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Machine Learning Basics For Data Science Enthusiasts

This article was published as a part of the Data Science Blogathon.


  • Introduction to Machine Learning Basics
  • Need of Machine Learning In Modern World
  • Various Concepts Related To Machine Learning
  • Resources to Learn Machine Learning
  • Scope Of Machine Learning

Machine learning is an emerging technology that enables computers to learn automatically from past historical data.

Machine learning deploys various mathematical algorithms for building models and giving output as predictions.

It is being applied in various fields such as image recognition, speech recognition, email filtering, Artificial Intelligence,  Netflix recommender system, Self-Driven Cars, and many more

Machine Learning basics


Table of Contents

1 What Is Machine Learning

2 How Does Machine Learning works

3 Need For Machine Learning

4 Key Features of Machine Learning

5 Types Of Machine Learning

6 Scope Of Machine Learning

7 Applications in Real World

8 Prerequisites For Machine Learning

9 Resources

1. What Is Machine Learning


What Is Machine Learning basics

Machine Learning is all about enabling the machine to learn from data automatically and improvise on previous experiences and predicting things without being given instruction or explicitly programmed to do so.

Making Machines behave like humans is what Machine Learning signifies. We, as humans, are bound to learn from our experiences and make decisions based on our learning, but on the other hand, Machines are always being given instructions to work. Machine Learning Aims at Such ways to make Machine learn from previous data like a human and make accurate, effective predictions

2. How does Machine Learning works

Machine Learning works in the following manner-

  1. A machine is being given traditional data as an Input
  2. Machine Learns from the input data being given
  3. Machine Builds the Prediction Model
  4. Machine trains the model with the input data
  5. After training, the Machine tests the model by predicting the output for the next set of input data
  6. Machine evaluates the accuracy percentage of prediction and improvises itself if the percentage is below the desired number
  7. Machine Improve the Accuracy and learn from previous data /experiences
How does Machine Learning basics works

3. Need For Machine Learning

We, as humans, have limitations and restrictions. With the world being data-driven, It’s hard for a human to process such an amount of data manually. This is where Machine learning is applied as machines are capable of solving such complex problems.

Machine Learning algorithms are being trained upon such huge amounts of data and are being used for making effective predictions and outputs, thus making it easy for a human to handle such complexities of data.

More is the amount of Data, More effective is the Machine Learning performance, thus the need for Machine Learning becomes necessary in the modern era for solving various complex problems.

4. Key Features Of Machine Learning

  1. Solving Various Complex Problems, which are beyond human capabilities to interpret and solve
  2. Identifying Various Pattern in the Data, Thus extracting useful insights to the various problems
  3. Making Effective predictions
  4. Improvise or learn from all the past data, thus enhancing the output accuracy
  5. Comes Under Data-Driven Technology

5. Types Of Machine Learning

Machine Learning can be broadly classified into 3 types namely

a. Supervised learning

b. Unsupervised learning

c. Reinforcement learning

a. Supervised Learning

Supervised learning is a type of machine learning technique in which we train the machine by providing Labeled data(data being tagged by one or more labels/data being categorized) and on that basis, the machine predicts output.

The Machine is being trained with the labeled data under our supervision, just like Teacher trains his/her student, and

The goal of supervised learning is to map the input data with the output data. Once the Model gets trained, it gives prediction as its output for the datasets

It’s of two types:

1 Regression:  Used to Predict continuous values like price, salary, etc.

2 Classification:  Used to Classify discrete values like True/False, Male/Female, etc.

Supervised Learning machine learning basics

b. Unsupervised learning

Unsupervised Learning is a learning method in which A machine is being trained without any sort of supervision.

In this, the Machine is being trained on data that is not being labeled, categorized and learning is done without any supervision. In this, Machine tries to find a useful pattern, insights from the huge amount of data and restructures/ segregate the data on basis of feature or similarities

It’s of two types:

  1. Clustering: Splits the data into various groups based on similarities.
  2. Association:  identifies  the sets of items/ data points that often occur together in the  dataset


Unsupervised learning

c. Reinforcement Learning

Reinforcement learning is a learning method, in which a Machine gets reward points for each right prediction and gets a negative point/penalty for each wrong prediction. The Machine learns automatically with these feedback points and improves its accuracy.

In reinforcement learning, the machine explores the environment it interacts with. The main goal is to get the most reward points, and improving its accuracy

Reinforcement Learning

6. Scope Of Machine Learning

There’s a brilliant Scope Of Machine Learning In the Modern World, Being Data-Driven, including various Sectors like Finance, Business, etc. With The Data being produced at a massive rate, various job roles constitute Machine learning as an important skill. Those Job roles are

1. Machine Learning Engineer – They are professionals who develop the systems and various machine learning algorithms that learn, train on the input data being given, and give various predictions, output, and improvise on the previous data, experiences.

2. Deep Learning Engineer – They are professionals that specialize in advanced Machine Learning Concepts, deep learning, to develop various models, software related to artificial intelligence. Their main goal is to be able to make the machine behave, mimic, think, make decisions like humans.

3. Data Scientist – These are the professionals who extract meaningful insights from data and analyze those insights and giving respective business solutions based on those hidden patterns.

4. Computer Vision Engineer – They are software developers who design such algorithms for recognizing various patterns in images.

With Various Industries investing billions of dollars in the R&D of Artificial Intelligence, which is an application of Machine Learning, The demand and need for exploration in the field of Machine Learning will always be there.

Reinforcement Learning

7. Applications In Real World

There are many Real-world Applications Of Machine Learning, such as

1 Self-driving car,

2 Amazon Alexa Voice Assistant

3 Apple Siri Voice Assistant,

4 Netflix Recommender system

5 Image Recognition

6 Speech Recognition

7 Google Translator

8 Fraud Detection and many more.

Machine learning models can be deployed for various prediction models, including Weather prediction Model, Disease prediction Model, Stock Market Analysis, Car Price Prediction, Real Estate Prediction, etc.


Applications In Real World

                                                            Various Voice Assistants




8. Prerequisites For Machine Learning

  1. Fundamental knowledge Of Probability & Linear algebra.
  2. Familiarity with coding in computer language, especially in Python.
  3. Fundamental Knowledge of Calculus
  4. Well Acquaintances with Statistics

9. Resources

MOOCs: Udacity Python Course, Coursera Python Course

YouTube Channel: Krish Naik, Code Basics

Blogs: Analytics Vidhya, KD Nuggets

With this I finish this blog.
Hello Everyone, Namaste
My name is Pranshu Sharma and I am a Data Science Enthusiast
Thank you so much for taking your precious time to read this blog. Feel free to point out any mistake(I’m a learner after all) and provide respective feedback or leave a comment.
Email: [email protected]

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