Course Detail
Data Science & AI
6-Month Advanced AI Program: Data Science, Machine Learning, Deep Learning, LLM & Generative AI
Career-oriented six-month AI program covering Data Science, Machine Learning, Deep Learning, LLMs, RAG, AI agents, and deployment.
0 students
The AI NEXO
Description
Career-oriented curriculum designed to take learners from data fundamentals to real-world ML, Deep Learning, LLM, RAG and AI application deployment. Learning outcome: build a portfolio of practical AI projects and understand the complete journey from data to model to intelligent application. Program approach: theory, hands-on labs, assignments, projects and capstone. Tools covered: Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow/Keras, PyTorch, Git/GitHub, Streamlit, FastAPI, LLM APIs, LangChain and LlamaIndex basics. Portfolio target: 10+ practical projects including Data Science, ML, Deep Learning and LLM/RAG applications.
What you'll learn
Analyze and visualize real-world datasets
Build, evaluate and optimize classical ML models
Develop neural-network and Deep Learning solutions
Understand CNNs, RNNs, LSTMs and Transformer architecture
Build LLM-powered applications using prompts, embeddings and RAG
Create basic AI agents and tool-enabled workflows
Expose AI models through APIs and build usable interfaces
Maintain a GitHub portfolio with documented projects
Course Curriculum
7 sections • 66 lessons • 38h 30m total
- Python fundamentals for Data Science Preview Watch • 35m
- Functions, data structures, modules and exception handling • 35m
- Jupyter Notebook and Google Colab workflow • 35m
- NumPy arrays, indexing, slicing, broadcasting and matrix operations • 35m
- Pandas DataFrames, filtering, sorting, groupby, merge and join • 35m
- CSV/Excel data loading and manipulation • 35m
- Statistics, probability, distributions, correlation and covariance • 35m
- Hypothesis testing and A/B testing basics • 35m
- Matplotlib, Seaborn, EDA, outlier and missing-value analysis • 35m
- Projects: Sales Data Analysis, Customer Analysis and Student Performance Analysis • 35m
- Data preprocessing: missing values, duplicates and outliers • 35m
- Encoding, scaling, normalization and feature engineering • 35m
- Train, validation and test splitting • 35m
- Supervised learning: Linear Regression, Polynomial Regression and Logistic Regression • 35m
- Decision Trees, Random Forest, KNN, SVM and Naive Bayes • 35m
- Unsupervised learning: K-Means, hierarchical clustering and PCA • 35m
- Model evaluation: Accuracy, Precision, Recall, F1, Confusion Matrix and ROC-AUC • 35m
- Regression metrics: MAE, MSE, RMSE, R2 and cross-validation • 35m
- Advanced ML: bias/variance, regularization, Gradient Boosting, AdaBoost and XGBoost • 35m
- Projects: House Price Prediction, Churn Prediction, Loan Prediction and Customer Segmentation • 35m
- Ensemble learning, feature importance and model optimization • 35m
- Reusable ML pipelines • 35m
- Neural-network fundamentals: neurons, weights, bias and layers • 35m
- Activation functions, forward propagation and loss functions • 35m
- Backpropagation and gradient descent • 35m
- TensorFlow/Keras workflow • 35m
- PyTorch fundamentals: datasets, tensors and training loops • 35m
- Training and optimization with SGD, Adam, batch size, epochs and learning rate • 35m
- Validation and early stopping • 35m
- Project: build and evaluate a neural-network based prediction system • 35m
- Computer vision: images as tensors, CNN architecture, filters and feature maps • 35m
- Pooling and image classification • 35m
- Transfer learning with pre-trained models and fine-tuning concepts • 35m
- VGG, ResNet and MobileNet overview • 35m
- Object detection concepts, bounding boxes and YOLO fundamentals • 35m
- NLP with Deep Learning: preprocessing, tokenization and word embeddings • 35m
- Sequence modeling with RNN, LSTM and GRU • 35m
- Vanishing-gradient problem and sequence classification • 35m
- Transformers: attention, self-attention, encoder/decoder and positional encoding • 35m
- Projects: Image Classification, Object Detection, Sentiment Analysis and Text Classification • 35m
- LLM fundamentals: tokens, parameters, context windows, pre-training and inference • 35m
- Transformer-based LLM architecture • 35m
- Prompt engineering: zero-shot, few-shot, role and instruction prompting • 35m
- Prompt templates, structured outputs and reliable prompting • 35m
- Embeddings, similarity search, vector databases and semantic search • 35m
- RAG architecture, document loading and chunking • 35m
- Embedding to retrieval to generation pipelines • 35m
- Grounded answers and evaluation basics • 35m
- LangChain and LlamaIndex concepts: chains, retrievers and tools • 35m
- Fine-tuning concepts: datasets, LoRA, PEFT and quantization basics • 35m
- Projects: PDF Chatbot, Company Knowledge Bot and AI Customer Support Assistant • 35m
- LLM APIs, open-source LLM concepts, function/tool calling and structured outputs • 35m
- AI agent architecture: tools, memory, planning and multi-step workflows • 35m
- Retrieval-enabled agents • 35m
- Streamlit application development • 35m
- FastAPI and REST APIs • 35m
- Connecting models to applications • 35m
- Git, GitHub, model saving and loading • 35m
- Docker basics and cloud deployment concepts • 35m
- MLOps basics: experiment tracking, model versioning, monitoring and deployment pipelines • 35m
- Capstone: Data to ML/DL to LLM/RAG to API to UI to deployment • 35m
- Data Science portfolio: Sales Dashboard and Customer Analytics • 35m
- Machine Learning portfolio: House Price, Churn, Loan Prediction and Customer Segmentation • 35m
- Deep Learning portfolio: Image Classifier, Object Detection and Sentiment/NLP Model • 35m
- LLM/GenAI portfolio: PDF Chatbot, Knowledge Bot and AI Support Assistant • 35m
- Capstone portfolio: end-to-end AI application with model/LLM, RAG or tools, API, UI and deployment • 35m
Requirements
- Basic computer knowledge
- Interest in AI, data and practical projects
- A laptop or desktop with internet access
- Willingness to practice with Python, notebooks and project assignments
Who this course is for
- Students and graduates building AI/ML careers
- Working professionals moving into Data Science and AI
- Developers who want hands-on ML, Deep Learning and LLM skills
- Learners who want a project portfolio and capstone experience
Enrollment
₹50,000.00
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Secure checkout, instant course access after payment.
- 66 lessons
- 38h 30m total length
- Advanced level
- English/Hindi language
- Certificate
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