Machine Learning Tutorial

In this tutorial, we will go through Machine Learning; what it is, its history, and its various applications of it. We will also look at the challenges and opportunities that Machine Learning presents in this current world.

Module Certificate
certificate icon
You can claim your course certificate upon course completion. You would be able to use this certificate on your resume, Linkedin profile or your website.
Learn More
certificate icon
Machine Learning Tutorial
This program includes modules that cover the basics to advance constructs of Machine Learning Tutorial. The highly interactive and curated modules are designed to help you become a master of this language.'
If you’re a learning enthusiast, this is for you.
Module Certificate
Upon successful completion of all the modules in the hub, you will be eligible for a certificate.
You need to sign in, in the beginning, to track your progress and get your certificate.

What is Machine Learning?

A subset of artificial intelligence (AI) and computer science called machine learning focuses on using data and algorithms to simulate how humans learn, gradually increasing the accuracy of the system.

The rapidly expanding field of data science includes machine learning as a key element. Algorithms are trained to generate classifications or predictions using statistical techniques, revealing essential insights in data mining operations.

Features of Machine Learning

Machine learning is a subfield of artificial intelligence that focuses on developing algorithms capable of learning from data and improving their performance over time. Machine learning algorithms can handle large and complex datasets and can automatically extract relevant features from data, reducing the need for manual feature engineering. These algorithms can be supervised or unsupervised and can be used for a wide range of applications, including predictive modeling, classification, clustering, and recommendation systems. Machine learning is a rapidly evolving field that is transforming the way we work with data, and its ability to adapt to new data and changing conditions makes it an essential tool for many industries and applications. In summary, machine learning is characterized by its ability to learn from data, handle complex datasets, perform automatic feature extraction, be used for various applications, and adapt to new data and changing conditions.

How Does Machine Learning Work?

Machine Learning works by using algorithms to analyze data, identify patterns and relationships, and make predictions or decisions based on the learned patterns. The process involves several steps, including data preprocessing, feature engineering, model selection, training, evaluation, and deployment. Machine Learning algorithms can be supervised, unsupervised, or semi-supervised, depending on the type of input data and the desired outcome. During training, the algorithm adjusts its parameters to minimize the difference between its predictions and the actual outcomes. Once the model is trained, it can be used to make predictions on new data. Overall, Machine Learning is a complex process that requires a solid understanding of statistics, mathematics, and computer science.

Need for Machine Learning

Machine learning is significant because it helps in the development of new goods and provides businesses with a picture of trends in consumer behavior and operational business patterns. A substantial portion of the operations of many of today's top companies, like Facebook, Google, and Uber, revolve around machine learning. For many businesses, machine learning has emerged as a key competitive differentiation.

Types of Machine Learning

There are three main types of machine learning:

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning
  • Supervised learning involves training a model on labeled data, where the correct outputs are provided alongside the inputs. The goal of supervised learning is to enable the model to generalize to new data by learning patterns and relationships in the data.

  • Unsupervised learning involves training a model on unlabeled data, where the inputs are not accompanied by any specific output. The goal of unsupervised learning is to enable the model to identify patterns and relationships in the data without any explicit guidance.

  • Reinforcement learning involves training a model to interact with an environment and learn from the rewards or penalties it receives based on its actions. The goal of reinforcement learning is to enable the model to learn the best sequence of actions to achieve a specific goal.

Each type of machine learning has its own strengths and weaknesses, and the choice of which type to use depends on the specific problem being addressed and the nature of the available data.

History of Machine Learning

As modern as it sounds, Machine Learning was originally derived in the 20th century. Here's an infographic depicting the history of Machine Learning:

History of Machine Learning

Applications of Machine Learning

Here are some applications of Machine Learning that are used in our day-to-day activities:

  • Social Media Features
  • Image Recognition
  • Regulating Medical Services and Healthcare Effectiveness
  • Banking Domain

Challenges and Limitations of Machine Learning

The lack of data or the diversity in the dataset is the main problem with machine learning. If there is no data, a machine cannot learn. A dataset with little variability also makes the computer work harder. For a device to get insightful knowledge, heterogeneity is necessary.

When there are no or few variations, it is uncommon for an algorithm to be able to extract information. For the machine to learn, it is advised that each group receive at least 20 observations.


This tutorial is aimed at readers who:

  • Beginners in Python who want to understand how Machine Learning works.
  • People willing to make a career switch in Data Science and Machine Learning.


To get the best out of this tutorial, the reader:

  • Must have Python installed in their system and an IDE to code with.
  • Basics of programming with Python would be beneficial.

How Long Does It Take to Learn Machine Learning?

Machine Learning, in itself, is a very big domain to learn. Concepts like Analytics, Big Data, Statistics, etc are to be mastered in order to learn Machine Learning.

After learning the basics of Machine Learning, we need to do some real-time projects regarding ML (predictions or visualizations).

By following this approach, you will be ready to be a Machine Learning Engineer in less than a year.

About This Machine Learning Tutorial

We understood a lot of important concepts in this Machine Learning tutorial. From the history to challenges and limitations, it is safe to say that we have covered it all. Later on in this course, here are some of the topics that we will go through:

  • Supervised Learning and the various algorithms it encompasses.
  • Types of various unsupervised learning algorithms.
  • Basics of Reinforcement Learning concepts like environment and agents.
  • In-depth explanation of Machine Learning algorithms with projects, so that readers can understand how ML is implemented in projects.

Take-Away Skills from This Machine Learning Tutorial

By completing this tutorial, the reader must be well aware of the basics of Machine Learning. By learning about the history behind Machine Learning, and its various applications of it, you indeed are ready to go on and conquer the world of Machine Learning!

Start Learning
Certificate Included
Written by Industry expertsLearn at your own paceUnlimited access forever
12 Modules6 Hour 56 Minutes62 Lessons62 ChallengesLanguage IconLanguage: English
Written by Industry expertsLearn at your own paceUnlimited access forever
12 Modules6 Hour 56 Minutes62 Lessons62 ChallengesLanguage IconLanguage: English