Future of machine learning

MACHINE LEARNING

MACHINE LEARNING is one of the hottest topic in today’s technical world and it’s going to rule the world quickly.

Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal.

How it will be, if a computer acts and thinks like a human …Is it possible?,YES that’s what Machine learning is all about. We are collecting a huge amount of database i.e. information and fed into the computer and it predicts the nature of the thing or anything and acts upon to the situation. We are giving the ability to learn on their own to the computers.

For your better understanding I’m telling you an example..you update the database of your body temperature to the computer i.e. your body temperature at various climatic condition and during fever..once you update all the information to the system,it analyses the whole condition of your body in every condition and stores up the data base and with the help of the database,the air conditioner can be automatically adjust the room temperature according to your body temperature.

See how amazing it is!!

Objectives

Let us look at some of the objectives under this Techniques of Machine Learning tutorial.

• Explain unsupervised learning with examples
• Describe semi-supervised learning and reinforcement learning
• Discuss supervised learning with examples
• Define some important models and techniques in Machine Learning

There are some Prerequisites for learning ML and AI:

You must have some knowledge on linear algebra,some basic programming skills and a bit of knowledge on Differential Calculus

Don’t worry if you don’t posses any of the above mentioned skills because it is not a rocket science, you can easily learn it

The ten methods described offer an overview — and a foundation you can build on as you hone your machine learning knowledge and skill:

1. Regression
2. Classification
3. Clustering
4. Dimensionality Reduction
5. Ensemble Methods
6. Neural Nets and Deep Learning
7. Transfer Learning
8. Reinforcement Learning
9. Natural Language Processing
10. Word Embeddings

Choosing the right algorithm can seem overwhelming—there are dozens of supervised and unsupervised machine learning algorithms, and each takes a different approach to learning.

There is no best method or one size fits all. Finding the right algorithm is partly just trial and error—even highly experienced data scientists can’t tell whether an algorithm will work without trying it out. But algorithm selection also depends on the size and type of data you’re working with, the insights you want to get from the data, and how those insights will be used.

You may also some books like Machine Learning for Hackers" by Drew Conway and John Myles White

Machine Learning For Dummies" by John Paul Mueller and Luca Massaron

But keep the books only for reference because it is not the subject to study theoretically.

It is going to have a brightest future in the upcoming years,so if you are planning to learn Machine learning, learn from the top Machine Learning training Institute which build the better future for you..

All the best!!!Cheers

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