A Beginners Guide to Machine Learning - 2020

By Ravi Kiran | 24th September, 2020 | 8 min Read

The term “Machine Learning” was first coined in 1959 by AI pioneer Arthur Samuel and has been the buzz word since the last 10 years

Humans were learning and evolving from their past experiences and the era of machines have begun.What if machines start to learn on their own and this is where machine learning becomes prominent.

What is machine learning and where exactly it is used. Keep reading this blog to find the answers.

What is Machine Learning?

Machine Learning is defined as an application that provides systems or computers the ability to automatically learn and improve from experience without being programmed time to time. The main aim of using Machine Learning techniques is to allow the computers to learn automatically without any involvement of humans. This methodology significantly reduces the burden on humans in terms of both time and effort.

ML is a booming field of Computer Science that has applications in almost every field of Engineering. Machine Learning, a subset of Artificial Intelligence has numerous applications in fields such as Robotics, self-driving cars, speech recognition and many more. Various industries and research organizations use ML to a large extent.


Where is Machine Learning used?

Machine Learning finds applications in almost every field not just pertaining to computer science or engineering fields. R&D’s of many industries and research organizations use ML to solve complicated problems saving a lot of human effort and time.Here are some of the domains where machine learning is used extensively,

  • Retail: Demand Forecasting,Supply Chain Optimization
  • Marketing: Social Media Analysis, Recommendation Engines and targeting
  • Healthcare: Diagnostics and alerts, Predicting Patient Disease risk
  • Finance: Credit Scoring, Risk Analytics

What can Machine Learning do?

In today’s time efficient and fast racing world, machine learning is a must for reducing the human effort and time and makes things look much simpler than they actually are.Here are some most frequently practiced machine learning applications which you would have already come across or at least heard about at some point of time.

  • Anomaly Detection

  • Machine learning can be used to detect anomalies in a large collection data. This is very useful in detecting fraudulent bank transactions and medical problems to name a few. If in a large collection of data one wants to find if there are any ambiguities or deviations from the expected trend, this methodology can be adopted in finding the anomalies.

  • Speech Recognition

  • Machine learning develops methodologies and technologies that enable the recognition and translation of spoken language into text. The best example for this is Siri in apple. This technology is based on speech recognition using ML. Siri recognizes the human voice, converts it into text, processes the text and converts the output text into a voice which is heard out by humans.

  • Recommender Systems

  • A list of videos is recommended for the user based on his/her interests. Yes! as you would have guessed it is the recommendation that the users get while watching videos in youtube or movies in Netflix. This has become particularly helpful for users to reduce the time needed for searching a movie or video that could interest them. ML algorithms are used to develop such suitable recommender systems.

  • Assistive Medical Technology

  • Machine Learning can be used to analyze 2D CT scans and come up with 3D models to predict where exactly their lesions are in the brain. They are also used for brain tumour analysis, cardiac analysis and many more. In 2012, co-founder of Sun Microsystems Vinod Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software. This indicates the impact Machine Learning has created in the medical field.

Machine Learning methods

Supervised and Unsupervised machine learning techniques are the two major classes of machine learning which are used to solve problems. These techniques have several sub categories under them and every technique has got its own advantages and disadvantages. Based on the type of the problem and data available to the user, different methods of solving are used.

  • Supervised learning:

  • This technique involves feeding the example input and output pairs of data. The machine uses these examples to find a suitable function that can be used to predict the solution for new inputs that are fed into the system. This technique basically involves training the data and predicting the output for new input data that was never seen before by the machine.

  • Unsupervised Learning:

  • As the name says, there is no need to supervise the data in unsupervised learning. This allows the model to work on its own to discover information and patterns that were never detected before.

Machine Learning jobs

Machine Learning jobs nowadays are plenty in industries. ML engineers have a huge demand in the industries as they get to work on innovative projects and building new technologies. According to statistics, the average salary paid to a Machine Learning Engineer in the USA is around $120,000 per annum. Also many startups have come up and are coming up in the field of machine learning and every other computer science related company works on projects which have machine learning applications. This field is an ocean and has a lot to offer. Machine Learning as a career is definitely a great choice.

1) Machine Learning Engineer:

Machine Learning Engineer runs various experiments through programming languages such as python with appropriate Machine Learning Libraries. They are primarily involved with the design and development of Machine Learning systems and applications by using ML algorithms and tools. It is their job to shape and develop efficient self-learning ML applications by performing statistical analysis and fine-tuning them using test results.

Skills required for a Machine Learning Engineer:

  • Strong foundation in mathematics and statistics
  • Programming languages such as python, matlab.
  • Well versed with algorithms, data structures and data modelling.

2) Data Scientist:

A data scientist uses Machine Learning for predicting large amounts of data that could be used to make business decisions by companies. These job roles require one to collect, store, process, analyze and interpret massive amounts of data.

Skills required for a Data Scientist:

  • Good knowledge of programming and mathematics
  • Should be well versed with statistical research techniques
  • Knowledge of using big data platforms such as spark, flume,hadoop

3) Human-centred ML Designer:

This profession is completely based on designing algorithms centred around humans. Machine Learning techniques are used to learn about the interests of humans and further guide the individuals based on their interests. The most popular examples of this are the suggestions in Netflix, youtube and Amazon Prime. This has become increasingly popular as it saves a lot of time and effort for the user.

Skills required for a Machine Learning Designer:

  • Strong programming skills
  • Good proficiency in mathematics and statistics.

4) Natural Language Processing Engineer:

This aims to impart the knowledge to the machine in order to recognize and understand the natural human language. NLP Engineers are responsible for designing and developing machines that can learn the patterns of human speech and translate them into other languages.

Skills required for a NLP Engineer:

  • Fluent in spellings and grammar of at least one spoken language.
  • Good understanding of ML algorithms and concepts.

Different Machine Learning job roles need different skill sets and the picture gives a concise yet effective information about the different skills needed for bagging a ML related job or to further enhance one's position in a machine learning career.

Machine Learning projects for beginners

Beginners like you can make great progress in applying machine learning to real-world problems with these fantastic machine learning projects for beginners recommended by industry experts.


Download datasets, Just a click away!

Is Machine Learning the future?

Most of the Engineering students at some point of time would have heard that machine learning is the future. But have you ever wondered what makes it so? The answer to this is very simple. ML is used to solve engineering problems in day to day life by an indefinite number of industries. The usage of machine learning has become a common or should say a compulsory practice in industries just like how we do our daily routines.

Engine based Automobiles are being replaced by self driving cars, robots are being used for the purpose of serving food in restaurants, your mobile recognizes your question and gives you the answer from google without you typing anything. The aim of all these technologies is to reduce human effort. The work atmosphere is changing day by day and machine learning is certainly the future of our world.

Machine Learning as a career choice

According to a report from Indeed,Machine Learning has the highest demand in 2019 due to growing demand and high salaries.

To learn Machine Learning a good knowledge of high school mathematics is highly recommended but some courses offered in various e-learning sites are designed in such a way that they teach from the utmost basics and hence no prior knowledge or experience is needed to start learning ML.

Almost in all the e-learning websites, ML courses are designed for all categories of people from beginner to advanced level programmers.

Here are Machine Learning courses that we would recommend to have a lucid understanding of the subject:

  1. Machine Learning Course by Stanford University offered on coursera
  2. Machine Learning course A-Z:Hands-On Python and R from udemy
  3. Machine Learning Course from Udacity
  4. Machine Learning from Edx
  5. Machine Learning Course offered by Datacamp

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