Every day more and more use cases are found for machine learning. It is a great field to get into.

We just released a 10-hour machine learning course for beginners on the freeCodeCamp.org YouTube channel.

Ayush Singh developed this course. He is a young data scientist and machine learning engineer.

Here are the sections covered in this course:

**Section 1: Basics of Machine Learning**

- What is Machine Learning? The way I like to think about it!
- Cool Applications of Machine Learning
- Types of ML and Their Types
- Workflow of basic ML Problem
- Main Challenges of Machine Learning
- Dividing the data
- Two famous problems of Machine learning: Underfitting and Overfitting
- Solutions to the Overfitting and Underfitting
- Supervised Learning and Unsupervised Learning In Depth

**Section 2: Linear Regression & Regularization**

- What is Linear Regression? Visual Understanding
- Hypothesis Function Or Prediction Function
- Closed Form Solution aka Normal Equation
- Coding Normal Equation
- Cost Function
- Gradient Descent
- Assumptions & Pros and Cons of Linear Regression
- Regularized Linear Models
- Ridge Regression
- Lasso Regression

**Section 3: Logistic Regression & Performance Metrics**

- Logistic Regression
- Hypothesis Function
- Cost Function
- Gradient Descent
- Assumptions and Pros and Cons

**Section 4: Support Vector Machine**

- Support Vector Machines
- Linear SVM Classification
- Hard/Soft Margin Classification
- Non-Linear SVM Classification
- Polynomial Kernel [Homogenous & Inhomogeneous ]
- RBF Kernel
- Computing SVM Classifier
- Primal and Dual Problem
- Sub-Gradient Descent
- Coordinate Descent
- Transductive SVM
- SVR

**Section 5: PCA**

- Review of Linear Transformation & EigenVectors and EigenValues
- Dimensionality Reduction Need
- Basic Intuition Behind PCA
- Data Preprocessing [Data Standardization]
- Compute the Covariance Matrix
- Compute the cumulative energy content for each eigenvector
- Select a subset of the eigenvectors as basis vectors
- Projecting Back

**Section 6: Learning Theory**

- Bias and Variance TradeOff
- Approx Estimation Error
- Empirical Risk Minimization
- Problem Sets releases

**Section 7: Decision Trees & Random Forest**

- Decision Trees
- Training of Decision Trees
- Prediction in Decision Trees
- Entropy
- Information Gain
- Gini Impurity
- Hyperparameter Tuning
- Project Proposal
- Decision Trees Assignment
- Ensemble Learning
- Ensemble Learning
- Bagging
- Random Forest
- Boosting
- Gradient Boosting
- Adaboost
- XGboost
- Stacking
- Cascading

**Section 7.5: Learning more algorithms and building more projects**

- Naive Bayes
- K-Nearest Neighbors

**Section 8: Unsupervised Learning Algorithms**

- Unsupervised Learning and Clustering Intro and Types
- Cluster Analysis
- Unsupervised Learning Algorithms
- K-Means with K-Means ++
- Hierarchical Clustering Techniques
- Unsupervised Learning Problem set and project releases
- K-Means Programming Assignment

**Section 9: Building Applications**

- Building Heart Failure Detection System with deployment
- Building Fake news detection system
- Building Email Spam Detection System

Watch the full course below or on the freeCodeCamp.org YouTube channel (10-hour watch).