AI ML Program
- Introduction to AI/ML
- Topics and Applications
- Course Overview
The root of AI and Machine Learning: Statistics
- Introduction to Statistics
- Descriptive Statistics
- Probability and Conditional Probability
- Correlation between Variables
- Statistical Hypothesis Tests
- Inferential Statistics
- Probability Distributions
- EDA: Exploratory Data Analysis
Machine Learning: Supervised learning
- Linear Regression
- Multiple Variable Linear Regression
- Logistic Regression
- Decision Trees
- Naive Bayes Classifiers
- k-NN Classification
- Support Vector Machines
- Model Evaluation and Case Studies
Unsupervised learning
- K-means Clustering
- Hierarchical Clustering
- Dimension Reduction-PCA
Ensemble Techniques
- Bagging
- Random Forests
- Boosting
Recommendation Systems
- Introduction to Recommendation Systems
- Popularity based model
- Content-based Recommendation System
- Collaborative Filtering (User similarity & Item similarity)
- Hybrid Models
Artificial Intelligence: Introduction to Neural Networks and Deep Learning
- Introduction to Perceptron & Neural Networks
- Activation and Loss functions
- Gradient Descent
- Batch Normalization
- TensorFlow & Keras for Neural Networks
- Hyper Parameter Tuning
Sequential Models and NLP
- Introduction to Sequential data
- RNNs and its mechanisms
- Vanishing & Exploding gradients in RNNs
- LSTMs - Long short-term memory
- GRUs - Gated recurrent unit
- LSTMs Applications
- Time series analysis
- LSTMs with an attention mechanism
- Neural Machine Translation
Computer vision
- Introduction to Convolutional Neural Networks
- Convolution, Pooling, Padding & its mechanisms
- Forward Propagation & Backpropagation for CNN's
- CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
- Transfer Learning
Advanced Computer Vision
- Object Detection
- YOLO, R-CNN, SSD
- Semantic Segmentation
- U-Net
- Face Recognition using Siamese Networks
NLP Basics(Natural Language Processing)
- Introduction to NLP
- Stop Words
- Tokenization
- Stemming and lemmatization
- Bag of Words Model
- Word Vectorizer
- TF-IDF
- POS Tagging
- Named Entity Recognition
Introduction to GANs (Generative adversarial networks)
- Introduction to GANs
- Generative Networks
- Adversarial Networks
- How GANs work?
- DCGANs - Deep Convolution GANs
- Applications of GANs
Introduction to Reinforcement Learning (RL)
- RL Framework
- Component of RL Framework
- Examples of RL Systems
- Types of RL Systems
- Q-learning
Focus on One Programming Language: R or Python
- Introduction to Python
- Python for Data Science
- Data Visualization in Python
Optional: Optimization Module
- Basics of optimization
- Linear Programming
- Nonlinear Programming
- Integer Programming
