Data Science & Machine Learning with Python

Go beyond analytics and start predicting the future. This comprehensive data science and machine learning course teaches you to build intelligent systems using Python — covering everything from exploratory data analysis to deploying machine learning models at scale.

What You Will Learn

• Python programming fundamentals and scientific computing with NumPy
• Data manipulation and analysis with Pandas and Matplotlib/Seaborn
• Supervised learning: regression, classification, decision trees, random forests
• Unsupervised learning: clustering, dimensionality reduction, PCA
• Deep learning fundamentals with TensorFlow and Keras
• Natural Language Processing (NLP) and text classification
• Model evaluation, validation, and hyperparameter tuning
• Deploy ML models as REST APIs with FastAPI and Flask

Course Modules

Module 1 — Python for Data Science

Python basics, NumPy arrays, Pandas DataFrames, data cleaning, merging datasets, handling missing values.

Module 2 — Exploratory Data Analysis

Statistical summaries, correlation analysis, outlier detection, Matplotlib & Seaborn visualisations, storytelling with data.

Module 3 — Supervised Machine Learning

Linear & logistic regression, decision trees, random forests, gradient boosting (XGBoost), support vector machines, evaluation metrics.

Module 4 — Unsupervised Learning & Feature Engineering

K-Means clustering, hierarchical clustering, PCA, t-SNE, feature selection, encoding, scaling, and pipeline automation.

Module 5 — Deep Learning

Neural network architecture, activation functions, backpropagation, CNNs for image classification, RNNs, transfer learning with pre-trained models.

Module 6 — NLP & Text Analytics

Text preprocessing, TF-IDF, word embeddings, sentiment analysis, named entity recognition, working with Hugging Face Transformers.

Module 7 — MLOps & Deployment

Model versioning with MLflow, building APIs with FastAPI, containerising with Docker, monitoring model drift in production.