Data Science +
Generative AI Course
In Hyderabad

Master the mathematical foundations of Data Science - essential for ML model evaluation, feature analysis, and business decision-making.

• Types of Data, Population vs Sample, Sampling Techniques
• Descriptive Statistics - Central Tendency, Variability, Distribution Shape
• Graphical Visualization (Histogram, Boxplot, Scatterplot)
• Inferential Statistics - Confidence Intervals, Parameter Estimation
• Hypothesis Testing (Null/Alternative, p-value, t-test, Chi-square, ANOVA)
• Regression Analysis (Linear, Logistic) & Correlation (Pearson, Spearman)
• Probability Basics - Sample Space, Events, Random Variables
• Permutations & Combinations, Conditional Probability
• Bayes' Theorem with real-world examples (spam filter, medical test)
• Probability Distributions (Binomial, Poisson, Normal, Uniform)

Hands-on with the core Python data ecosystem used in every Data Science and ML project.

• NumPy - Arrays, vectorized operations, linear algebra basics
• Pandas - DataFrames, data loading, filtering, groupby, merging
• Matplotlib & Seaborn - Visualizations for EDA (charts, heatmaps)
• Exploratory Data Analysis (EDA) on real datasets

Learn the full ML pipeline - from data preprocessing and model training to evaluation and deployment concepts with real industry use cases.

• Types of ML - Supervised, Unsupervised, Reinforcement Learning
• Confusion Matrix, Accuracy, Precision, Recall, F1-Score
• Data Cleaning - Missing values, Duplicates, Outlier Detection
• Feature Encoding (LabelEncoder, OneHotEncoder) & Scaling
• Linear Regression, Logistic Regression, Regularization (Ridge, Lasso)
• Naive Bayes, k-NN, Decision Trees (Gini, Entropy, Pruning)
• Ensemble Methods - Random Forest, AdaBoost, XGBoost, LightGBM
• Support Vector Machines (Linear & Non-linear, Kernel Trick)
• Feature Engineering, PCA, t-SNE, Hyperparameter Tuning
• NLP Basics - Tokenization, TF-IDF, Text Classification
• Recommendation Systems - Collaborative & Content-based Filtering
• Capstone: End-to-End ML Project (Numerical + Text Features)

Understand and build the neural network architectures that power modern AI - from basic ANNs to Transformers and Generative models.

• Artificial Neural Networks (ANNs) - Neuron structure, Activation Functions
• Deep Learning Foundations - Forward Pass, Backpropagation, Loss Functions
• Optimizers - SGD, Adam, RMSProp
• Convolutional Neural Networks (CNNs) - Architecture, Pooling, Transfer Learning
• Popular CNN Models - LeNet, AlexNet, VGG, ResNet
• Recurrent Neural Networks (RNNs) - Sequence Modeling, Vanishing Gradients
• LSTMs - Gates, Memory Cells; GRUs as simpler alternative
• Generative Adversarial Networks (GANs) - Generator vs Discriminator
• Variational Autoencoders (VAEs) - Encoding, Latent Representations
• Transformers - Attention Mechanism, Encoder-Decoder Structure
• Pretrained Models - BERT, GPT, Vision Transformers

The most in-demand section - build production-grade AI applications using LLMs, Hugging Face, LangChain, RAG pipelines, and autonomous AI Agents.

• Introduction to Generative AI - Text, Image, Audio, Video, Code generation
• Large Language Models (LLMs) - Pre-training, Fine-tuning, RLHF
• Word Embeddings - Word2Vec, GloVe, Contextual Embeddings
• Hugging Face Hub - Exploring and using pre-trained models
• Hugging Face Pipelines - Sentiment, Summarization, Q&A tasks
• Fine-Tuning Models on Hugging Face Trainer API (Text Classification)
• LangChain - PromptTemplate, LLM, Output Parsers, Sequential Chains
• LangChain Memory - ConversationBuffer, ConversationSummary for Chatbots
• LangChain Agents - Tools, Multi-tool Agents, Planning vs Reactive
• RAG (Retrieval-Augmented Generation) - Architecture & Workflow
• Vector Databases - FAISS, Chroma for Embeddings Storage
• RAG Implementation - Document Loaders, Splitters, Retriever, Q&A Chain
• Evaluating RAG Performance (Precision, Recall)
• Building Multi-turn Chatbots with Long-term Memory

Master the art and science of communicating effectively with Large Language Models for consistent, reliable, and production-ready outputs.

• Principles of effective prompting for LLMs
• Zero-shot, Few-shot, and Chain-of-Thought prompting
• Structured outputs - JSON, tables, formatted responses
• Prompt optimization, evaluation, and iteration techniques
• Real-world prompt templates for business use cases