Machine Learning with Python

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Reed Courses Certificate of Completion

Digital certificate - Included

• Module 01: Foundations of Machine Learning 15:21

  • 1: Foundations of Machine Learning 14:21
  • 2: Code Resources 01:00

• Module 02: Data Preprocessing & Exploratory Analysis 13:19

  • 3: Data Preprocessing & Exploratory Analysis 12:19
  • 4: Code Resources 01:00

• Module 03: Supervised Learning – Regression & Classification 12:13

  • 5: Supervised Learning – Regression & Classification 11:13
  • 6: Code Resources 01:00

• Module 04: Model Evaluation & Tuning 10:54

  • 7: Model Evaluation & Tuning 09:54
  • 8: Code Resources 01:00

• Module 05: Unsupervised Learning & Dimensionality Reduction 11:36

  • 9: Unsupervised Learning & Dimensionality Reduction 10:36
  • 10: Code Resources 01:00

Machine Learning is at the heart of today’s data-driven decision-making. This course demystifies machine learning by teaching it through Python—a widely adopted programming language in the data science ecosystem. You’ll not only learn how ML models work but also gain the skills to build and evaluate them using real-world datasets.

Module Breakdown

Module 01: Foundations of Machine Learning

• What is Machine Learning?
• Types and Applications
• Python Basics for ML: Variables, Loops, Functions
• Jupyter Notebook & Anaconda Overview
• Installing Scikit-learn, Pandas, Matplotlib

Module 02: Data Preprocessing & Exploratory Analysis

• Understanding Datasets: Types and Formats
• Data Cleaning: Handling Missing Values, Removing Duplicates
• Feature Engineering Basics
• Exploratory Data Analysis (EDA) with Pandas & Seaborn
• Encoding, Normalisation, and Scaling Techniques

Module 03: Supervised Learning – Regression & Classification

• Linear Regression (Single & Multiple)
• Logistic Regression
• k-Nearest Neighbours (kNN)
• Decision Trees and Random Forests
• Model Training, Prediction, and Interpretation

Module 04: Model Evaluation & Tuning

• Train-Test Split and Cross Validation
• Confusion Matrix, Precision, Recall, F1 Score
• ROC-AUC Curve and Model Comparison
• Hyperparameter Tuning with GridSearchCV
• Avoiding Overfitting & Underfitting

Module 05: Unsupervised Learning & Dimensionality Reduction

• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Customer Segmentation, Pattern Detection

• Foundation for roles like Data Analyst, ML Engineer, or Data Scientist
• Helps businesses utilise data for intelligent decision-making
• Builds internal capabilities for data-centric projects

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