Customer churn prediction model
Build, evaluate, and select the best model for predicting customer churn on a real telecom dataset, with full feature engineering, cross-validation, and an MLflow experiment log.
scikit-learnXGBoostMLflow
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Machine Learning & Gen AI
From scikit-learn to PyTorch to RAG pipelines and LLM fine-tuning, a complete 5-month course covering classical ML, deep learning, and the Gen AI tools transforming every industry.
Curriculum
Five modules taking you from Python data science foundations through deep learning to building production LLM and Gen AI applications.
Wk 1: Python for ML, advanced Python, comprehensions, decorators, OOP, file I/O
Wk 2: NumPy, arrays, broadcasting, vectorised operations; pandas, DataFrames, groupby, merge, cleaning
Wk 3: Matplotlib, Seaborn, Plotly, exploratory data analysis (EDA), distributions, correlations
Wk 4: Statistics for ML, probability, Bayes' theorem, normal distribution, hypothesis testing, covariance
Wk 5: Supervised learning: linear and logistic regression, evaluation metrics (accuracy, F1, AUC)
Wk 6: Decision trees, random forests, gradient boosting (XGBoost, LightGBM)
Wk 7: Unsupervised learning: k-means, DBSCAN, hierarchical clustering, PCA
Wk 8: Feature engineering, handling missing data, encoding, scaling, pipeline construction
Wk 9: Model selection, cross-validation, hyperparameter tuning (GridSearch, Optuna), MLflow basics
Wk 10: Neural networks: forward/backward propagation, activation functions, loss functions from scratch
Wk 11: TensorFlow and Keras: sequential API, functional API, callbacks, model saving
Wk 12: PyTorch: tensors, autograd, custom Dataset, DataLoader, training loop
Wk 13: CNNs: convolutional layers, pooling, image classification, transfer learning (ResNet, EfficientNet)
Wk 14: RNNs, LSTMs, sequence modelling, text classification, sentiment analysis
Wk 15: NLP, tokenisation, embeddings (Word2Vec, GloVe, FastText), text preprocessing
Wk 16: Transformers architecture, attention mechanism, BERT, GPT; Hugging Face Transformers library
Wk 17: LLM fine-tuning with LoRA/QLoRA, OpenAI API, Anthropic API, prompt design
Wk 18: Generative AI, RAG (retrieval-augmented generation), vector databases (FAISS, ChromaDB), LangChain
Wk 19: Building AI agents, tool calling, memory, multi-step workflows
Wk 20: ML deployment: model serving with FastAPI, Dockerising ML models, deploying to AWS; capstone project and mock interviews
Hands-on Projects
Every module ends with a working project you built yourself using real data, the kind that fills a portfolio and anchors an interview story.
Build, evaluate, and select the best model for predicting customer churn on a real telecom dataset, with full feature engineering, cross-validation, and an MLflow experiment log.
Train a convolutional neural network for multi-class image classification using PyTorch, then apply transfer learning with ResNet to improve accuracy on a custom dataset.
Fine-tune a BERT model on a domain-specific dataset for sentiment classification, implemented end-to-end with Hugging Face Transformers, Trainer API, and model deployment.
Build a production-ready RAG application that answers questions from a custom document collection, with FAISS vector store, LangChain orchestration, and a FastAPI endpoint deployed on AWS.
Career Outcomes
We don't promise "100% placement"; no honest institute can. Here's what we actually provide, and what you can realistically expect.
Typical starting salaries in Pune for ML and AI roles, an observation, not a guarantee.
Four end-to-end portfolio projects: ML model, CNN, BERT fine-tune, and a RAG application, deployed and demonstrable.
A TechSummitTrainers completion certificate for this program once you finish the course and capstone project.
Roles this prepares you for
Schedule & Format
Attend live from our FC Road centre or join remotely. Your choice, every session.
Every session includes a Jupyter notebook with working code and exercises, shared after each class.
Every session runs live, and is recorded so you can run code alongside the replay at your own pace.
Bring your own laptop. All labs run on your own machine and in Google Colab, no special hardware required.
Course FAQ
You need basic familiarity with algebra and statistics, but advanced mathematics is not required to start. The course covers all the mathematical foundations needed, linear algebra, probability, and statistics, in the context of the algorithms you will implement.
Python fundamentals are required. If you do not know Python, we recommend attending one of our full-stack Python sessions or working through Python basics for 2-4 weeks before joining. The course covers NumPy, pandas, and Matplotlib as the first module.
