What Is Machine Learning (ML)?
And
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?
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.”