Explore Machine Learning: Here's How to Find Your Way Through the Data Science Maze

The term ‘Machine Learning’ was coined in 1959 by then-IBM computer scientist Arthur Samuel while designing a computer algorithm for the classic game of Checkers. Today, this term is immensely popular owing to the technology’s wide application across industries.

But what is Machine Learning or ML? It is a computational method that is used to obtain artificial intelligence by making a machine learn how to solve problems on its own rather than requiring explicit programming software. 

Machine Learning is widely used in the field of data science as it helps find the way through tons of data instantly and accurately! But how? By using statistical methods and algorithms to train computers so that they can accurately classify data sets and make reliable predictions to uncover key data insights.

Does all this sound interesting to you? Do you aspire to use advanced Machine Learning technologies to solve real-life problems and arrive at data-driven solutions? If yes, then you should check out our data science courses which are equipped with not only data mining techniques but also machine learning tools along with Python, SQL, and Tableau.

Machine Learning Concepts Which Every Data Scientist Must Know About

Data science learners must be able to develop a solid foundation and specialise in machine learning with Python for data-driven decision-making. Ultimately, you want to assist organisations to make smart decisions for growth and offer insightful data analysis.

Following are some of the key Machine Learning tools which you must know about if you are aspiring for a data science career:

• Clustering

CLustering is the simplest unsupervised ML method that lets the algorithm define the output for mining data. The most famous clustering method is ‘K-Means’ under which the letter ‘K’ refers to the number of clusters into which the miner wants to divide the unlabelled data. 

The clustering method is used for drawing analysis in varied fields such as for creating customer segments for different marketing techniques as well as for identifying earthquake-prone areas.

• Neural Networks

If you are interested in the Deep Learning subset of ML, then you must know Neural Networks in and out. Neural Network is a network of algorithms that identify patterns or relationships among different data points in a set in a way similar to the working of a human brain. 

It is widely used for making forecasts and improving decision-making in fields like stock market trading, medical diagnosis, etc. You can learn more about neural networks in our data science online training programs.

• Regression

Regression is one of the fundamental supervised ML techniques which help data scientists in creating predictive models by defining a relationship between dependent and independent variables. 

There are various types of regression models, however, broadly they can be classified into three groups: Simple Linear Regression Model (SLRM), Multiple Linear Regression Model (MLRM), and Logistic Regression.

• Natural Language Processing (NLP)

Natural Language Processing (NLP) forms the basis of Machine Learning as it trains machines to learn the language of humans. You can find some of the everyday applications of NLP in voice-controlled applications like Apple’s Siri, Google Assistant, Amazon’s Alexa, etc. NLP is also found in execution in the fields of text summarization and sentiment analysis. 

• Ensemble Methods

The concept of the Ensemble Method is quite similar to that of assembling. For instance, if you are not happy with all the car options available in the market and wish to come up with a car design, you can assemble your favorite car parts of different cars and design a car of your choice. 

Similarly, if as a data scientist, you are not convinced with the results of different predictive models, you can combine all of them to arrive at better predictions.

• Transfer Learning

Transfer Learning is one of the efficient ML techniques which lets you use parts of previously programmed neural nets to develop a similar model. For instance, if you are a data scientist who has developed a technique to filter different styles of men’s clothing in buckets like shirts, t-shirts, kurtas, etc., you can use parts of transfer learning to develop a mechanism that can be used for categorising women’s clothing in say, dresses, jumpsuits, tops, etc.

Takeaway

Machine Learning has become a crucial part of the data science field today, which has made the process of analysing and predicting data faster and more accurate than before. 

Be it for real-time navigation, or product recommendations, as a data scientist you will always find Machine Learning and Data Science going hand-in-hand. And the future of data science is expected to be even more promising with the advancements in ML techniques and methods.

Thinking of kickstarting your data science career? Contact us through chat support, or just visit our nearest training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon.

Our Certificate Program In Data Science And Machine Learning is created by iHUB DivyaSampark at IIT Roorkee and will instruct you on the fundamentals and features of data science and machine learning and give you the skills necessary to put these ideas into practice and apply them to real-world issues.