What is machine learning?

Machine Learning (ML) is a branch of Artificial Intelligence (AI) used in the development of computer systems that have the ability to learn from previously used data and past experience without being explicitly programmed to do so.

Machine Learning by using algorithms and statistical models built such environments that are helpful to train it automatically to become more accurate at predicting output values.

How does machine learning work?

Machine Learning uses two main techniques:

  1. Supervised Learning.
  2. Unsupervised Learning.

Supervised Learning

Supervised Learning uses the known pattern of data to make predictions on the basis of a previous Machine Learning deployment. A supervised learning algorithm takes a known set of data as input and known responses to the data as output. Supervised Learning trains a pattern to determine reasonable predictions based on the previous examples.

Supervised Learning techniques use two techniques to build a machine learning model, 1st is Classification Techniques and the 2nd is Regression.

Unsupervised Learning

Unsupervised Learning algorithms use unknown and hidden patterns to develop Machine Learning models. In this technique, the algorithm learns from the inherent pattern of the data, and data is not labeled unless it is put into the algorithm.

Unsupervised Learning uses the Clustering Technique to draw the pattern based on inferences from a dataset.

Which language is best for machine learning?

R and Python are the most commonly used programming languages for machine learning. Apart from this, there are plenty of other programming language possibilities for machine learning.

As Python support algorithm used in machine learning such as classifications, regression, clustering, and dimensionality reduction. Therefore, it would not be wrong to say that Python is number one on the list of top programming languages for machine learning.

What are the best examples of Machine Learning?

Machine Learning is used in various fields and industries, it has grown over time a lot. Here are following are the best real-life example of machine learning: –

  • Image and face recognition

Image recognition is the most common feature being provided in the latest gadgets that are the best example of machine learning. Machine learning is also used for facial recognition such as mobile face unlock and also in law enforcement systems can identify the commonalities and match with the faces.

  • Products Recommendations

Machine learning is used in e-commerce websites for product recommendations to customers on the basis of their previous searches and purchase.

  • Language Translator

Language Translator websites and apps are one of the best examples of machine learning. Using these translators, you can translate any language into any other desired language.

Google Translate used Google Neural Machine Translator to detect the language from the given text or speech and translate it into your desired language.

  • Speech Recognition

Voice Search and voice dialing are the very common feature of your mobiles, laptops, and other gadgets. With the help of machine learning, many computer speech recognition and automatic speech recognition applications have been developed that help to convert speech into text and text into a command for the devices.

Alexa and Google home are the best real-world examples of speech recognition.

  • Medical Diagnosis

The use of machine learning has greatly increased in the medical diagnosis of deceases. Machine learning recognizes the patterns in symptoms of diseases, which help doctors to take quick decision and save patients.

  • Ads Recommendation

On the internet, each user of the same location gets different ads recommendations, because the machine learning algorithm learns from their previous surfing history and stared to show ads recommendation according to their interest.

For example, if one is searching on the internet for a mobile phone within his budget, he will get start ads recommendation of latest mobiles within his budget and another is looking for something else on an e-commerce website he will get ads recommendations according to his search history.

  • Social Networking

  • On social networking platforms, you get friends suggestions, images from various places posts according to your interest, and many more, all happening with the help of machine learning algorithms.
  • Email Filtering

The machine learning algorithm automatically filters all received emails and sends important emails to our inbox and spam emails in the spam box.

  • Extraction of Information or data

The machine learning algorithm has the ability to extract structured data from unstructured data. It is used in predictive analytics tools. Organizations extract structured information from collected customers’ data.

  • Traffic Alert using Google map

Now, when we need to go to an unknown place, we use Google Maps to find the best possible route to our destination.

Google Maps suggests to us the fasted route, arrival time, and pick-up location, and predicts the traffic. All this is possible with help of a machine learning algorithm.

Machine learning draws a pattern from the density of vehicles and speed of vehicles at a location and provides a reasonable prediction about traffic and route.

  • Self-driving cars

Many companies are working on driverless cars and automobile company like Tesla has achieved this goal. Self-driving cars are considered as future of the automobile. Self-driving cars concepts are based on deep learning and machine learning algorithm.

Apart from the above, there are many other real-life examples of machine learning that you come across every day.

Machine learning is related to almost every aspect of our life in some way, so machine learning and AI are the future of humans.

Therefore, it is beneficial to understand or learn machine learning, and there can be no better source than books to be familiar with any new information.

Best Books for Machine Learning 


The list of books for machine learning that I have provided below has tried to place the books in the list for both machine learning beginners and experienced

Here are some good books on machine learning: –


  • Mathematics for Machine Learning

  • Python Machine Learning By Example

  • Introduction to Machine Learning with Python

  • The Hundred-Page Machine Learning Book

  • Machine Learning For Absolute Beginners

  • Python for Data Analysis

  • Deep Learning (Adaptive Computation and ML Series)

  • The Hundred-Page Machine Learning Books by Andriy Burkov

  • Machine Learning For Absolute Beginners by Oliver Theobald

  • Machine Learning for Hackers by Drew Conway and John Myles White

  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron Aurelien

  • Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

  • An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

  • Programming Collective Intelligence by Toby Segaran

  • Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy

  • Machine Learning for Humans by Vishal Maini and Samer Sabri


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