Basic Concepts of Machine Learning and AI

Introduction to Machine Learning (ML) and Artificial Intelligence (AI)

Real-World Applications of ML and AI

Basic Programming Concepts

Natural Language Processing (NLP) Libraries - SpaCy & Gensim

Introduction to OpenCV, TensorFlow, and Keras

Basic Statistics

Sampling & Sampling Statistics

Inferential Statistics: Hypothesis Testing

Calculus

Derivatives and Optimization

Linear Algebra

Functions and Matrices

Scalar, Vector, and Matrix Operations

Probability and Probability Spaces

Probability Distributions

Unsupervised Learning

K-Means & Hierarchical Clustering

Linear Regression

Logistic Regression

Evaluation Metrics

Train, Test & Validation Distribution

Supervised Learning

Gradient Descent

Decision Tree & K-Nearest Neighbors (KNN)

Random Forest, Bagging & Boosting

Introduction to Deep Learning

Deep Learning: Strengths & Limitations

Single Perceptron (SP) & Multi-Layer Perceptron (MLP)

Feed Forward & Backward Propagation

Overview of Neural Networks

Neural Network Representation

Activation Functions

Loss Functions

Importance of Non-Linear Activation Functions

Gradient Descent for Neural Networks

Practical Aspects of Deep Learning

Train, Test & Validation Sets

Vanishing & Exploding Gradient Problem

Dropout Regularization

Regularization Techniques

Optimization Strategies

Bias Correction in Deep Learning

RMS Prop Algorithm

Adam, AdaGrad & AdaBoost Optimizers

Learning Rate and Its Impact

Hyperparameter Tuning

Softmax Activation Function

Image Preprocessing

Introduction to Computer Vision

Image Transformation, Filters & Noise Removal

Edge Detection, Non-Max Suppression & Hysteresis

Advanced CNN Concepts - Part 1

Object Detection: Bounding Boxes & Object Detection Models

Landmark Detection, RCNN, Fast RCNN, Faster RCNN

Mask RCNN, YOLO Pre-trained Models & Transfer Learning

Segmentation Concepts

Advanced CNN Concepts - Part 2

Advanced CNN Applications: Face Detection & Recognition

Techniques in Face Recognition & Style Transfer

Speech Processing

Introduction to Speech Processing & Automated Speech Recognition (ASR)

Speech Synthesis

Text-to-Speech Conversion

Voice Assistant Devices

Building Alexa Skills

Autoencoders & Decoders

Basics of Autoencoders & Different Types

Applications of Autoencoders with Examples

Variational Autoencoders & Introduction to Generative AI

Generative Adversarial Networks (GANs)

GAN Basics & Foundations

Upsampling & GAN Models

Evaluating GAN Models (Inception Score, Frechet Inception Distance, GAN Loss Functions)

Different Types of GANs

Conditional GAN, Info GAN, Auxiliary GAN, etc.

GAN Use Cases

Image Translation Applications & Cycle GAN Concepts

Implementation of Cycle GAN

Basic NLP Concepts & Models

Introduction to Text Mining & Vector Space Model (VSM)

Word Embeddings Applications

RNN, GRU, LSTM Models

Introduction to Transformers & Attention Mechanism

Attention-based Models (ELMo, BERT, T5)

Text Mining & NLP Applications

Web Scraping for NLP

Word Clouds & Document Similarity using Cosine Similarity

Named Entity Recognition (NER)

Machine Translation using Hugging Face Libraries

Emotion Mining using Different Libraries

Naive Bayes for Text Classification

Frequentist vs Bayesian Approaches

Apriori & Posterior Distributions

Bayesian Estimators: Posterior Mean & Posterior Median

Advanced NLP Models & Generative AI using LLMs

Introduction to Transformers & Attention Mechanisms

Single-Head vs Multi-Head Attention

Pretrained Models (GPT, BERT, BART, T5) & Applications

Examples using Python

Transformer Encoder Models: BERT, RoBERTa, DistilBERT

Transformer Decoder Models: GPT, GPT-2, GPT Variants

GPT Progress & OpenAI API Integrations

Exploring LLM Playgrounds

Transformer Sequence-to-Sequence Models: BART, T5