Machine Learning

Overview

This course introduces learners to the fundamental principles and practices of machine learning. It begins with an overview of what machine learning is and how it works, followed by essential statistics and probability concepts needed for understanding models. The curriculum progresses into supervised learning techniques like regression and classification, including decision trees, random forests, and support vector machines. Learners will also explore unsupervised learning methods such as clustering and dimensionality reduction. A mini-project on house price prediction reinforces these skills through practical application.

Objectives

• Understand the core concepts of machine learning and how to apply different categories of ML algorithms.
• Learn statistical foundations including probability distributions, sampling, and inference essential for modeling.
• Apply regression and classification models such as linear, polynomial, logistic, and tree-based methods.

Key topics

• Introduction to Machine Learning
• Regression and Classification Techniques
• Decision Trees and Ensemble Methods
• Unsupervised Learning with Clustering

Expected outcomes

• Build and evaluate predictive models using regression and classification techniques.
• Perform exploratory data analysis and preprocess data for machine learning tasks.
• Implement clustering and dimensionality reduction methods to uncover patterns in data.

How the AI Tutor can help

How the Skillama AI Tutor helps with the listed lectures: explanations, follow-up questions, and running or debugging practice code.

Who this is for

This course is for learners seeking to understand machine learning fundamentals and apply them through hands-on practice.

Curriculum

1. 1. Introduction to Machine learning

   • What is Machine learning
   • Machine Learning Process Flow Diagram
   • Different Categories of Machine Learning
   • SciKit-Learn Overview
   • SciKit-Learn Cheat-sheet

2. 2. Statistics and Probability

   • Descriptive Statistics
   • Central tendency: Mean, Median, Mode
   • Sample variance
   • Standard deviation
   • Random Variables: Discrete, Continuous
   • Probability density functions
   • Binomial distribution
   • Inferential Statistics
   • Central limit theorem
   • Sampling distribution of the sample mean
   • Standard error of the mean
   • Mean and varianæ of Bernoulli distribution

3. 3. Regression

   • Linear Regression
   • Exploratory Data Analysis (EDA)
   • Correlation Analysis and Feature Selection
   • Performance Evaluation - Residual Analysis, Mean Square Error (MSE), Co-efficient
   • Polynomial Regression
   • Regularized Regression — Ridge, Lasso and Elas- tic Net Regression
   • Multiple Regression
   • Data Pre-processing - Standardization, Min-Max, Normalization

4. 4. Decision Tree

   • CART (Classification and Regression Tree)
   • Advantages and Disadvantages and its applications.
   • Decision Tree Learning algorithms - ID3, C4.5, C5.O and CART
   • Gini Impurity, Entropy and Information Gain
   • Decision Tree Regression
   • Visualizing a Decision Tree using graphviz module.

5. 5. Classification

   • Bootstrap Aggregating or Bagging
   • Random Forest algorithm
   • Extremely Randomized (Extra-Trees) Ensemble
   • Boosting - AdaBoost (Adaptive Boosting), Gradient Boosting
   • Machine (GBM), XGBoost (Extreme Gradient Boosting)

6. 6. Unsupervised

   • Connectivity- based Clustering using Hierarchical Clustering.
   • Ward's Agglomerative Hierarchical Clustering
   • K-Means Clustering

7. 7. Classification — Logistic Regression

   • Sigmoid function
   • Logistic Regression learning using Stochastic Gra dient Descent (SGD) SGD Classifier
   • Measuring accuracy using Cross-Validation, Strati-fied k-fold
   • Confusion Matrix — True Positive (TP), False Posi-tive (FP), False
   • Negative (FN), True Negative (TN)
   • Precision, Recall, Fl Precision/RecaII Trade-Off
   • Receiver Operating Characteristics (ROC) Curve.

8. 8. Classification — k-Nearest Neighbor(KNN)

   • Classification and Regression
   • Application, Advantages and Disadvantages
   • Distance Metric — Euclidean, Manhattan, Cheby- shev,
   • Minkowski
   • Measuring accuracy using Cross-Validation, Stratified k-fold,
   • Confusion Matrix, Precision, Recall, F I-score.

9. 9. SVM (Support Vector Machine)

   • Separating line, Margin and Support Vectors
   • Linear SVM Classification
   • Polynomial Kernel — Kernel Trick
   • Gaussian Radial Basis Function (rbf)
   • Grid Search to tune hyper-parameters.
   • Support Vector Regression.

10. 10. Random Forest

    • Bootstrap Aggregating or Bagging
    • Random Forest algorithm
    • Extremely Randomized (Extra-Trees) Ensemble
    • Boosting — AdaBoost (Adaptive Boosting), Gradient Boosting
    • Machine (GBM), XGBoost (Extreme Gradient Boosting)

11. 11. Forecasting

    • Forecasting Error and it metrics
    • Model Based Approaches
    • AR Model for errors
    • Data driven approaches
    • Exponential Smoothing ARIMA

12. 12. Reduction Technique

    • What is Dimensionality Reduction
    • Benefits of applying Dimensionality Reduction
    • Approaches of Dimension Reduction
    • Common techniques of Dimensionality Reduction

13. 13. Common techniques of Dimensionality Reduction

    • Deep Learning Importance
    • Neural Network Overview
    • Neural Network Representation
    • Activation Function
    • Loss Function
    • Importance of Non-linear Activation Function
    • Gradient Descent for Neural Network

14. 14. Understanding Parameters and Hyperparameters

    • Train, Test & Validation Set
    • Vanishing & Exploding Gradient,Drop
    • Regularization
    • Optimization algo
    • Learning Rate
    • Tuning and Softmax

15. 15. CNN

    • CNN
    • Deep Convolution Model
    • Detection Algorithm
    • Face Recognition

16. 16. RNN

    • RNN
    • LSTM
    • Bi Directional LSTM

17. 17. Mini Project

    • House Price Prediction
    • Property Data Collection
    • Data Preprocessing
    • Exploratory Data Analysis
    • Price Prediction
    • Data Visualization
    • Report Generation