Machine Learning Syllabus

1. Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
• Applications of Machine Learning
• Tools and Libraries (scikit-learn, pandas, NumPy, Matplotlib)

2. Getting Started

• Setting up your environment (Jupyter Notebook, Python)
• Understanding datasets
• Loading and inspecting data (CSV, Excel, etc.)
• Basic data operations

3. Descriptive Statistics

• Mean, Mode, and Median
  • Definition and importance
  • Calculating these measures in Python
• Standard Deviation and Variance
  • Understanding data spread
  • Using Python to compute standard deviation and variance
• Percentile
  • What are percentiles?
  • Finding percentiles using Python

4. Data Distribution

• Normal distribution
• Visualizing data distribution (Histograms, Bell curves)
• Skewness and kurtosis
• Identifying outliers

5. Regression Analysis

• Linear Regression
  • Simple linear regression concept
  • Implementing linear regression in Python
  • Plotting linear regression with Matplotlib
• Polynomial Regression
  • Introduction to polynomial regression
  • Implementing and visualizing polynomial regression
• Multiple Regression
  • What is multiple regression?
  • Handling multiple features for prediction
  • Implementing multiple regression in Python

6. Data Preprocessing

• Scaling Data
  • Importance of scaling features
  • Techniques: Min-Max Scaling, Standard Scaling
• Splitting Data (Test/Train)
  • Importance of training and test sets
  • Using scikit-learn’s train_test_split
• Categorical Data
  • Encoding categorical variables (One-Hot Encoding, Label Encoding)

7. Model Evaluation and Tuning

• Cross-Validation
  • K-Fold Cross-validation explained
  • Implementing cross-validation in Python
• Grid Search
  • What is Grid Search?
  • Using Grid Search for hyperparameter tuning
• Confusion Matrix
  • Understanding true positive, true negative, false positive, and false negative
  • Visualizing confusion matrices with Python

8. Classification Techniques

• Logistic Regression
  • Introduction to logistic regression
  • Differences between linear and logistic regression
  • Implementing binary classification with logistic regression
• K-Nearest Neighbors (KNN)
  • Concept of KNN algorithm
  • Implementing KNN in Python
  • Visualizing KNN results
• Decision Making with Decision Trees
  • How decision trees work
  • Implementing decision trees for classification
  • Visualizing decision trees

9. Clustering

• K-Means Clustering
  • Introduction to K-Means algorithm
  • Choosing the right number of clusters (Elbow method)
  • Implementing K-Means with scikit-learn