Machine Learning Bootcamp

Python Programming for Data Science – Mastering Python fundamentals, NumPy arrays, Pandas DataFrames, data manipulation, and scientific computing libraries.

Mathematics for Machine Learning – Linear algebra, calculus, probability, statistics, and mathematical foundations essential for understanding ML algorithms.

Data Preprocessing & Feature Engineering – Data cleaning, handling missing values, feature scaling, encoding categorical variables, and feature selection techniques.

Exploratory Data Analysis (EDA) – Statistical analysis, data visualization with Matplotlib and Seaborn, identifying patterns, and deriving insights from data.

Supervised Learning Algorithms – Implementing linear regression, logistic regression, decision trees, random forests, SVM, and k-NN for classification and regression.

Unsupervised Learning Techniques – Clustering algorithms (K-means, hierarchical), dimensionality reduction (PCA, t-SNE), and anomaly detection methods.

Model Evaluation & Validation – Cross-validation, confusion matrix, precision-recall, ROC curves, and metrics for assessing model performance.

Neural Networks Fundamentals – Understanding perceptrons, activation functions, backpropagation, gradient descent, and building neural networks from scratch.

Deep Learning with TensorFlow & Keras – Building and training deep neural networks, CNNs for image processing, RNNs for sequential data, and transfer learning.

Convolutional Neural Networks (CNNs) – Image classification, object detection, image segmentation using architectures like ResNet, VGG, and YOLO.

Recurrent Neural Networks (RNNs & LSTMs) – Sequence modeling, time series forecasting, natural language processing, and sentiment analysis.

Natural Language Processing (NLP) – Text preprocessing, word embeddings, transformers, BERT, and building NLP applications like chatbots and text classifiers.

Model Optimization & Hyperparameter Tuning – Grid search, random search, Bayesian optimization, and techniques for improving model accuracy.

Ensemble Methods – Boosting algorithms (XGBoost, LightGBM, CatBoost), stacking, and combining multiple models for improved predictions.

MLOps & Model Deployment – Deploying ML models using Flask, fa-solidtAPI, Docker, cloud platforms (AWS SageMaker, Google AI Platform), and monitoring in production.

Computer Vision Applications – Building real-world CV applications including face recognition, object tracking, and image enhancement using OpenCV and deep learning.

Time Series Forecasting – ARIMA, Prophet, and LSTM-based forecasting for financial data, sales prediction, and demand forecasting.

Reinforcement Learning Basics – Q-learning, policy gradients, and introduction to training agents for sequential decision-making problems.

Capstone Project – End-to-end ML project from data collection and preprocessing through model development, evaluation, deployment, and presentation.