Free 10-Hour Machine Learning Course

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).

[embedded content]

Posted by Contributor