What is Machine Learning ? – A Comprehensive Guide

Most of the time we hear people asking questions like

What is Machine Learning?

How do I Learn Machine Learning?

What are the Career Opportunities in Machine Learning?

If you do have similar questions then this post will surely answer all your doubts. If you are in a hurry to read this article then just wait and listen. This is not just a 2-minute read article, its a complete guide for learning Machine Learning.

You can bookmark this article and get back here when you are ready to collect a lot of information. I have seen multiple articles which leaves many questions unanswered but this is not the case here.

So below are the questions and their answers in complete details:

What is Machine Learning?

Machine Learning is the base part of Artificial Intelligence which lets any computer program to learn things. The program once coded could be trained over data sets which then could take self decisions.

This way Machine Learning algorithms could work without depending on Human intervention. The main way for training these algorithms is to provide data through which the algorithms tries to find patterns.

These patterns could be used to take future decisions. Same way Human beings take decisions based on the pattern and Human intelligence.

The process of learning is automated but it could also be monitored for providing additional annotations to the algorithm.

This is your answer to the question ‘What is Machine Learning?’

Now you might be thinking that how a simple Machine program could be trained to work on its own. You will get the answer to this question in the below segment.

How it is Performed? – Types of Machine Learning

The algorithms are mainly classified into these three types:

1. Supervised Learning: In Supervised learning the algorithms are trained on already collected data. This data contains two main things, Input values and a Target set which contains the end Target notation.

For example, there is an input image of Cat with Goal set containing category Animal. The algorithm will then be provided data set containing millions of different Cat images with the same category name Cat.

After the completion of training, the algorithm would be able to identify Cats from totally new images. This is how Supervised Machine learning works and algorithms are trained with Input and a Target.

2. Unsupervised Learning: In this type of learning there is no trainer or teacher for the algorithm. It starts classifying the incoming data based on various patterns. These patterns will then help the Machine Learning algorithm to perform various tasks.

Clustering is a type of unsupervised learning where the algorithms build clusters of similar patterns to identify classes. The main thing with clustering is that the system itself finds the patterns from data and we don’t provide any labels.

Twitter sentiment analysis is an example of Unsupervised learning. In this process, the tweets from Social media twitter are scanned by algorithms and clustered in different categories. For example, Tweets that promotional, Tweets with hate comments, etc.

3. Semi-Supervised Learning: This type of learning is widely used in many types of algorithms. Here the data is based on both kinds of inputs, one with targets and one with targets. With the help of both kinds of data, the algorithm can build better and accurate predictive methods.

Semi-Supervised learning could be helpful when the labeled data is not enough for predictions. Using the labeled data the other data sets could also be cleaned and clustered easily.

4. Reinforcement Learning: It is the type of learning with a reward system. The reward could both be negative and positive to the process of execution. If the algorithm is taking better decisions and has good performance it is rewarded with better feedback.

Otherwise, they get a negative reward which tells them that the performance is not accurate. Reinforcement Machine learning has various applications in Computer resource allocation, robotics, and Artificial Intelligence.

So these were some of the types of Machine Learning algorithms. Now we are heading forward to another question.

How to Learn Machine Learning? – Complete Career Path

As you have already guessed that Machine Learning is not just a single thing to learn. It even encompasses various unseen applications of its technology.

Being a field of research, New discoveries are taking place with each passing day. Machine Learning Engineers are doing researches on finding profitable and efficient uses of this field to help mankind in all the ways possible.

Thus you will have to keep a mindset to learn new things every now and then. Below is the step by step career path for getting into the field and not getting lost into so much of knowledge. These steps are all covered in any offline or online course that the institutes provide:

Step 1 – Learning Maths and Statistics

For becoming a Machine Learning Engineer you should know basic and advance concepts of Mathematics. Though it would not be used at the early stage of learning its always good to start from basics.

Along with the Mathematical approach, you will also be learning Statistics concepts like Mean, Median, Mode, Probability, and other advanced stats too.

Mathematics would include Linear Algebra, Calculus, Optimization Theory also known as Lagrange multipliers, etc. You can skip this step if you are not good at mathematics for now. But you will surely face all these topics and concepts while working on Advanced ML algorithms.

Step 2 – Learning a Programming Language

A programming language is necessary for working on Building algorithms. Mainly Python and R language are used for Machine Language. You can choose any one of them and we have already covered an article for Learning Python from YouTube.

Python is a simple and effective programming language. It provides multiple libraries for building ML algorithms. Its ability to handle large data sets makes it efficient for this field.

Step 3 – Learn to Handle Database

You will need to handle tons of data stored in databases. For this, it is recommended to learn database concepts of MySQL, MongoDB, Oracle DB, etc.

There are many online resources for Learning database management and performing data storage and recovery. While you will be taking a Machine Learning course you will surely be taught to handle databases.

Step 4 – Understanding Libraries for Machine Learning

Either you have chosen Python or R language for ML working you will come across libraries. These are prebuilt modules that could be used to perform various tasks and making it easy to perform functions.
You can learn about all the major Python libraries for Machine Learning from our previous posts. Once you are ready with these libraries and their functions you can move ahead to What is Machine Learning?

Step 5 – Building Machine Learning Algorithms

After having enough working knowledge with the above steps you can learn to build ML algorithms. They are nothing but bits of codes coded in programming languages. The size and complexity of these algorithms increase with the complexity of tasks.

But if you are comfortable in that programming language and its libraries then it will not be a problem.

Step 6 – Learning Important ML Tools

There are multiple tools and platforms available for making ML easy and efficient. You must have hands-on experience with major tools including Tensorflow, Google Cloud, Amazon AWS, TensorBoard, PyTorch, etc.

Step 7 – Going Deeper into Artificial Intelligence

Although the above steps are good for basic Machine Learning functions there is a lot more. After learning all the above things practically you can move into the following subsets:

• Neural Networks
• Natural Language Processing
• Recommendation Systems
• Deep Learning

So this was all about the career path and roadmap for Machine Learning Engineer.

What are the Jobs in the Machine Learning field?

Here is a list of jobs or career opportunities in this field:

• Machine Learning Engineer
• Data Mining Engineer
• Data Analyst
• BI Developer
• ML Researcher
• Data Scientist
• NLP Engineer

We will be creating a separate article for learning about all these fields. If you are still confused about the salary package for a Machine Learning Engineer then don’t worry.

They are paid one of the highest in hand packages in the tech field. The salary packages start from 6-10 LPA and go up with your skills and research ability.

This was all about your question What is Machine Learning? and answers all your doubts.

Do share this post with your friends 🙂