Learn Machine Learning

Machine Learning Tutorial

Predictive text and chatbots, tools for language translation, Netflix’s recommendations for movies you might like, and the layout of your feeds on social media are all products of machine learning. It fuels self-driving cars and equipment that uses images to identify medical issues.

Every industry is being transformed by this pervasive and potent form of AI. Here’s everything you want to learn about machine learning’s uses, potential, and restrictions. Basic and advanced machine learning (ML) principles are covered in the ML tutorial. Both students and professionals in the workforce can benefit from our ML tutorial.

What is Machine Learning?

A set of algorithms known as “machine learning” is capable of learning from experience and improving itself without having explicit programming. Artificial intelligence includes machine learning, which uses statistical methods and data to predict an outcome that can be utilized to generate valuable insights.

It is a data analysis technique used to automate the creation of analytical models. It is a subfield of artificial intelligence founded on the notion that machines are capable of learning from data, spotting patterns, and making decisions with very minimal assistance from humans.

The innovation is the notion that a machine can provide precise outcomes by learning just from the data (i.e., examples). Data mining, Bayesian predictive modeling, and machine learning are all closely linked. As input, the machine takes in data and generates results using an algorithm.

ML algorithms create a mathematical model with the aid of previous sample data, or “training data,” which aids in making predictions or judgements without being explicitly programmed. The algorithms that use historical data to learn are created or used by machine learning.

History of Machine Learning

The concept of machine learning is quite old and behind it has a long history. Some significant events in the history of ML are listed below:

Era of Machine Learning as a “Theory”

  1. 1940: ENIAC, the first electronic general-purpose computer, was created in 1940 and became the first computer to be operated manually. Then came the invention of the stored program computer, including the EDSAC (1949) and the EDVAC (1951).
  2. 1943: A model of a human neural network using an electrical circuit was created in 1943. The scientists began putting their theory to use in 1950 and researching potential functions of human neurons.
  3. 1950: On the subject of artificial intelligence, Alan Turing presented a groundbreaking paper in 1950 titled “Computer Machinery and Intelligence.” ” Can machines think?”- he posed a question in his article.
  4. 1952: A program created by ML pioneer Arthur Samuel assisted an IBM computer in playing the game of checkers. The more it played, the better it performed.
  5. 1959: The phrase “Machine Learning” was originally put forward by Arthur Samuel in 1959.

Era of Machine Learning as a “Reality”

  1. 1959: In order to use an adaptive filter to eliminate echoes across phone lines, the first ever neural network was put to use in 1959.
  2. 1985: A neural network called NETtalk, developed in 1985 by Terry Sejnowski and Charles Rosenberg, learned how to speak 20,000 words accurately in a single week.
  3. 1997: Garry Kasparov was defeated by the Deep Blue intelligent computer of IBM in a chess match, making history as the first computer to defeat a human chess master.
  4. 2006: Geoffrey Hinton, a computer scientist, renamed neural network research as “deep learning” in 2006, and it has since emerged as among the most popular technologies.
  5. 2012: Google developed a deep neural network in 2012 that has mastered the ability to identify both cats and people in YouTube videos.
  6. 2014: The Chabot “Eugen Goostman” passed the Turing Test in 2014 and became the first ever chatbot which successfully made 33% of the human judges believe that it was not a machine. Same year, Facebook developed a deep neural network called DeepFace that they claimed could identify people as precisely as a human could.
  7. 2017: The Jigsaw team at Alphabet developed an intelligent system that was capable of learning online trolling in the year 2017. It learnt to curb online trolling by reading bundles of comments over different websites. In the same year, at the game of Go, “AlphaGo” defeated Ke Jie, the top player in this game.

Era of present modern Machine Learning

Machine learning has made enormous strides in its research in recent years, and it is already pervasive in our daily lives in the form of Amazon Alexa, self-driving cars, recommender systems, and many other applications. Weather forecasting, disease identification, market trends and analysis, and other forecasts can all be made using modern ML algorithms.

Why Learn Machine Learning?

Machine learning is significant because it aids in the development of new goods and provides businesses with a glimpse of trends in consumer behavior and operational business patterns. A significant portion of the operations of many of today’s top businesses, like Google, Facebook, Twitter, Netflix, Youtube, and many others, revolves around machine learning. For many businesses, machine learning has emerged as a key competitive differentiation.

