Our AI, Machine Learning & Generative AI with Python training course is designed to equip learners with the advanced skills required to build, deploy, and scale intelligent systems using Python. This course is ideal for individuals who want to move beyond basic data science and become industry-ready AI and ML professionals capable of working with deep learning, NLP, computer vision, and large language models.
What is the Course About?
This course provides a comprehensive and practical understanding of Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI using Python. It covers the entire AI lifecycle—from data analysis and model building to deployment, cloud integration, and real-world applications.
You will learn how to process and analyze data, build machine learning and deep learning models, work with natural language and images, and develop LLM-powered AI applications such as chatbots, search systems, and intelligent agents.
What Will You Learn?
You will learn how to use powerful Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, PyTorch, Hugging Face, and LangChain to build modern AI systems.
You will work with:
- Supervised and unsupervised machine learning algorithms
- Deep learning models such as CNNs, RNNs, LSTMs, Autoencoders, GANs, and Transformers
- Natural Language Processing and Large Language Models (LLMs)
- Computer Vision for image and video understanding
- Model evaluation, fine-tuning, deployment through APIs, and cloud platforms
You will also learn how to deploy models using Flask, integrate databases and vector stores, and build real-time AI applications.
Skills You Will Gain
After completing this course, you will gain:
- Strong proficiency in Python for AI and Machine Learning
- Advanced data analysis, visualization, and feature engineering skills
- Ability to build, train, and optimize machine learning and deep learning models
- Experience working with LLMs, NLP, and Computer Vision systems
- Skills to deploy AI models through APIs and cloud platforms
- Understanding of model evaluation, tuning, and real-world AI system design
Overall, this course gives you a complete, job-ready skillset to build and deploy intelligent, data-driven applications.
Why Should I Enroll in This Course?
Taking this advanced AI and Generative AI training offers several benefits:
High demand for AI and Generative AI skills
AI, Machine Learning, and Generative AI are among the fastest-growing fields globally. Organizations across industries are actively hiring professionals who can build intelligent systems and AI-powered products.
Hands-on experience with industry tools
You will work with modern tools like TensorFlow, PyTorch, Hugging Face, LangChain, Flask, and cloud platforms, gaining real-world experience that employers look for.
Learn cutting-edge AI technologies
You will gain in-depth knowledge of deep learning, LLMs, NLP, computer vision, RAG, and agentic AI, which are powering today’s ChatGPT-like and enterprise AI systems.
Develop strong problem-solving and AI engineering skills
You will learn how to analyze problems, build models, evaluate performance, fine-tune results, and deploy scalable AI solutions.
Flexible learning and live project experience
With hands-on labs, live projects, and an internship, you will build a strong portfolio while learning at a flexible pace.
Career Pathways, Average Salary and Hiring Companies
After completing this course, you can pursue roles such as:
Data Scientist
Data Scientists analyze large datasets and build predictive models.
Average salary in India ranges from ₹4 LPA to ₹25+ LPA.
Hiring companies include Accenture, Deloitte, Fractal Analytics, IBM, Infosys, TCS, and Wipro.
Machine Learning Engineer
ML Engineers design and deploy machine learning systems.
Average salary is around ₹8–15 LPA.
Hiring companies include Amazon, Google, Microsoft, IBM, Nvidia, and Qualcomm.
AI / Generative AI Engineer
AI Engineers build intelligent systems and LLM-based applications.
Average salary ranges from ₹9–18 LPA.
Hiring companies include Accenture, Deloitte, Genpact, Infosys, TCS, and AI startups.
NLP or Computer Vision Engineer
These professionals build AI systems for text, speech, images, and video.
Average salary ranges from ₹7–16 LPA.
AI Application Developer
Developers build AI-powered web apps, APIs, and chatbots.
Average salary ranges from ₹6–12 LPA.
Note: Salaries vary based on experience, location, skillset, and company.
Curriculum
- 16 Sections
- 70 Lessons
- Lifetime
- Fundamentals of Python8
- Python Programming for Mathematical and Scientific Computation4
- Exploratory Data Analysis1
- Introduction to Machine Learning5
- Building models with popular Supervised Learning algorithms6
- 5.1K-Nearest Neighbors: Classification and Regression
- 5.2Linear Regression: Least-Squares, Ridge,Lasso, and Polynomial Regression
- 5.3Logistic Regression, Support Vector Machines, Decision Trees
- 5.4Neural Networks, Naive Bayes
- 5.5Ensemble Modelling, Random Forest, Gradient Boosted Decision Trees
- 5.6Tools Covered: Python, NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn
- Model Evaluation6
- Building models with popular Unsupervised Learning4
- Serving Machine Learning Models through web API2
- Introduction to Deep Learning5
- 9.1Programming with Tensorflow/Keras and Pytorch
- 9.2Deep Learning Architectures: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) Networks, Autoencoders
- 9.3Transfer Learning and Fine Tuning
- 9.4Generative AI algorithms: Generative Adversarial Networks (GANs), Transformers, Diffusion models,Large Language Models (LLMs)
- 9.5Tools Covered: Python, TensorFlow/Keras
- Natural Language Processing (NLP) and Applied Text Mining in Python3
- Computer Vision4
- Generative AI and Large Language Models (LLMs)12
- 12.1Introduction to Generative AI and Popular LLMs: Definition, history, key characteristics, and applications; popular LLMs (GPT, BERT, T5, LLaMA, Phi)
- 12.2Fundamentals of LLMs: Transformer architecture and attention mechanism; pre-training and fine-tuning; ethics in Generative AI
- 12.3Working with LLMs: Hugging Face setup and usage; NLP tasks (text generation, classification, summarization); evaluation metrics (Perplexity, BLEU, Precision, Recall, F1 Score)
- 12.4LangChain & Application Development: Core components (Chains, Agents, Memory, Prompts); chatbot development and integration with LLMs
- 12.5Vector 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
- 12.6Transfer Learning & Fine-Tuning: Transfer learning concepts; fine-tuning LLMs on domain-specific data; LoRA (Low-Rank Adaptation) for efficient fine-tuning
- 12.7Agentic AI & Model Context Protocol(MCP): Understanding agentic models; multi-step reasoning and planning
- 12.8Advanced Techniques: RLHF (Reinforcement Learning from Human Feedback); Chain-of-Thought (CoT) prompting; agentic models and decision-making
- 12.9Open-Source Models & Ollama: Setup and usage of Ollama; comparison with commercial LLMs; demo
- 12.10Chatbot Development & Best Practices: APIs, context management, personalization; evaluating chatbots using BLEU, Perplexity, F1 Score
- 12.11Future Directions: Multi-modal models (text + image + video); few-shot and zero-shot learning; scalability and real-time processing
- 12.12Tools Covered: Python, Hugging Face Transformers, LangChain, OpenAI API, Ollama, PyTorch, TensorFlow, Vector Databases, evaluation tools
- Prompt engineering1
- Database5
- Cognitive Computing using Cloud Platforms4
- Internship - 2 Live Project, Internship duration - 3 to 6 months0
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