The basics of Machine Learning

The basics of Machine Learning

The basics of Machine Learning

Fundamentals of machine learning to get you started in the field

Language: english

Note: 2.8/5 (2 notes) 1,521 students  New course 

Instructor(s): Sharjeel Kazim

Last update: 2022-08-25

What you’ll learn

  • Basic understanding of Machine Learning.
  • What machine learning is.
  • Why we use machine learning.
  • What are the types of the machine learning systems.
  • What are the challenges in machine learning.
  • Underfitting/Overfitting.
  • And a lot more.



  • No need to have any prior knowledge of coding of machine learning.
  • Have a good understanding of English language.



Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to “self-learn” from training data and improve over time, without being explicitly programmed. Machine learning algorithms are able to detect patterns in data and learn from them, in order to make their own predictions. In short, machine learning algorithms and models learn through experience.

In traditional programming, a computer engineer writes a series of directions that instruct a computer how to transform input data into a desired output.

Machine learning, on the other hand, is an automated process that enables machines to solve problems with little or no human input, and take actions based on past observations.

In this course we are going to talk about the basics of the machine learning which will provide a strong foundation to the students who want to make a career in the field of data sciences and machine learning, we will go through each of the basic important thing that a beginner needs to know to get started with machine learning. We will be talking about what is the machine learning and why exactly we need to use the machine learning, then we will discuss the types of the machine learning system where we will be going in detail about all type and classification of the machine learning system. Then we will talk about the main problems that the data scientist face when they perform machine learning task or making a machine learning algorithm.

This course is introductory, do not expect high level coding and programming, this course is just to build a foundation on which a strong building shall stand.


Who this course is for

  • Those who want to start fresh in the field of Machine Learning
  • Those who have dove in this filed yet have some lack of knowledge of the domain
  • those who are looking for a solution related to machine learning
  • those who want to expand their knowledge


Course content

  • Introduction
    • Introduction
    • Intro to ML
    • Machine Learning Landscape
    • What is Machine Learning?
    • Why Machine Learning?
    • Machine Learning vs. Deep Learning vs. Neural Networks
  • Types of ML Systems
    • Types of ML system
    • Supervised ML
    • Unsupervised ML
    • Semi-Supervised ML
    • Reinforced ML
    • Batch Learning
    • Online Learning
    • Instance Based Learning
    • Modal Based Learning
  • Main Challenges of Machine Learning
    • Insufficient Quantity of data
    • Non Representative data
    • Poor Quality of data
    • Irrelevant features
    • Overfitting the training data
    • Underfitting the training data
  • Testing & Validation
    • Testing and validation
    • Hyperparameter Tuning & Modal selection
    • Hyperparameter tuning for machine learning models
    • Data mismatch
    • What are Data Mismatch and their potential solutions in Machine Learning?


The basics of Machine LearningThe basics of Machine Learning

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