The importance of machine learning is expanding as a result of the vastly increased amounts and types of data, the accessibility and affordability of computer power, and the accessibility of high speed Internet. One may quickly and automatically create models that can evaluate incredibly huge and complicated data sets with speed and accuracy because of these digital transformation aspects.

The first step in the machine learning process is feeding the chosen algorithm with training data. The final machine learning algorithm is developed using training data, which might be known or unknown data.

The ML algorithm is then fed fresh input data to see if it functions properly. Then, the prediction and outcomes are cross-checked.
If the prediction and findings are inconsistent, the algorithm is repeatedly re-trained until the data scientist achieves the desired result. As a result, the ML algorithm is able to continuously learn/train on its own and produce the best solution, steadily improving in accuracy.

Features of Machine Learning

  1. Machine Learning is a technology that is powered by data.
  2. Automated data visualization capability.
  3. By using historical data, it can automatically get better.
  4. Data mining and machine learning are quite similar because both processes work with vast amounts of data.
  5. Pattern recognition feature.
  6. Extraction of valuable information from data.

What are the types of Machine Learning?

Since supervised learning, unsupervised learning and reinforcement learning are the three main subfields of machine learning, it has been decided to divide this complex field into three. Each one has a certain function, takes a specific course of action, produces outcomes, and makes use of different kinds of data.

1. Supervised Learning: Supervised learning is used in most practical ML applications. In supervised learning, sample “labeled” data is given to the machine learning algorithm as training data, and then it uses that information to predict the outcome.
With supervised learning, an algorithm to learn the function that maps the input variables( say X) to the output variable (say Y) is used.


The objective is to estimate the mapping function as closely as possible so that the model can forecast the output data (Y) for new input variables (X).

The foundation of supervised learning is supervision, just like when a student learns under the teacher’s observation. Supervised learning assists firms in finding scalable solutions to a range of real-world issues, such as spam classification in a different folder from your email.

Supervised Learning can be classified into two types of problems:

  • Classification
  • Regression

2. Unsupervised Learning: Unsupervised learning is a subcategory of machine learning in which unlabeled datasets are used which allows the model to operate on data without any supervision. The goal of unsupervised learning is to uncover relevant insights and hidden patterns from an unknown dataset.

Unsupervised learning can be applied in situations where one just has access to the input data and is unaware of the output data. Since unsupervised learning continues to acquire new knowledge as it gains more experience, it is more like artificial intelligence.

For example, “Analysis of Social networking” is used to group people into friendship clusters based on how frequently they connect with one another. These analyses show the connections between users of particular social networking websites.

Unsupervised Learning can be categorized into two types of problems:

  • Clustering
  • Association

3. Reinforcement Learning: In order to achieve a specific objective, a system communicates with a dynamic environment (for eg. vehicle driving or games with opponents). As the system moves through its problem area, it receives feedback in the form of rewards and penalties. With the help of this feedback, the agent automatically learns and performs better. To earn the greatest reward points, an agent strives, and as a result, performs better.

The robotic dog that learns automatically how to move its arms is an illustration of reinforcement learning. Chess Game is another example for this type of machine learning technique.

Major Real-World Areas with ML Implications

Every business aspires to increase profits and strengthen client relationships. Machine learning has aided in making it possible to stand out from the competitors. Every industry can benefit from machine learning and many have already started to apply it to operate their major tasks. Sectors, where machine learning is playing significant roles, include:

  • Machine Learning in healthcare.
  • Machine Learning in banking.
  • Machine Learning in education.
  • Machine Learning in finance.
  • Machine Learning at Google.
  • Machine Learning at Facebook.
  • Machine Learning in the stock market.
  • Machine Learning for Risk management.
  • Machine Learning in Agriculture.

ML Interview Questions for Beginners

Following ML-based questions are most likely to be asked during interviews:

  1. What is Machine Learning?
  2. What is the major difference between supervised and unsupervised learning? What are the various applications of Machine Learning?
  3. What are various applications of Machine Learning?
  4. What are the features of Machine Learning?


This was all about the ML tutorial, where you learned what ML is, why it is needed, what major areas it can be used for, its working and the subcategories it is divided into.