OFP-TR / introduction-to-machine-learning

Welcome to our Introduction to Machine Learning Course Repo!

You can find more information about our Introduction to Machine Learning Course by visiting Course Website.

Syllabus

Lesson 1 - Introduction

• What is Machine Learning?
• Machine Learning Disambiguation
• Types of Machine Learning
• Machine Learning Algorithms
• Machine Learning Applications
• Mathematics in Machine Learning
  • Lineer Algebra
  • Probability
  • Statistics
• Data Science
  • What is Data Science?
  • Feature Engineering
• End-to-End Model Training Steps
  • Seeing the Big Picture
  • Data Collection
  • Exploratory Data Analysis(EDA) and Visualization
  • Data Preprocessing
  • Model Selection and Model Training
  • Success Metrics
  • Deployment
• Machine Learning Terminology
  • Cross-Validation
  • Bias/Variance Tradeoff
  • Early Stopping
  • Epoch
  • Batch size
• Tools Used in Machine Learning
  • Python
  • NumPy
  • Pandas
  • Matplotlib
  • Sci-kit Learn
• Datasets
  • Kaggle
  • UCI

Lesson 2 - Regression

• What is Regression?

• Regression Types

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression

• Measuring the Performance of Our Regression Model

  • Error Concept
  • R²
  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)

• Errors That May Be Encountered in Model Training

  • Underfitting & Overfitting
  • Bias/Variance Tradeoff

• Error Reduction Methods

  • Train-test-validation Split
  • Early Stopping
  • Gradient Descent
  • Regularization
    • L1 Lasso
    • L2 Ridge
  • Hyper-parameter Definitions
  • Cross Validation

• Project 1: Sepal Length Estimation with Iris Dataset – Regression Project

Lesson 3 - Classification

• What is Classification?

• Logistic Regression

• Activation Functions

  • Sigmoid Function
  • Softmax Function

• Measuring the Performance of Classification Model

  • Error Concept
  • Confusion Matrix
  • Accuracy, Precision, Recall, F1 Score
  • Classification Threshold
  • ROC(Receiver Operating Characteristics) & AUC (Area Under the Curve)

• Commonly Used Classification Algorithms

  • K-Nearest Neighbors
  • Support Vector Machine
  • Decision Trees

• Project 2: Prediction of Cancer with the Breast Cancer Dataset – Classification Project

Lesson 4 - Decision Trees

• What are Decision Trees?
• Decision Trees Application
• How Are Decision Trees Calculated?
• Decision Trees Advantages
• Information Gain
• Entropy
• Gini Index
• Visualization of Decision Trees
• Bagging
• Boosting
• XGBoost

Lesson 5 - Unsupervised Learning

• What is Unsupervised Learning?
• Why Use Unsupervised Learning?
• Unsupervised Learning Algorithms
• Visualization and Dimension Reduction
  • Principal Component Analysis (PCA)
  • t-SNE
• What is Clustering?
• Clustering Types
  • Affinity Propagation
  • Hierarchical Cluster Analysis (HCA)
  • Density-based Spatial Clustering (DBSCAN)
  • Centroid-based
• K-Means Clustering
  • Elbow Method
  • Mini-Batch K-Means