Curriculum
16 Sections
70 Lessons
Lifetime
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Fundamentals of Python
8
1.1
History & Background
1.2
Basic Syntax
1.3
Data Types: Lists, Tuples, Dictionaries etc.
1.4
Basic Operators
1.5
Control Flow, Functions, File I/O & Exceptions
1.6
Classes & Libraries
1.7
Functional Programming
1.8
Tools Covered: Python IDLE (for writing and running Python programs).
Python Programming for Mathematical and Scientific Computation
4
2.1
NumPy (package for fast numerical calculation)
2.2
Pandas (Tabular data manipulation package)
2.3
Plotting and Charting with Matplotlib,Seaborn ang plotly (Data Visualization package)
2.4
Tools covered: NumPy, Pandas, Matplotlib, Seaborn, plotly
Exploratory Data Analysis
1
3.1
Learn to analyze, visualize, and summarize data to uncover patterns, insights, and relationships before modeling.
Introduction to Machine Learning
5
4.1
Machine Learning Overview
4.2
Types of Machine Learning
4.3
Predictive modelling with scikit-learn (Machine Learning package)
4.4
Basics of building Machine Learning Models
4.5
Tools Covered: Python, NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn.
Building models with popular Supervised Learning algorithms
6
5.1
K-Nearest Neighbors: Classification and Regression
5.2
Linear Regression: Least-Squares, Ridge,Lasso, and Polynomial Regression
5.3
Logistic Regression, Support Vector Machines, Decision Trees
5.4
Neural Networks, Naive Bayes
5.5
Ensemble Modelling, Random Forest, Gradient Boosted Decision Trees
5.6
Tools Covered: Python, NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn
Model Evaluation
6
6.1
Model Evaluation & Selection
6.2
Overfitting and Underfitting
6.3
Cross-Validation
6.4
Confusion Matrices & Advanced Evaluation
6.5
Precision-recall and Receiver Operating Characteristic (ROC) curves
6.6
Tools Covered: Python, scikit-learn, Matplotlib, and Seaborn.
Building models with popular Unsupervised Learning
4
7.1
Kernel Density Estimation (KDE)
7.2
Dimensionality Reduction – Principal Component Analysis (PCA) and Manifold Learning
7.3
Clustering – KMeans, Hierarchical and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
7.4
Tools Covered: Python, scikit-learn, Matplotlib, and Seaborn.
Serving Machine Learning Models through web API
2
8.1
Deploy your machine learning models seamlessly through a web API using Flask for efficient and scalable real-time predictions.
8.2
Tools Covered: Python, Flask, scikit-learn
Introduction to Deep Learning
5
9.1
Programming with Tensorflow/Keras and Pytorch
9.2
Deep Learning Architectures: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) Networks, Autoencoders
9.3
Transfer Learning and Fine Tuning
9.4
Generative AI algorithms: Generative Adversarial Networks (GANs), Transformers, Diffusion models,Large Language Models (LLMs)
9.5
Tools Covered: Python, TensorFlow/Keras
Natural Language Processing (NLP) and Applied Text Mining in Python
3
10.1
Introduction to Natural Language Processing (NLP)
10.2
NLP with transformer package (Text Classification, Summarisation, Paraphrasing, Translation,Information Extraction)
10.3
Tools Covered: Python, Hugging Face Transformers, Natural Language Toolkit (NLTK) (optional).
Computer Vision
4
11.1
Introduction to Image and Video Processing with OpenCV
11.2
Computer vision with deep neural networks: Image Classification, Object Detection, Segmentation & Image Generation
11.3
Computer vision with Yolo and Detectron
11.4
Tools Covered: Python, OpenCV, TensorFlow/Keras, PyTorch, YOLO, Detectron2
Generative AI and Large Language Models (LLMs)
12
12.1
Introduction to Generative AI and Popular LLMs: Definition, history, key characteristics, and applications; popular LLMs (GPT, BERT, T5, LLaMA, Phi)
12.2
Fundamentals of LLMs: Transformer architecture and attention mechanism; pre-training and fine-tuning; ethics in Generative AI
12.3
Working with LLMs: Hugging Face setup and usage; NLP tasks (text generation, classification, summarization); evaluation metrics (Perplexity, BLEU, Precision, Recall, F1 Score)
12.4
LangChain & Application Development: Core components (Chains, Agents, Memory, Prompts); chatbot development and integration with LLMs
12.5
Vector Databases & Retrieval-Augmented Generation (RAG): Vector databases for storing and searching embeddings; RAG for combining retrieved knowledge with LLM generation to produce accurate, context-aware responses
12.6
Transfer Learning & Fine-Tuning: Transfer learning concepts; fine-tuning LLMs on domain-specific data; LoRA (Low-Rank Adaptation) for efficient fine-tuning
12.7
Agentic AI & Model Context Protocol(MCP): Understanding agentic models; multi-step reasoning and planning
12.8
Advanced Techniques: RLHF (Reinforcement Learning from Human Feedback); Chain-of-Thought (CoT) prompting; agentic models and decision-making
12.9
Open-Source Models & Ollama: Setup and usage of Ollama; comparison with commercial LLMs; demo
12.10
Chatbot Development & Best Practices: APIs, context management, personalization; evaluating chatbots using BLEU, Perplexity, F1 Score
12.11
Future Directions: Multi-modal models (text + image + video); few-shot and zero-shot learning; scalability and real-time processing
12.12
Tools Covered: Python, Hugging Face Transformers, LangChain, OpenAI API, Ollama, PyTorch, TensorFlow, Vector Databases, evaluation tools
Prompt engineering
1
13.1
Learn techniques to design effective prompts for AI models to generate accurate, relevant, and optimized responses.
Database
5
14.1
MySQL, PostgreSQL
14.2
Redis
14.3
Cassandra
14.4
Neo4J
14.5
MongoDB
Cognitive Computing using Cloud Platforms
4
15.1
IBM Watson
15.2
Microsoft Azure
15.3
Amazon Al
15.4
Google Cloud ML
Internship - 2 Live Project, Internship duration - 3 to 6 months
0
Advanced AI & Applied Data Science with Python(Level2)
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