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Artificial Intelligence

AI & Machine Learning

Dive into the world of artificial intelligence and machine learning. Learn to build intelligent systems using Python, TensorFlow, and modern AI frameworks that are transforming industries worldwide.

2.5 Months (10 Weeks)
4 Projects
Certificate
Enroll NowView Curriculum
Build 4 AI/ML projects
Learn from industry AI experts
Physical & online classes available
Industry-recognized certificate
Internship placement support

Technologies You'll Master

PythonNumPyPandasTensorFlowPyTorchScikit-learnKerasOpenCVNLPDeep LearningCNNData Visualization
Week by Week

Course Curriculum

A comprehensive 10-week journey into AI and Machine Learning

Build a strong foundation in Python programming specifically for AI and data science. Master the tools that every ML engineer uses daily.

What You'll Learn

Python fundamentals: data structures, control flow, functions, and OOP concepts
NumPy for numerical computing: arrays, broadcasting, and vectorized operations
Pandas for data manipulation: DataFrames, data cleaning, and transformation
Data visualization with Matplotlib: line plots, bar charts, histograms, and subplots
Advanced visualization with Seaborn: statistical plots, heatmaps, and pair plots
Jupyter notebooks: interactive development, documentation, and reproducible analysis
Week 1 Project: Analyze and visualize a real-world dataset

Perform comprehensive exploratory data analysis on a dataset of your choice (e.g., COVID-19 data, stock prices, or climate data). Clean the data, generate insights, and create publication-quality visualizations with a detailed report.

Understand the mathematical concepts that power ML algorithms. This foundation will help you understand why algorithms work, not just how to use them.

What You'll Learn

Linear algebra essentials: vectors, matrices, eigenvalues, and matrix decomposition
Probability fundamentals: distributions, Bayes theorem, and conditional probability
Statistics for ML: mean, variance, standard deviation, and hypothesis testing
Calculus basics: derivatives, gradients, partial derivatives, and chain rule
Optimization theory: gradient descent, learning rates, and convergence
Mathematical intuition behind loss functions and cost optimization
Week 2 Project: Implement mathematical operations from scratch

Build a mini linear algebra library in Python implementing matrix multiplication, transpose, determinants, and eigenvalue computation. Then implement gradient descent from scratch to optimize a simple function.

Learn the core concepts of machine learning that form the basis of all modern AI systems. Understand the ML workflow from data to deployment.

What You'll Learn

Supervised vs unsupervised learning: understanding when to use each approach
Linear regression: theory, implementation, and interpretation of coefficients
Polynomial regression and feature engineering for non-linear relationships
Model evaluation metrics: MSE, RMSE, MAE, R-squared, and adjusted R-squared
Train/test split, cross-validation techniques, and preventing data leakage
Bias-variance tradeoff, underfitting, overfitting, and regularization basics
Week 3 Project: Build a house price prediction model

Create a comprehensive house price prediction system using the Boston Housing or Kaggle Housing dataset. Implement feature engineering, try multiple algorithms, perform hyperparameter tuning, and create a detailed analysis of which features most impact prices.

Master classification techniques for categorizing data. Classification is one of the most common ML tasks in real-world applications.

What You'll Learn

Logistic regression: sigmoid function, decision boundaries, and multiclass classification
Decision trees: information gain, Gini impurity, tree pruning, and visualization
Random forests: ensemble learning, bagging, feature importance, and hyperparameters
Support Vector Machines: kernel trick, margins, and handling non-linear data
K-Nearest Neighbors: distance metrics, choosing K, and weighted voting
Classification metrics: accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices
Week 4 Project: Create a spam email classifier

Build an email spam detection system using multiple algorithms. Implement text preprocessing (tokenization, stemming, TF-IDF), compare algorithm performance, handle class imbalance, and create a deployable model with a simple interface.

Discover patterns in unlabeled data. Unsupervised learning is crucial for exploratory analysis and finding hidden structures in data.

What You'll Learn

K-means clustering: algorithm, elbow method, silhouette score, and initialization strategies
Hierarchical clustering: agglomerative, dendrograms, and linkage methods
DBSCAN: density-based clustering for arbitrary shapes and noise handling
Dimensionality reduction with PCA: variance explained, scree plots, and visualization
t-SNE for visualization: perplexity tuning and interpreting 2D/3D projections
Anomaly detection: isolation forests, LOF, and one-class SVM for outlier detection
Week 5 Project: Customer segmentation for e-commerce

Analyze e-commerce customer data to identify distinct customer segments. Use RFM analysis, apply multiple clustering algorithms, visualize segments with PCA/t-SNE, create customer personas, and provide actionable marketing recommendations.

