Dive into the world of Artificial Intelligence and Machine Learning. Learn Python, data science fundamentals, neural networks, natural language processing, computer vision, and how to build and deploy AI-powered applications.
What You'll Learn
Python programming from scratch
Machine learning algorithms & models
Deep learning with TensorFlow & PyTorch
NLP, computer vision & generative AI
Deploy AI models to production
4
Months Program
16 Weeks
4 Phases
16 Detailed Weeks
Coming Soon
Curriculum
W1
Python Programming
Python syntax & data types
Control flow & functions
Object-oriented programming
File I/O & error handling
W2
Data Science Libraries
NumPy for numerical computing
Pandas for data manipulation
Data cleaning & preprocessing
Exploratory data analysis
W3
Data Visualization
Matplotlib fundamentals
Seaborn for statistical plots
Interactive visualizations with Plotly
Dashboard creation
W4
Statistics & Probability
Descriptive statistics
Probability distributions
Hypothesis testing
Correlation & regression basics
W5
ML Fundamentals
Supervised vs unsupervised learning
Train/test splits & cross-validation
Bias-variance tradeoff
Scikit-learn introduction
W6
Regression & Classification
Linear & logistic regression
Decision trees & random forests
Support vector machines
Model evaluation metrics
W7
Advanced ML Algorithms
Ensemble methods (XGBoost, LightGBM)
Clustering (K-means, DBSCAN)
Dimensionality reduction (PCA, t-SNE)
Feature engineering techniques
W8
ML Project
End-to-end ML pipeline
Data collection & preprocessing
Model training & evaluation
Model serialization & API serving
W9
Neural Networks
Perceptrons & activation functions
Backpropagation & gradient descent
TensorFlow & Keras basics
Building your first neural network
W10
Computer Vision
Convolutional neural networks (CNNs)
Image classification & object detection
Transfer learning & fine-tuning
OpenCV for image processing
W11
Natural Language Processing
Text preprocessing & tokenization
Word embeddings (Word2Vec, GloVe)
Recurrent neural networks & LSTMs
Sentiment analysis project
W12
Transformers & LLMs
Attention mechanism & transformers
BERT, GPT & modern architectures
Hugging Face ecosystem
Fine-tuning pre-trained models
W13
Generative AI
Prompt engineering techniques
LangChain & RAG pipelines
Building AI chatbots
Image generation with Stable Diffusion
W14
MLOps & Deployment
Model deployment with FastAPI
Docker for ML applications
Cloud deployment (AWS SageMaker)
Monitoring & model versioning
W15
Capstone Project
Project planning & data collection
Model development & training
API & frontend integration
Testing & optimization
W16
Capstone Presentation
Final project demo
Documentation & portfolio
Ethics in AI discussion
Career paths & next steps
Ready to start your journey?
Enrollment for Artificial Intelligence will open soon. Stay tuned!
