Course Overview
This learning path provides a comprehensive introduction to the fundamental principles of machine learning. You will explore supervised and unsupervised learning, understand common algorithms, and learn how to evaluate and deploy ML models.
Modules
Module 1 Progress
75%
Introduction to Machine Learning
Understand what machine learning is, its different types, and real-world applications.
- What is Machine Learning?
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
- Key Terminology (Features, Labels, Models)
- Common Use Cases and Applications
Module 2 Progress
90%
Data Preprocessing and Exploration
Learn how to prepare your data for ML models, including cleaning, transformation, and feature engineering.
- Understanding Datasets
- Data Cleaning and Handling Missing Values
- Feature Scaling and Normalization
- Exploratory Data Analysis (EDA)
- Introduction to Feature Engineering
Module 3 Progress
50%
Supervised Learning: Regression
Dive into supervised learning techniques for predicting continuous values.
- Linear Regression
- Polynomial Regression
- Evaluating Regression Models (MSE, R-squared)
- Regularization Techniques (Lasso, Ridge)
import numpy as np
from sklearn.linear_model import LinearRegression
# Sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 5, 4, 5])
# Create a linear regression model
model = LinearRegression()
model.fit(X, y)
# Predict
print(f"Prediction for X=6: {model.predict([[6]])[0]}")
Module 4 Progress
60%
Supervised Learning: Classification
Explore algorithms used for predicting categorical labels.
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Decision Trees
- Evaluating Classification Models (Accuracy, Precision, Recall, F1-score)
Module 5 Progress
30%
Unsupervised Learning
Discover methods for finding patterns in unlabeled data.
- Clustering (K-Means)
- Dimensionality Reduction (PCA)
- Association Rule Learning
Module 6 Progress
0%
Model Evaluation and Improvement
Understand how to assess model performance and prevent overfitting.
- Cross-Validation
- Bias-Variance Tradeoff
- Hyperparameter Tuning
- Ensemble Methods (Bagging, Boosting)