According to Wikipedia

Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.

Tec4Tric explains Machine Learning

Machine Learning is just a LEARNING technique for machines or computers.

That is the simplest definition you can get. But what is learning? How do you define it?

Learning – the acquisition of knowledge or skills through study, experience or being taught.

Machine Learning – the acquisition of knowledge or skills through study, experience or being taught by a Machine.

Why Machine Learning is needed?

Suppose you have a rule Y = 3x-1 and a dataset x = {-1, 0, 1, 2, 3, 4, 5}, from that you can simply get the output(Y). But what about If you have the dataset and output, and you have to come up with the rule? How will you manage? for this thing, we need Machine Learning.

If we have Rules and Datasets and we have to come up with the Answer, this is basically Traditional Programming. Where we put Rules and Data in and we get Answer as an output. 

But in the case of Machine Learning, it redefines this system, here we put Answer and Data in and we get Rules out.

Mainly 3 types of learning are there for Machine Learning.

Supervised Learning

Unsupervised Learning

Reinforcement Learning

  • Supervised Learning Algorithms

    Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events.

  • Unsupervised Learning Algorithms

    Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.

  • Reinforcement Learning Algorithms

    Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning.

And there is one more that is Semi-Supervised Learning.

  • Semi-supervised Learning Algorithms

    Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy.

If you are a beginner and want to learn Machine Learning, then start with the below options. Everything is FREE.

Machine Learning Books For FREE

Download Paid Machine Learning Books for FREE.

Machine Learning Projects

Browse Projects with Datasets & Solution.

Machine Learning FREE Courses

Get Free Courses at Coursera, Udacity, Udemy.