AI & Machine Learning with Python Course in Botanical Garden, Noida

Main Topic 1: Advanced Python & Vectorization — NumPy Matrix Operations, Tensor Structures, Pandas DataFrames & Scipy Main Topic 2: Linear Algebra & Calculus — Vectors, Matrices, Eigenvalues, Gradient Descent, Partial Derivatives Sub-topic: Probability & Statistics — Bayes Theorem, Probability Distributions, Maximum Likelihood Estimation (MLE) Practical Capstone: Implementing Gradient Descent algorithm from scratch in Python

Main Topic 1: Regression & Classification — Linear/Polynomial Regression, Logistic Regression, Support Vector Machines (SVM) Main Topic 2: Ensemble Learning Algorithms — Decision Trees, Random Forests, XGBoost, LightGBM, Gradient Boosting Sub-topic: Clustering & Dimensionality Reduction — K-Means, Hierarchical Clustering, PCA, t-SNE Practical Capstone: Building a Predictive Customer Churn & Fraud Detection Model

Main Topic 1: Artificial Neural Networks (ANN) — Perceptrons, Activation Functions (ReLU, Softmax), Backpropagation, Loss Functions Main Topic 2: Convolutional Neural Networks (CNN) — Image Processing, Convolutional Layers, Max Pooling, Transfer Learning (ResNet, VGG) Sub-topic: Recurrent Neural Networks (RNN) — Sequence Modeling, LSTM, GRU networks for Time Series Forecasting Practical Capstone: Computer Vision Medical Image Classification System with CNN

Main Topic 1: Natural Language Processing (NLP) — Tokenization, TF-IDF, Word Embeddings (Word2Vec, GloVe), Transformers Main Topic 2: Generative AI & RAG Architecture — Open AI APIs, LangChain, LlamaIndex, Vector Databases (Pinecone, ChromaDB), Prompting Practical Capstone: Enterprise Custom RAG AI Chatbot Deployed with LangChain & OpenAI

Model evaluation and performance metrics Feature engineering and data preprocessing Model optimization and validation techniques Practical implementation using machine learning libraries

Building real-world machine learning projects Predictive model development using datasets Data-driven decision systems End-to-end AI workflow implementation