Enter the world of neural networks and deep learning. Understand the architecture that powers modern AI systems from chatbots to self-driving cars.

What You'll Learn

Neural network architecture: neurons, layers, weights, biases, and network topology
Activation functions: sigmoid, tanh, ReLU, Leaky ReLU, and when to use each
Forward propagation: how data flows through the network for predictions
Backpropagation: chain rule, gradient computation, and weight updates
Introduction to TensorFlow and Keras: model building, compilation, and training
Training neural networks: batch size, epochs, learning rate scheduling, and callbacks
Week 6 Project: Image classification with neural networks

Build an image classifier for the MNIST or Fashion-MNIST dataset. Experiment with different architectures (shallow vs deep), activation functions, and optimizers. Visualize learning curves, analyze misclassifications, and achieve >95% accuracy.

Master computer vision with CNNs. These specialized networks are the backbone of image recognition, object detection, and visual AI.

What You'll Learn

CNN architecture: convolutional layers, filters, feature maps, and receptive fields
Pooling layers: max pooling, average pooling, and global pooling for dimensionality reduction
CNN building blocks: stride, padding, and designing effective architectures
Image preprocessing: normalization, augmentation (rotation, flip, zoom, crop)
Transfer learning: using pre-trained models (VGG, ResNet, EfficientNet) effectively
Fine-tuning strategies: freezing layers, learning rate selection, and domain adaptation
Week 7 Project: Build an image recognition application

Create a custom image classification system for a real-world use case (plant disease detection, food recognition, or product categorization). Use transfer learning with a pre-trained model, implement data augmentation, and deploy as a web app or API.

Teach machines to understand human language. NLP powers search engines, chatbots, translation services, and sentiment analysis.

What You'll Learn

Text preprocessing: tokenization, lowercasing, stopword removal, and stemming/lemmatization
Text representation: bag of words, TF-IDF, and n-grams for feature extraction
Word embeddings: Word2Vec (CBOW, Skip-gram), GloVe, and semantic similarity
Sentiment analysis: building classifiers for opinion mining and emotion detection
Named Entity Recognition (NER): extracting entities from text
Introduction to transformers: attention mechanism, BERT architecture, and pre-trained models
Week 8 Project: Create a sentiment analysis tool

Build a comprehensive sentiment analysis system for product reviews or social media posts. Implement multiple approaches (traditional ML vs deep learning), handle sarcasm and negation, create visualizations of sentiment trends, and deploy as an API.

Explore advanced architectures including sequence models and generative AI that powers ChatGPT, image generators, and more.

What You'll Learn

Recurrent Neural Networks (RNN): sequential data, hidden states, and vanishing gradients
LSTM and GRU: gating mechanisms for learning long-term dependencies
Sequence-to-sequence models: encoder-decoder architecture for translation and summarization
Attention mechanisms: self-attention, multi-head attention, and transformer blocks
Introduction to Large Language Models: GPT architecture and prompt engineering
Fine-tuning pre-trained models: LoRA, adapters, and efficient fine-tuning techniques
Week 9 Project: Build a text generation application

Create a text generation system using either a fine-tuned language model or a custom LSTM. Applications could include story generation, code completion, or chatbot responses. Implement temperature-based sampling and interactive generation.

Deploy models to production and complete your portfolio project. Learn the practices that separate hobbyists from professional ML engineers.

What You'll Learn

Model deployment with Flask/FastAPI: creating REST APIs for ML models
Model serialization: pickle, joblib, ONNX, and TensorFlow SavedModel formats
MLOps fundamentals: experiment tracking with MLflow, model versioning, and reproducibility
Cloud deployment basics: AWS SageMaker, Google AI Platform, or Azure ML overview
Model monitoring: drift detection, performance tracking, and retraining strategies
Portfolio building and technical interview preparation for ML/AI roles
Week 10 Project: End-to-end ML project of your choice

Build and deploy a complete ML solution from problem definition to production. Examples: recommendation system, fraud detection, medical diagnosis, or predictive maintenance. Must include data pipeline, model training, API deployment, and monitoring dashboard.

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