Machine Learning Training for Professionals

Build the Python foundation required for every ML project.

• Python syntax, functions, OOP basics
• NumPy: array operations, broadcasting
• Pandas: DataFrames, merge, groupby, apply
• Matplotlib & Seaborn: visualizing distributions and correlations
• Jupyter workflow for ML projects

Understand the math that powers ML algorithms — without a PhD.

• Linear algebra: matrix multiplication, SVD
• Calculus: gradient, partial derivatives, chain rule
• Probability: Bayes theorem, distributions
• Optimization: gradient descent variants

Master all major supervised learning algorithms with theory, intuition, and hands-on implementation.

• Linear and Polynomial Regression
• Logistic Regression and classification metrics
• Decision Trees and Random Forests
• Gradient Boosting: XGBoost, LightGBM, CatBoost
• Support Vector Machines
• Model evaluation: precision, recall, F1, AUC

Discover patterns and structure in unlabelled data.

• K-Means, DBSCAN, Hierarchical Clustering
• PCA and t-SNE for dimensionality reduction
• Anomaly detection methods
• Market segmentation use case

Build and train neural networks using TensorFlow and Keras.

• Neural network architecture and backpropagation
• CNNs for image classification
• RNNs and LSTMs for time series and NLP
• Transfer learning: fine-tuning pre-trained models
• Hyperparameter tuning with Keras Tuner

Deploy your ML model to production and showcase it in your portfolio.

• MLflow for experiment tracking
• FastAPI for model serving
• Docker containerization
• Capstone: end-to-end ML project deployed on cloud