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Machine Learning

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This course offers a practical introduction to Machine Learning using Python. Learn how machines learn from data, build predictive models, and apply ML to real-world problems .Learn how to prepare data, choose the right algorithms, and evaluate models — all through hands-on practice.

Who is this course for?

  • Aspiring Data Analysts or Data Scientists

  • Professionals switching to AI/ML roles

  • Students of Computer Science, Engineering, or Statistics

  • Anyone curious to build intelligent systems using real data

What You Will Learn:

  • Key ML concepts: supervised & unsupervised learning

  • Core algorithms & when to use them

  • Data preparation, model training, testing, and tuning

  • Practical use of Python’s scikit-learn for building models

  • How to evaluate model performance with real-world datasets

Detailed Course Curriculum

Module 1: Introduction to Machine Learning

  • What is ML? Why it matters in today’s world

  • ML vs Traditional Programming

  • Applications of ML in business, governance & everyday life

Module 2: Types of Learning

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning (brief overview)

  • Use-case-based comparisons

Module 3: Understanding Data for ML

  • Features vs Labels

  • Structured vs Unstructured Data

  • Dataset splitting (Train/Test/Validation)

  • Introduction to preprocessing & feature engineering

Module 4: Supervised Learning Algorithms

  • Linear Regression: Predicting continuous outcomes

  • Logistic Regression: Binary classification

  • Decision Trees & Random Forests: Interpretable models

  • K-Nearest Neighbors (KNN): Distance-based prediction

  • Support Vector Machines (SVM): High-dimensional classification

Module 5: Unsupervised Learning Basics

  • Clustering with K-Means

  • Dimensionality Reduction with PCA

  • When & where unsupervised learning is used

Module 6: Model Evaluation & Tuning

  • Evaluation Metrics: Accuracy, Precision, Recall, F1, ROC

  • Confusion Matrix Explained

  • Overfitting vs Underfitting

  • Cross-Validation techniques

  • Hyperparameter Tuning Basics

Module 7: ML Workflow with Python (Hands-On)

  • Using scikit-learn to build ML models step-by-step

  • Practical walkthrough of a classification project

  • Pipelines for real-world use

Hands-on Project:

Learn end-to-end: data cleaning → model training → evaluation → results interpretation.

Prerequisites:

  • Basic understanding of Python

  • Comfort with NumPy, Pandas, and basic plotting (Matplotlib/Seaborn)

  • Logical thinking and interest in problem-solving

Certification

Earn a Certificate of Completion powered by DigData — trusted by professionals, valued by employers, and aligned with real-world industry needs.

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