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Learn Machine Learning — A Complete Guide for Beginners.

by Nitin Sharma 8 days ago in how to

Machine Learning is easy if you learn from the right one.

Learn Machine Learning — A Complete Guide for Beginners.
Source: Pixabay

When most people hear about Machine Learning they imagine Iron man (Character of Tony Stark). But Machine Learning is not just a character of Iron man it is much more than that. Also, Machine Learning is not a new concept it has been around for decades.

Before understanding let’s talk about some important facts and stats of Machine Learning in our real life.

1. According to Statista, the below graph shows current and anticipated machine learning maturity levels in companies.

Source: Statista

2. According to Statista, The NLP(Natural Language Processing) market is predicted to be almost 14 times larger in 2025 than it was in 2017, increasing from around three billion U.S. dollars in 2017 to over 43 billion in 2025.

3. According to Forbes, The global machine learning market was valued at $1.58B in 2017 and is expected to reach $20.83B in 2024, growing at a CAGR of 44.06% between 2017 and 2024.

4. According to Statista, In 2020, there will be 4.2 billion digital voice assistants being used in devices around the world. Forecasts suggest that by 2024, the number of digital voice assistants will reach 8.4 billion units — a number higher than the world’s population.

5. According to Forbes, 80% Of Enterprises Are Investing In AI Today.

What is Machine Learning?

At present, the adoption of Machine Learning has increased tremendously among every business out there.

But it was started before the 21st century.

When Arthur Samuel(In 1959), an American explorer in the field of artificial intelligence coined the term Machine Learning for the first time.

So we have now gone in 1959 to know its existence but What exactly is Machine Learning?

According to Arthur Samuel, Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed. ML(Machine Learning) can be understood as computational methods that use the experience to improve performance or to make accurate predictions.

Not able to understand, it’s okay let’s start in a more child’s play.

Simply it means to teach a machine to learn i.e Machine + Learning = Machine Learning.

When you look at websites like Facebook, Amazon, Netflix you will be going to see multiple machine learning models working for user’s benefit. For example recommendation of movies on Netflix.

You can see Machine Learning in various applications like Image Recognition, Speech Recognition, Medical diagnosis, the Banking sector, and tons of others as well.

Why Machine Learning?

It is not possible without Machine Learning to create Image Recognition, Speech Recognition. Every one of us uses Email and many of you know about the Spam folder. But you know that Machine Learning is used for Spam filtering.

Machine Learning helps us to write an algorithm that learns by itself so we don’t have to update it in the future. It learns based on data and changes its working.

Finally, Machine Learning can help humans to learn. For example, a Machine Learning algorithm can be used for spam filtering. For spam filtering, we write and use an algorithm (based on spam words like free, amazing) and send it to spam folders. Then spammers try to use other words due to which we have to write other algorithms. It’s become complicated.

So here, Machine Learning came into existence and detect the different words that are used by spammers again and again. Machine Learning detects the word and changes its algorithms to the new words used by spammers. After that, based on collected data humans can able to find the new words used by a spammer and learn from them.

There are special kinds of machine learning algorithms as well which create an output for input, it has never seen before, without any human intervention.

Machine Learning can also be used for Data Mining.

Types of Machine Learning Systems.

Machine learning can be broadly classified into three types:

1. Supervised learning: In this type of classification, labeled data is provided to the machine. So that it gives a decision, for example, providing labeled data in the form of images of dogs to a device can quickly learn to identify dogs.

Here are some of the most critical supervised learning algorithms.

* k-Nearest Neighbors

* Linear Regression

* Logistic Regression

* Support Vector Machines

* Decision Trees and Random Forests

2. Unsupervised learning: In this type, Unlabelled data is provided in a group/cluster to the machine. The machine is capable of learning with this Unlabelled data.

For example, in Unsupervised learning, Workers in a company are group together according to their specifications based on some algorithms.

Some algorithms for Unsupervised learning.

* Clustering

1. k-Means

2. Hierarchical Cluster Analysis

3. Expectation Maximization

* Visualization and dimensionality reduction

1. Principal Component Analysis

2. Kernel PCA

3. t-distributed Stochastic Neighbor Embedding

* Association rule learning

1. Apriori

2. Eclat

3. Reinforcement learning: Reinforcement learning works in different ways. Here, the learning system is called an agent. The agent observes the environment and performs some tasks.

If tasks are done in the right manner/succeeded, the reward is given in return, or else penalties for the Job failed. For example, Robot uses Reinforcement learning to learn to walk.

Data plays a crucial role in Machine Learning.

We all know Data scientists require data to work on?

Does Machine learning also require data?

Yes, Machine Learning also works with data. The more the data, the more accurate the machine learning model will be.

It’s very crucial to know about the data you are working with before you begin a model. It is not effective to choose an algorithm and deal with data.

Before building a model you have to at least know the answer to the following question:

1. How much data do I need? Is it efficient?

2. Is there any missing data?

3. Is the data collected can answer the question?

How to learn?

Machine learning is a subset of Artificial Intelligence. So you are digging into a whole new concept and learning a specific topic might confuse you at the beginning. Practice will help you to grasp.

Here I will be providing the best resource you can follow to learn Machine Learning which personally I have found out.

Learn from

I have used Python Machine Learning Tutorial. Other than that I have followed Kaggle, Fast.ai

You can also learn from

1. Google: Yes…yes you can able to learn it from Google.

2. Videos: Learner loves video content.

a) Machine Learning by Andrew Ng

b) Machine Learning Crash course

c) Siraj Raval (Youtube Channel)

3. Books: For anyone who wants to learn from books, I have found both the below book interesting for beginners to become an expert. Read both the books orders so you can able to learn precisely.

a) Introduction to Machine Learning with Python: A Guide for Data Scientists.

b) Hands-On Machine Learning with Scikit-Learn, Keras, and Tensor.

Let’s Wrap Up

I hope that all of you find it interesting. Machine learning is undeniably the most powerful technology out there. There’s no doubt, it will continue to make headlines in the future. This article is designed to grasp the reader some valuable information about Machine Learning and clears every fundamental topic without being too arduous.

Don’t hesitate to leave feedback or doubts.

That’s all for now! Keep an eye to update yourself — Thanks.

If you like it, you can also read the below ones.

This was originally published at Medium.

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Nitin Sharma
Nitin Sharma
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Nitin Sharma

An Engineer, A writer and a Web Developer.

See all posts by Nitin Sharma