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Introductıon to machıne learnıng

Kick-start your journey in Machine Learning with a 10-hours-long live instructor-led "Introduction to Machine Learning" course.

Introductıon to machıne learnıng

March 22-26, 2021 | 7:00 pm - 9:00 pm (GMT+3)
Apply for free

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
Data Preparation
  • Steps of Machine Learning Project
  • Data Gathering
  • Exploratory Data Analysis (EDA)
  • Pre-processing
  • Train / Validation / Test Split
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
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

Welcome to our Introduction to Machine Learning Courses, a 10-hours-long live lecture tailored for you to kick-start your journey in Machine Learning!

To increase the number of educated people and raise awareness in AI, Global AI Hub, The Union of Chambers and Commodity Exchanges Turkey (TOBB), and TOBB ETÜ planned to realize ‘Education and Awareness Project on Artificial Intelligence’. We are eager to enable high-quality projects to arouse, contribute to this AI ecosystem, and get people ready for possible job opportunities in the future. It is our great pleasure to announce that this course will be held under this exciting project’s roof!

You will be getting into the world of Machine Learning with our unique content created by using the sources of the world’s leading universities: Stanford, Caltech, MIT, and Harvard!

In this course, you will learn the basics of Supervised and Unsupervised Machine Learning algorithms with hands-on experience. By completing this course, you will develop the required skills to build Machine Learning projects by using Python.

You can ask all your questions via Machine Learning Hub on Global AI Hub Community.

You can check most frequently asked questions about this course.

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March 22, 2021 – March 26, 2021

 

7:00 pm – 9:00 pm (GMT+3)


You need to login first.

 

Introductıon to machıne learnıng

Kick-start your journey in Machine Learning with a 10-hours-long live instructor-led "Introduction to Machine Learning" course.

Introductıon to machıne learnıng

March 22-26, 2021 | 7:00 pm - 9:00 pm (GMT+3)
Apply for free

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
Data Preparation
  • Steps of Machine Learning Project
  • Data Gathering
  • Exploratory Data Analysis (EDA)
  • Pre-processing
  • Train / Validation / Test Split
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
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

This course is brought to you by Global AI Hub, TOBB, TOBB ETÜ and AI Business School.