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Machine Learning/Learning Center

Machine Learning Learning Center

Master machine learning through comprehensive tutorials, from fundamental concepts to advanced algorithms, with practical examples and real-world applications

Featured Learning Modules

Introduction to Machine Learning
Featured
Comprehensive introduction covering ML fundamentals, terminology, model evaluation, and classical algorithms with practical watermelon dataset examples throughout.
Beginner to Intermediate4-6 hours

Topics Covered:

ML Overview & Applications
Basic Terminology & Concepts
Model Evaluation & Selection
Classical ML Algorithms
Linear Models
New
Master linear regression, logistic regression, LDA, and advanced techniques including multi-class classification and handling class imbalance with real-world examples from housing, credit, and fraud detection.
Beginner to Intermediate6-8 hours

Topics Covered:

Linear Regression & OLS
Logistic Regression & Sigmoid
Linear Discriminant Analysis
Multi-Class Strategies
Class Imbalance Solutions
SMOTE & Regularization
Decision Trees
New
Master decision tree algorithms including ID3, C4.5, and CART. Learn information gain, Gini index, pruning techniques, handling continuous/missing values, and multivariate trees with practical examples from housing, credit, and customer segmentation.
Beginner to Advanced8-10 hours

Topics Covered:

Tree Structure & Building
Information Gain & ID3
Gain Ratio & Gini Index
Pre-Pruning & Post-Pruning
Continuous & Missing Values
Multivariate Decision Trees
Neural Networks & Deep Learning
New
Journey from simple perceptrons to cutting-edge deep learning. Master backpropagation, CNNs, modern architectures (ResNet, VGG), transfer learning, and real-world applications in computer vision, autonomous vehicles, and medical imaging.
Intermediate to Advanced8-10 hours

Topics Covered:

Neural Network History & Evolution
Neuron Models & Activation Functions
Multi-Layer Networks & Universal Approximation
Backpropagation & Gradient Descent
Specialized Architectures (RBF, SOM, ART, RBM)
Deep Learning & CNNs (ResNet, Transfer Learning)

Suggested Learning Paths

Beginner Path

  • Introduction to ML
  • Basic Algorithms
  • Model Evaluation

Intermediate Path

  • Supervised Learning
  • Unsupervised Learning
  • Feature Engineering

Advanced Path

  • Neural Networks
  • Deep Learning
  • Advanced Optimization

More Topics Coming Soon

Ensemble Methods
Learn powerful ensemble techniques that combine multiple models for superior performance.
Random Forests
Gradient Boosting
XGBoost
+2 more topics
Unsupervised Learning
Explore clustering, dimensionality reduction, and pattern discovery techniques for unlabeled data.
K-Means Clustering
Hierarchical Clustering
PCA
+2 more topics
Model Optimization & Deployment
Advanced techniques for model tuning, interpretability, and production deployment.
Hyperparameter Tuning
Model Interpretability
Production Deployment
+2 more topics

Why Learn Machine Learning?

Career Opportunities

ML engineers and data scientists are among the most in-demand and highest-paid professionals in tech.

Cutting-Edge Technology

Work with the latest AI technologies powering everything from recommendation systems to autonomous vehicles.

Problem-Solving Skills

Learn to tackle complex real-world problems using data-driven approaches and intelligent algorithms.

Research Impact

Multiple Turing Award winners from ML, demonstrating its fundamental importance in computer science.

Start Your Journey