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

Language

  • English

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

EDA

NumPy arrays

Model evaluation

Seaborn

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

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. 

Brief Course Description - 8 modules

  • 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. 

Previous

Python for Machine Learning