What Is Machine Learning (ML)? When Should You Use ML?

What Is Machine Learning (ML)?

Machine learning is a type of Artificial intelligence (AI) that provides computers with the ability to learn data automatically without human interventions.

Machine learning is a method of data analysis that provides computers with the ability to learn without being explicitly programmed.

It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Over the last few years, machine learning has become more and more popular. As a result, the demand for machine learning specialists is growing too. Luckily, it is relatively easy to start learning the subject - for example, various types of online learning materials are available online, including ML training courses, guides, and online videos. All you need to do - is just to have a desire to learn this subject.

Machine learning is a simply a way of achieving Artificial intelligence (AI).

Machine learning was coined in 1959 by Arthur Samuel.

When Should You Use Machine Learning?

Using machine learning when you have a more complex task or problem involving a large amount of data and lots of variables, but no existing formula or equation.

Why Is Machine learning Important?

It's possible to quickly and automatically produce models that can analyse more complex data and faster delivery with more accurate results.

Organizations can make better decisions without human intervention.

What Is Deep Learning?

Deep Learning also known as Deep Neural Learning or Deep Neural Network.

Deep learning is a subset of Machine Learning in Artificial Intelligence (AI) that has networks which are capable of learning unsupervised from data that is unstructured or unlabeled.

How Machine Learning Works?

And

What Are some Popular Machine Learning Methods?

Broadly, there are 4 types of Machine Learning Algorithms -

1.      Supervised Learning - It used for learning algorithms are trained using labelled.

2.      Unsupervised Learning - It is used against data that has no historical labels.

3.      Reinforcement Learning - It is used for robotics, gaming and navigation.

4.      Semisupervised Learning - It is used for the same applications as supervised learning.

What's required to create good Machine Learning Systems?

Data preparation capabilities and Analyse more complex data.

1.      Algorithms – basic and advanced

2.      Automation and iterative processes

3.      Scalability

4.      Ensemble modeling

5.      Scalability

6.      Ensemble modeling

What Are The Common Machine Learning Algorithms?

Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem-

1.      Linear Regression

2.      Logistic Regression

3.      Decision Tree

4.      SVM

5.      Naive Bayes

6.      kNN

7.      K-Means

8.      Random Forest

9.      Dimensionality Reduction Algorithms

10.  Gradient Boosting algorithms

-          GBM

-          XGBoost

-          LightGBM

-          CatBoost

Why Should People Learn Machine Learning?

So should you learn machine learning?

According to Forbes, “Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn. Data scientist roles have grown over 650% since 2012, but currently, 35,000 people in the US have data science skills, while hundreds of companies are hiring for those roles.”