Python for Machine Learning

What You'll Learn

NumPy arrays and vectorised operations

Pandas data wrangling and EDA

Scikit-learn model training and evaluation

Classification algorithms (Logistic Regression, Random Forest, SVM)

Regression algorithms (Linear, Ridge, Lasso, Gradient Boosting)

Clustering with K-Means and DBSCAN

Feature engineering and selection

Hyperparameter tuning (GridSearchCV, RandomizedSearchCV)

Model evaluation metrics and cross-validation

ML pipelines and model deployment with Flask

Course Content

7 modules · 25 chapters

NumPy Arrays & Vectorised Operations Preview

Pandas – Data Wrangling for ML Preview

Exploratory Data Analysis (EDA)

Course Description

Master the essential Python libraries for machine learning. Build production-quality models covering the complete ML pipeline — from raw data to deployed API.

You will work with NumPy, Pandas, Scikit-learn, Matplotlib, XGBoost, and more, applying real-world datasets to classification, regression, clustering, and deep-learning tasks.

Your Instructor

A

System Administrator

Course Instructor