Fundamentals of Machine Learning Online Course (5 credits)


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Sale price€99.75

Data science and machine learning are essential technologies for businesses who wish to capitalize on the vast insights buried in their data. Machine learning (ML) is a branch of artificial intelligence (AI) that seeks to create computer programs that learn from data and automatically perform specified tasks. It is a data analysis technique that automates the creation of analytical models based on the idea that systems can learn from the hidden pattern in data and make decisions with little or no human intervention. Machine learning has gotten a lot of attention in the last decade, not just from academia but also from industry. Nowadays, machine learning has found its way into many of the services we use daily, e.g., Google Search, YouTube, Netflix, and Spotify.

TechClass Fundamental of Machine Learning online course will introduce you to the basic concepts of ML, the most popular machine learning algorithms, their applications, and the intuition behind them as your very first step in your journey to the ML world. By the end of this course, you can think outside the box using machine learning and aptly state your AI-based attitude toward business problems using the appropriate machine learning techniques discussed in the course.

Learning outcomes

  • Learn the basic definitions and the general framework of machine learning
  • Learn about the primary types of machine learning algorithms
  • Get familiar with the history of AI and machine learning
  • Get familiar with the real-life applications of machine learning
  • Learn the main concepts and intuitions of linear regression and logistic regression methods
  • Learn how the K-NN algorithm works
  • Learn how the decision tree algorithm works for classification tasks
  • Understand the main concepts and intuitions of neural network
  • Understand the main concepts and intuitions of linear and kernelized SVM
  • Get familiar with data preprocessing for machine learning and data science
  • Learn about preprocessing methods such as imputation, feature encoding, and feature scaling
  • Get familiar with the general framework of clustering algorithms
  • Learn how data clustering can be performed using the k-Means clustering algorithm
  • Get familiar with the "curse of dimensionality" and the need for dimensionality reduction
  • Learn the intuitions of using PCA to represent data in lower-dimensional spaces
  • Learn the key concepts of model selection and model evaluation in machine learning
  • Learn when to use different model selection/evaluation criteria to compare models and choose the best among them

Table of contents

Chapter 1: Beginning with This Course

  • 1.1. Our Approach in This Course
  • 1.2. About TechClass AI Department
  • 1.3. Your Expectations, Goals, and Knowledge

Chapter 2: Introduction

  • 2.1. What is Machine Learning?
  • 2.2. Machine Learning and Data
  • 2.3. History and Philosophy
  • 2.4. Python

Chapter 3: Types of Machine Learning

  • 3.1. Introduction
  • 3.2. Supervised Learning
  • 3.3. Classification
  • 3.4. Regression
  • 3.5. Unsupervised Learning
  • 3.6. Clustering
  • 3.7. Dimensionality Reduction
  • 3.8. Reinforcement Learning
  • 3.9. Quiz

Chapter 4: Regression

  • 4.1. Linear Regression
  • 4.2. Quiz
  • 4.3. K-Nearest Neighbors Regression
  • 4.4. Exercise

Chapter 5: Classification

  • 5.1. Logistic Regression
  • 5.2. Quiz 1
  • 5.3. Linear Support Vector Machines
  • 5.4. Kernelized Support Vector Machines
  • 5.5. Quiz 2
  • 5.6. K-Nearest Neighbors Classification
  • 5.7. Quiz 3
  • 5.8. Decision Tree
  • 5.9. Exercise
  • 5.10. A Single Neuron
  • 5.11. Neural Networks

Chapter 6: Unsupervised Learning

  • 6.1. Curse of Dimensionality
  • 6.2. Dimensionality Reduction with PCA
  • 6.3. Exercise 1
  • 6.4. k-Means Clustering
  • 6.5. Exercise 2

Chapter 7: Data Preprocessing

  • 7.1. Why Preprocessing?
  • 7.2. Data Imputation
  • 7.3. Feature Encoding
  • 7.4. Feature Scaling
  • 7.5. Exercise

Chapter 8: Model Development

  • 8.1. Model Generalization
  • 8.2. Overfitting and Underfitting
  • 8.3. Evaluation Metrics for Regression Models
  • 8.4. Evaluation Metrics for Classification Models
  • 8.5. Model Selection
  • 8.6. Exercise

Chapter 9: Final Tasks

  • 9.1. Project
  • 9.2. Self-study Essay
  • 9.3. Congrats! You did it!

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J
Jenni M.
Great course

This course really made me interested to continue my plan to learn further about Machine learning.

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