Changing the dynamics of Machine Learning education.

What comes to your mind when you hear the word “Machine Learning”?

You might be thinking that machine learning is all about the highly intelligent machines which can take away all the jobs from the human beings and be the boss!

Or it can be a secret to a better future for the mankind as our lives are already being influenced by those “smart and highly intelligent machines”.

In order to understand machine learning we must ask three questions :

• What is Machine Learning?
• Where exactly can we apply machine learning?
• How to apply machine learning?

What is Machine Learning?

In simple words, it’s a science of finding patterns and making predictions from data based on work in multivariate statistics, data mining, pattern recognition, and advanced/predictive analytics.

Machine Learning can best be understood through four ways which is as follows:

1. The Broad: Machine Learning is the process of predicting things, usually based on what they’ve done in the past.
2. The Practical: Machine Learning tries to find relationships in your data that can help you predict what will happen next.
3. The Technical: Machine Learning uses statistical methods to predict the value of a target variable using a set of input data.
4. The Mathematical: Machine Learning attempts to predict the value of a variable Y given an input of feature set X.

Kinds of Machine Learning

There are three kinds of Machine Learning Algorithms.

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Supervised Learning

A majority of practical machine learning uses supervised learning.

In supervised learning, the system tries to learn from the previous examples that are given.

Unsupervised Learning

In unsupervised learning, the algorithms are left to themselves to discover interesting structures in the data.

Mathematically, unsupervised learning is when you only have input data (X) and no corresponding output variables.

Reinforcement Learning

A computer program will interact with a dynamic environment in which it must perform a particular goal (such as playing a game with an opponent or driving a car). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.

What are the applications of Machine Learning?

1. Self-Driving cars

2. Product Recommendations

3. Fraud Detection

4. Image Recognition

5. Speech Recognition

6. Diagnosis in Medical Imaging

7. Drug Discovery

And many more.

Do you know how machine learning is improving our life as well as the profits for the companies?

1.Personalized recommendation

Ever wondered how companies like Facebook, amazon, Netflix knows everything you might want to buy or people you might want to get connected with and which shows you can might want to watch next? Answer is machine learning. It is used to process an extensive amount of user data: personal information, search history, content interactions, etc. to offer personalized data that we see.

Netflix saved nearly $1 billion due to machine learning algorithm that recommends personalized TV shows and movies to its customers.

2.Speech Recognition

Ever wondered how Apple’s Siri and Amazon echo knows everything you ask or is able to answer to all your questions? Answer is Machine Learning.

3.Fraud detection in Banks

Ever wondered how banks or finance industries detect frauds? Answer is Machine Learning. It has tools which scan and flag the fraud transactions. Companies like PayPal has various machine learning tools that study billions of transactions and determine which is legitimate and which is fraudulent. Thus, helps in dealing with money laundering cases.

4.Medical Diagnosis

Ever wondered how drug discovery and robotic surgery actually work? Is it possible to detect cancerous cells/tumors using Machine Learning ? The answer is Yes!

Machine Learning is everywhere!!

But how can you apply Machine Learning?

As a beginner, do you face problems/issues while studying and applying machine learning algorithms and techniques? Do you think that machine learning is tough, or you are intimidated by its complexity? Do you know how to read machine learning research papers? These are some of the questions which the machine learning enthusiasts have to deal with and in the quest to find the answers they get confused and finally give up!

We are trying to solve this problem. We believe that anyone who wants to learn and apply machine learning should be able to do so by following right steps and implementing the solutions rather than just ending up in the confusion. We are coming soon, and we believe that we will be changing the dynamics of the machine learning education.

We are “Machine Learning Byte” (https://www.machinelearningbyte....).

Let us know the issues/problems you face while studying and applying machine learning to the real-world problems on “Machine Learning Byte” (https://www.machinelearningbyte....). We are here to help, find solutions for you and learn more.

Would you like to know how can we practically apply the machine learning algorithms? Stay tuned as we will be covering the implementation of each and every algorithm in our next article.

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Happy learning!!

Image source : Google