Python for Machine Learning 

This course transforms you from a Python beginner into a machine learning practitioner, covering data wrangling, visualization, and model building with tools like Pandas, Seaborn, and Scikit-learn. You'll apply your skills through hands-on projects that solve real business problems and deliver actionable insights.

Instructor: Ridwan Ibidunni

Available to members only

Prerequisite

  • Computer basics

  • Basic math

  • Google account

  • Growth mindset

  • No coding/ML background

Modules & Duration

  • 8 modules

  • 7 weeks + final project

Assignments

  • 14 assignments

  • 4 projects

What you'll learn

Core Python for ML: Master Python control structures, data structures (lists, dictionaries), and OOP principles to build scalable ML scripts. 

End-to-End ML Workflows: Implement supervised (regression, classification) and unsupervised (clustering) models using Scikit-learn pipelines.

Data Visualization & Preprocessing: Visualize trends with Matplotlib/Seaborn and preprocess data using Pandas/NumPy for real-world datasets (e.g., Titanic, COVID-19). 

Model Evaluation: Select metrics (MSE/R² for regression; precision/recall for classification) and diagnose bias-variance tradeoffs. 

Data Wrangling: Clean raw data by handling outliers, normalizing features, encoding categorical variables, and splitting datasets. 

Production-Ready Models: Optimize models via cross-validation, hyperparameter tuning (GridSearchCV), and ML pipelines.

Skills you’ll gain

Python scripting

Pair plots 

Scikit-learn

Variable

Data structure

NumPy

Supervised and unsupervised ML with Scikit-learn

Pandas

Control statement

Matplotlib

Pandas DataFrames

Pipelines

Jupyter

Cross-validation

Data cleaning

Matplotlib charts

hyperparameter tuning

MSE, MAE, R², accuracy, precision, recall 

Pipelines structuring

Seaborn heatmaps

Google Colab

Add certificate to your LinkedIn profile

Taught in English

EDA

NumPy arrays

Model evaluation

Seaborn

Brief Course Description - 8 modules

Python is the backbone of modern machine learning, powering everything from data preprocessing to deep learning. This course transforms you from a Python beginner into an ML-ready practitioner. You’ll master data wrangling with Pandas/NumPy, visualize insights with Matplotlib/Seaborn, and build production-grade models using Scikit-learn. Through hands-on projects—like predicting diabetes progression or segmenting customers—you’ll tackle real business problems and learn to deploy solutions that drive decisions. Enroll today to turn data into actionable intelligence. 

  • What you'll learn:

    Write Python scripts using variables, loops, functions, and OOP. 
    Includes: 

    • 2 hours of live virtual lectures.

    • Hands-on: Grade calculator, student database.

    • Assignment: Build a factorial script 

  • What you'll learn:

    Manipulate arrays, compute statistics, and handle multi-dimensional data. 
    Includes: 

    • 2 hours of live video lectures 

    • Hands-on: Matrix multiplication, sensor data analysis 

    • Assignment: 3D array normalization 

  • What you'll learn:

    Clean, aggregate, and merge datasets for EDA. 
    Includes: 

    • 2 hours of live virtual lectures 

    • Hands-on: Titanic dataset cleaning, COVID-19 recovery rate calculation 

    • Assignment: Sales data analysis

  • What you'll learn:

    Create line/bar plots, heatmaps, and pairplots to uncover trends. 
    Includes: 

    • 2 hours of live virtual lectures 

    • Hands-on: Monthly sales charts, Iris dataset correlations 

    • Assignment: EDA report with 5+ plots 

    • Tools: Seaborn, Matplotlib 

  • What you'll learn:

    Scale features, encode categories, and prevent data leakage. 
    Includes: 

    • 2 hours of live virtual lectures 

    • Hands-on: House price standardization, gender encoding 

    • Assignment: Preprocess banking data for ML 

    • Tools: Scikit-learn pipelines 

  • What you'll learn:

    Train models like logistic regression and compare frameworks. 
    Includes: 

    • 2 hours of live virtual lectures 

    • Hands-on: Iris classification, MNIST digit recognition 

    • Assignment: Build a diabetes predictor 

    • Tools: Scikit-learn 

  • What you'll learn:

    Optimize models using cross-validation and hyperparameter tuning. 
    Includes: 

    • 2 hours of live virtual lectures 

    • Hands-on: GridSearchCV, learning curves 

    • Assignment: Churn prediction model 

    • Dataset: Telecom customer data 

  • What you'll learn:

    Integrate all skills into an end-to-end ML workflow. 
    Includes: 

    • Guided project: Diabetes progression prediction 

    • Tasks: Data exploration → preprocessing → model tuning → evaluation 

    • Deliverable: Jupyter notebook with actionable insights 

    • Grading: Functionality (40%), EDA (20%), tuning (10%) 

Why This Course Stands Out 

Real-World Projects: Tackle datasets from healthcare (diabetes), finance (churn), and retail (customer segmentation).

Industry Tools: Use Scikit-learn, Numpy, and Pandas in every module. 

Career Focus: Build a portfolio with 4 projects to showcase on LinkedIn. 

Next

Applied Classical Machine Learning