Join Amit Kulkarni, an industry expert and certified AI specialist, as he walks you through the Mastering GenAI course at Sunbeam Pune. This program is designed to help you understand, build, and innovate with AI-powered solutions.

Part I: Machine Learning Foundations

• Module 1 — Statistical Foundations for Machine Learning
  • Types of data & measurement scales
  • Descriptive statistics (mean, variance, skewness, kurtosis)
  • Probability theory & conditional probability
  • Random variables & expectation
  • Common distributions (Normal, Binomial, Poisson)
  • Central Limit Theorem
  • Sampling techniques & sampling bias
  • Confidence intervals
  • Hypothesis testing
  • p-value interpretation & statistical significance
• Module 2 — Linear Algebra & Optimization for ML
  • Vectors, matrices & tensor intuition
  • Matrix multiplication & transformations
  • Eigenvalues & eigenvectors
  • Gradient descent intuition
  • Convex vs non-convex optimization
  • Cost functions & loss landscapes
• Module 3 — Python for Data Science & EDA
  • NumPy numerical computing
  • Pandas data manipulation
  • Data visualization (Matplotlib / Seaborn / Plotly)
  • Handling missing values & outliers
  • Feature scaling & encoding
  • Correlation analysis & feature importance
  • Automated EDA tools
• Module 4 — Supervised Machine Learning
  • Linear regression & regularization (Ridge/Lasso)
  • Logistic regression
  • Decision Trees
  • Random Forest
  • Gradient Boosting (XGBoost / LightGBM overview)
  • K-Nearest Neighbors
  • Support Vector Machines
• Module 5 — Unsupervised Learning & Representation
  • K-Means clustering
  • Hierarchical clustering
  • DBSCAN
  • Dimensionality reduction (PCA, t-SNE intuition)
  • Anomaly detection techniques
• Module 6 — Model Evaluation & Improvement
  • Train-test split & cross validation
  • Bias-variance tradeoff
  • Overfitting & underfitting
  • Hyperparameter tuning
  • Feature selection
  • Handling imbalanced datasets
• Capstone Projects
  • Customer churn prediction
  • Recommendation system
  • Fraud detection / risk prediction

Part II: Deep Learning & Generative AI

• Module 1 — Deep Learning Fundamentals
  • Biological vs artificial neurons
  • Perceptron & multilayer networks
  • Activation functions
  • Loss functions
  • Backpropagation mathematics intuition
  • Gradient descent variants
  • Regularization techniques
• Module 2 — Deep Learning Frameworks & Training
  • PyTorch fundamentals
  • Dataset & dataloader design
  • GPU acceleration basics
  • Model training pipelines
  • Debugging deep learning models
• Module 3 — Computer Vision
  • Convolution & pooling
  • CNN architectures intuition
  • Transfer learning
  • Image classification project
  • Object detection overview
• Module 4 — NLP & Sequence ModelsRecurrent Neural Networks (RNNs)
  • Text preprocessing
  • Word embeddings
  • RNN / LSTM / GRU intuition
  • Attention mechanism
• Module 5 — Transformers & Large Language Models
  • Transformer architecture deep dive
  • Encoder vs decoder models
  • Pretraining vs fine-tuning
  • Tokenization strategies
  • HuggingFace ecosystem
• Module 6 — Generative AI Systems
  • Prompt engineering
  • Embeddings & semantic search
  • Vector databases
  • Retrieval Augmented Generation (RAG)
  • Building LLM chatbots
• Module 7 — AI Agents & Production AI
  • Tool calling & agent workflows
  • Multi-step reasoning systems
  • AI app deployment (FastAPI and Streamlit)
  • Safety & guardrails
• Capstone Projects
  • RAG-based document assistant
  • AI chatbot / copilot
  • Vision or NLP deep learning project

• Beginners with basic python knowledge

1. Amit Kulkarni Sir is truly the best in this field. He explains the basics of technology, which is rare, and makes difficult concepts easy to understand. His syllabus is market-oriented, covering the latest trends.
   — Amruta Deole, Senior Software Developer
    
2. The hands-on projects, prompt engineering techniques, and deployment strategies were especially valuable. A great choice for both freshers and professionals aiming to upskill in Gen AI.
   — Rahul Kulkarni, Data Analyst & ML/DL Developer
    
3. Amit Sir answered thousands of questions with a smile. His passion for teaching and deep expertise inspired me. The sessions were technically sound and full of real-world insights.
   — Sanket Gawali, Solution Developer
    
4. The course provided a strong foundation, progressing seamlessly from basics to advanced topics like RAG and LLMs. The balance of theory and practice was perfect.
   — Shivani Bhinge, Associate Data Science Engineer
    
5. This was a zero-to-hero Generative AI course. I can now make an impact in my work using AI. Amit Sir’s humility and support even after the course are unparalleled.
   — Chaitanya Takalikar, Software Engineer