Introduction to Machine Learning

Introduction to Machine Learning Training will take place for 10 hours in total with 2-hour programs for 5 days!

We created the content of the education by using the sources of the world’s leading universities Stanford, Caltech, MIT and Harvard!

We will explore supervised and unsupervised learning in our 10-hour journey, where we will start with the topics of linear algebra and probability. As we learn how algorithms work, we will implement them using Python.

Our education is certified.

“Artificial intelligence is a bridge between art and science.”
Pamela McCorduck

If you also want to have a say in this vast world, sign up for our training at the link on our profile and take your first step to build the future!

Curriculum:

Introduction to ML​
  • What is Big Data?​
  • What is ML?​
  • Supervised Learning​
  • Unsupervised Learning
  • ​Reinforcement Learning​​
Linear Algebra Review​
  • Matrices and Vectors​
  • Addition and Scalar Multiplication
  • ​Matrix-Vector Multiplication
  • ​Matrix-Matrix Multiplication
  • Matrix Multiplication Properties
  • Inverse and Transpose​
Probability Review​
  • Random Variables
  • ​Probability Distributions
  • ​Marginal & Conditional Prob.
  • ​Independence​
  • Expectation, Variance, Covariance​
  • Bayes’ Rule
Linear Regression​
  • Linear Regression with a Single Feature ​
  • Linear Regression with Multiple Variables​
  • Error Function ​
  • Gradient Descent ​
  • Cross validation
Logistic Regression
  • Hypothesis Representation​
  • Decision Boundary​
  • Sigmoid Function
  • ​Cost Function
  • ​Gradient Descent​
  • Optimization​
  • Multiclass Classification
Regularization​
  • Overfitting and Underfitting​
  • Regularized Linear Regression
  • ​Regularized Logistic Regression​​
Naive Bayes​
  • Bayes Decision Rule​
  • Naive Bayes Classifier​
  • Conditional Likelihood​
  • Learning Parameters
Decision Tree​
  • Choosing an attribute​
  • Using Information Theory​
  • Information Gain​
  • Gain-Ratio
  • ​Pruning
  • ​Regression Trees
  • ​Random Forest
Unsupervised Learning​
  • Clustering​
  • K-means
  • ​Dimensionality Reduction
  • ​Challenges with High-dimensional Data​
  • Feature Reduction
  • ​Principal Component Analysis

Course Information

  Skill Level: Introduction

  Students Enrolled: 1.690

  Languages: English

  Total Duration: 10 hours

  Certification: Yes