Machine Learning is the broader field of algorithms that learn from data. Deep Learning is a subset using neural networks. Generative AI is the latest wave, LLMs and diffusion models that generate new content. This course covers all three in that order.
Both. The first three modules cover classical ML and deep learning, which you need to understand to use AI tools effectively. Modules 4 and 5 cover NLP, LLMs, fine-tuning, RAG pipelines, and prompt engineering.
RAG (Retrieval-Augmented Generation) is the technique of connecting an LLM to your own data so it can answer questions about content it was not trained on. It is now the most common pattern for building real-world AI applications in companies.
Python, NumPy, pandas, scikit-learn, TensorFlow, Keras, PyTorch, Hugging Face Transformers, LangChain, OpenAI API, FAISS, ChromaDB, FastAPI, MLflow, and Jupyter Notebooks, the standard toolkit for ML engineers today.
The course fee is discussed during your free demo call. We offer flexible payment options. Contact us to know more.
Machine Learning Engineer, AI Engineer, Data Scientist, NLP Engineer, Gen AI Developer. Fresher salaries in Pune for ML and AI roles typically range from ₹5 LPA to ₹12 LPA based on the company and project complexity.
If you have done our Data Science course or have equivalent experience, you can join from Module 2 (deep learning). Discuss your background at the free demo and we will suggest the right entry point.
Recordings of every live session are shared within 24 hours. Lab notebooks are shared after each session so you can run the code independently.
Yes. Batch timings are confirmed at your free demo based on current batch schedules, with morning and evening options available.
Any graduate degree in engineering, science, or mathematics is ideal, BE/B.Tech, BCA, BSc IT, BSc (Math/Stats), MCA, or MSc. Candidates from other disciplines with strong quantitative aptitude are also welcome.
Other tracks at TechSummitTrainers
Machine Learning (ML) is the branch of artificial intelligence where systems learn patterns from data to make predictions or decisions, without being explicitly programmed for each case. From the recommendation engines that suggest your next video, to the fraud detection system that flags unusual bank transactions, to the generative AI tools now transforming knowledge work, ML is the underlying technology. For IT professionals and graduates in Pune, machine learning skills are among the highest-value and fastest-growing credentials in the current hiring market.
Demand in Pune is driven by multinational IT services firms running AI transformation projects for global clients, fintech and banking companies deploying ML for credit scoring and fraud detection, healthcare technology companies building diagnostic AI, and product companies at every stage integrating AI into their offerings. The 2024–25 hiring cycle has seen particular demand for engineers who combine classical ML expertise with hands-on Gen AI skills, exactly the combination this course covers.
These three terms are often used interchangeably in job postings, but they describe distinct levels of a technology stack:
This course covers all three layers in sequence, because understanding ML and deep learning fundamentals is what allows you to use Gen AI tools effectively and build on top of them, rather than just calling an API.
RAG (Retrieval-Augmented Generation) is the technique of connecting a large language model to an external knowledge base, your company's documents, database, support tickets, product catalogue, so it can answer questions about information it was not trained on. Without RAG, an LLM can only answer from its training data, which is frozen in time and doesn't include your specific business context. With RAG, you can build AI assistants, document Q&A systems, and customer support bots that give accurate, source-cited answers from your own data.
RAG has become the most common architecture for deploying LLMs in production. The core components, a vector database (FAISS or ChromaDB), an embedding model, a retriever, and an LLM, are covered in full in Module 5 of this course, culminating in a fully deployed RAG application as the capstone project.
The Data Science & Analytics course at TechSummitTrainers focuses on data analysis, SQL, Power BI/Tableau, and business analytics with Python, a broader analytics and business intelligence role. The Machine Learning & Gen AI course goes deeper into the engineering side: advanced ML algorithms, neural network implementation, model training, and building AI applications. The two courses overlap at the Python for ML layer but diverge strongly from Module 2 onwards. Many students take Data Science first and then advance to this course for the ML and AI engineering depth.
TechSummitTrainers is located at Office No. 509, Kushal Wallstreet, Fergusson College Road, Shivajinagar, central Pune, easily accessible from Shivajinagar station, Deccan, Kothrud, and Hinjewadi via the expressway. All labs in this course run on Jupyter Notebooks, shared after every session, and can be executed on your own laptop with no special hardware required (GPU sessions use Google Colab). The practitioner-led format means every concept is taught in the context of how it's actually applied in production ML systems, not academic exercises. Batches are kept small so every student gets individual support during lab sessions.
Book your free demo today. No payment, no commitment. Next batch starts Nov 10, 2026.
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