This course provides a comprehensive path to learning both Machine Learning and Deep Learning from the ground up.
You will begin with Python & statistical foundations, explore ML algorithms, and then progress into advanced topics like neural networks, CNNs, RNNs, Transformers, and deployment techniques.

The curriculum includes hands-on implementation using Scikit-Learn, TensorFlow, and PyTorch, along with real projects from domains like vision, NLP, and predictive analytics.
By the end, learners will be able to build, train, evaluate, and deploy ML/DL models confidently.

Learning Outcomes

Machine Learning Foundations

• Understand ML workflow & pipelines

• Exploratory Data Analysis (EDA)

• Data preprocessing & feature engineering

• Model selection & evaluation

• Regression & classification models

• Clustering & dimensionality reduction

Supervised Learning

• Linear & Logistic Regression

• Decision Trees & Random Forest

• SVM

• Naive Bayes

• Gradient Boosting (XGBoost, LightGBM)

Unsupervised Learning

• K-Means clustering

• Hierarchical clustering

• PCA & dimensionality reduction

• Anomaly detection basics

Deep Learning Foundations

• Understanding neural networks

• Forward & backward propagation

• Activation functions

• Loss functions & optimizers

• Training strategies & regularization

TensorFlow & PyTorch

• Building neural networks

• Model training & evaluation

• Custom layers & functions

• Saving & loading models

Convolutional Neural Networks (CNNs)

• Image preprocessing

• CNN architecture

• Image classification

• Data augmentation

• Transfer learning using VGG, ResNet, MobileNet

Natural Language Processing (NLP)

• Text preprocessing

• Tokenization & embeddings

• RNN, LSTM, GRU

• Attention mechanism

• Intro to Transformers

• Sentiment analysis & text classification

Advanced Topics

• Hyperparameter tuning

• Model optimization

• Overfitting/underfitting control

• Intro to GANs

• Intro to Reinforcement Learning

Deployment & MLOps Basics

• Export ML/DL models

• Build prediction APIs with Flask/FASTAPI

• Model monitoring basics

• Intro to ML pipelines (TensorFlow Serving / TorchServe)

Project Work

• ML regression & classification projects

• EDA project

• Image classification using CNN

• NLP sentiment analysis project

• Final AI Capstone Project: ML/DL model + deployment