AI & ML Training Syllabus & Modules
Complete dynamic pacing topics, hand-on tools, and project milestones.
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Detailed Syllabus
Below is the comprehensive, module-by-module curriculum. As this training is strictly 1-to-1, we can adjust the syllabus scope or spend more time on specific modules based on your learning speed.
Course Prerequisites
Basic Python programming concept. Linear Algebra and Calculus basics are helpful but fully covered in our math modules.
Full Curriculum Structure
Module 1: Mathematical Foundations & Classical Machine Learning
4 Weeks- Linear Algebra, Multivariable Calculus & Probability for AI
- Feature Engineering, Data Preprocessing & Scikit-Learn 1.5
- Supervised Learning: Regression, Classification, SVM & Random Forests
- Model Evaluation: Confusion Matrices, ROC-AUC & Hyperparameter Tuning
Module 2: Deep Learning & Computer Vision with PyTorch 2.4
4 Weeks- Neural Network Architecture, Backpropagation & Loss Functions
- Building Custom Neural Networks with PyTorch 2.4 Tensors
- Convolutional Neural Networks (CNNs) for Image Recognition
- Transfer Learning with ResNet and Vision Transformers
Module 3: NLP & Generative AI with Large Language Models
4 Weeks- Word Embeddings, Attention Mechanism & Transformer Architecture
- Fine-Tuning Open Source LLMs (Llama 3.1, Mistral) with LoRA/QLoRA
- Running Local LLMs via Ollama and vLLM Inference Engines
- Hugging Face Transformers Pipeline Implementation
Module 4: LangChain 0.3, RAG & Live AI Project Internship
4 Weeks- Retrieval-Augmented Generation (RAG) with LangChain 0.3 & LlamaIndex
- Vector Embeddings & Semantic Search with ChromaDB & Pinecone
- Building Autonomous AI Agents with Tool Calling Capabilities
- Live Enterprise Generative AI Application Project Internship
Tools & Technologies Mastered
You will gain hands-on operational capability in these tools during screenshare coding loops, creating real repositories.
Hands-on Lab Assignments & Projects
- Project 1: Real estate pricing predictor using Linear Regression and Scikit-Learn.
- Project 2: Customer Churn Classification app using Decision Trees & Random Forests.
- Project 3: Image Classifier using Convolutional Neural Networks (CNN) in TensorFlow.
- Project 4: Custom API Deployment of an NLP text classification system.
Syllabus FAQs
When does the next 1-to-1 training intake start?
Intakes start twice monthly on the 1st and 15th. The next upcoming 1-to-1 intake starts on October 1, 2026 (with secondary intake on October 15, 2026).
Is the AI & Machine Learning syllabus updated for modern industry standards?
Yes. Our syllabus is continuously updated to cover the latest versions of PyTorch, TensorFlow, Scikit-Learn, OpenCV, Hugging Face, LLMs and modern production software engineering practices.
Can the syllabus be customized for my current skill level?
Because all sessions are strictly 1-to-1, your mentor can adjust module depth or accelerate topics based on your existing knowledge and target career goals.
Does the syllabus focus on theoretical concepts or hands-on coding?
Over 80% of training time is dedicated to live hands-on coding, terminal execution, pull request reviews, and building production applications.
Student Success Stories
Real feedback from students who completed our 1-to-1 virtual AI & ML Training training.
"Upgrading to AI and ML was seamless due to the personalized attention. My mentor helped me understand the mathematical foundations behind SVM and Random Forests, and we implemented neural networks in Keras. Great coding practice."
Vikram S.
AI Engineer (Formerly Data Analyst), Hinjewadi, Pune"I did my final year project on NLP, but CACTS gave me the industry perspective. We built and deployed custom models using FastAPI. The trainer's knowledge of Scikit-Learn pipelines and TensorFlow was impressive."
Snehal J.
ML Research Assistant, DY Patil Pune"Having a dedicated mentor to help debug my model training and explain hyperparameter tuning saved me months of struggle. The live project pipelines on company data were super challenging but rewarding."