algorithms
  • Hyperparameter Tuning
  • Skill Mastery Challenge: Optimization Strategies in Neural Networks
  • Mid Course Quiz Assessment

Week 34

  • Introduction to ML Strategy
  • Error Analysis
  • User Conversion - Case Study
  • Skill Mastery Challenge: Structuring Machine Learning Projects
  • End Course Quiz Assessment

Week 35 (Deep Learning for Computer Vision)

  • Image Processing
  • Introduction to Computer Vision
  • Maths Behind Computer Vision
  • Introduction to OpenCV
  • Computer Vision Implementation
  • Skill Mastery Challenge: Convolutional Neural Networks
  • Getting Inspired from Existing Models
  • Object Localization
  • Implementing YOLO V4
  • Skill Mastery Challenge: Transfer Learning
  • Mid Course Assessment: Deep Learning for CV
  • Getting Started with Face Recognition
  • Neural Style Transfer
  • Face Recognition Implementation
  • Skill Mastery Challenge: Face Recognition
  • End Course Quiz Assessment: Deep Learning for CV

Week 36 (Deep Learning for NLP)

  • Introduction to Topic Modeling
  • Introduction to Recurrent Neural Networks
  • Language Modelling
  • Natural Language Processing and Word Embeddings
  • Learning Word Embeddings
  • Implementing RNN
  • Skill Mastery Challenge: Recurrent Neural Networks
  • Mid Course Quiz Assessment : Deep Learning for NLP
  • Sequence Models
  • Attention Models
  • Transformers & BERT
  • Skill Mastery Challenge: Advanced RNN Models
  • Mastering LLM Trainings
  • The Art of Fine-Tuning
  • Ethics, Fairness, and Transparency in LLMs
  • Skill Mastery Challenge: Large Language Modelling
  • End Course Quiz Assessment: Deep Learning for NLP

Week 37, 38, 39

  • Overall revision of all topics
  • Doubt clearing session

Benefits of the AI Bootcamp Program