IntermediateUpdated January 2025

Machine Learning Fundamentals

Build Your Foundation in Machine Learning Algorithms and Techniques

4.8(3,654 reviews)
24,600 students
8 weeks total
96 lectures

Course Structure

16
Live Teacher Sessions
16
Homework Sessions
32
Total Sessions
8
Weeks Duration
Supervised LearningUnsupervised LearningModel EvaluationFeature EngineeringCross ValidationAlgorithm Selection

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Course Overview

Build your foundation in machine learning algorithms and techniques

This comprehensive course follows a structured learning approach with 16 live teacher sessions and 16 homework sessions, ensuring you get both expert guidance and plenty of hands-on practice.

Prerequisites

  • Python programming knowledge required
  • Basic statistics and linear algebra helpful
  • Understanding of data manipulation (Pandas)
  • Mathematical curiosity and analytical thinking
  • Computer with Python ML environment
  • Basic understanding of business analytics

What You'll Learn

Machine Learning Fundamentals & Algorithm Types
Mathematical Foundations (Linear Algebra, Statistics)
Supervised Learning (Regression & Classification)
Unsupervised Learning (Clustering & Dimensionality Reduction)
Model Evaluation & Hyperparameter Optimization
Ensemble Methods & Advanced Techniques
Feature Engineering & Selection Strategies
Model Interpretability & Explainability
Ethical AI & Bias Detection
Production ML & Model Deployment

Course Projects

Predictive Analytics System

Build a comprehensive predictive analytics platform with multiple ML models

Scikit-learnXGBoostFeature EngineeringModel Interpretation
Duration: Final 2 weeks
Features: 5 components
Key Features:
  • Multi-model prediction
  • Automated feature engineering
  • Model interpretation
  • Performance monitoring
  • A/B testing framework

Customer Segmentation & Recommendation Engine

Create an intelligent customer segmentation and recommendation system

ClusteringDimensionality ReductionEnsemble MethodsRecommendation Systems
Duration: Week 6-7
Features: 5 components
Key Features:
  • Customer clustering
  • Behavioral analysis
  • Product recommendations
  • Personalization engine
  • Business intelligence dashboard

Certificate of Completion

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Machine Learning Fundamentals

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