/introduction-to-machine-learning

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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