Last Updated on March 7, 2021

Hey, you are also entangled in the problem that where should we start with, machine learning or deep learning. Should I learn machine learning before deep learning?

Okay, this problem comes to the mind of every developer. Even I was confused about *what to learn first after Python Programming Language and Mathematics:- Machine Learning, or Deep Learning.*

I did some research for this question when I had to learn deep learning, and I will help you clear this doubt. So stay with me and read the full article.

**I would say that for deep learning, machine learning is not mandatory, but it will be required.** **How, machine learning or a set of algorithms has some concepts that will be discussed in deep learning that you should be familiar with. If you will learn some basics concepts before in machine learning then it will become easier for you to learn deep learning.**

There are many concepts that you need to know when you are deciding where to start from either. So what are those concepts that I need to learn to make deep learning easier?

Below I have mentioned those concepts, must read. But before that, let us know the basics of machine learning and deep learning.

## Machine Learning

Machine learning is a subset of artificial intelligence. It refers to a system that learns from data and is able to make accurate predictions. The more useful data you provide, the more accurate you will get the output.

Machine learning technology is also being used in many fields. We use machine learning algorithms to make our work easier in our daily life. **Such are: – healthcare, disease detection, face detection, spam detection, and many more.**

## Deep Learning

Deep Learning is the part of machine learning. In other words, it is the subset of machine learning. Deep Learning technology came in handy to solve very complex problems that are impossible to solve from machine learning algorithms.

**Deep Learning techniques use a specific set of algorithms i.e. based on artificial neural network (ANN) or deep neural networks. **The artificial neural networks are based on the functions of the human brain. How the human brain learns things and more.

The artificial neural network learn with the help of three layers:-

- Input Layer
- Hidden Layer
- Output Layer

Such examples where deep learning is used are:- **computer vision, text analysis, voice search, language translations, and more.**

Below this video will explain in more detail about the difference between machine learning and deep learning. If you are interested you can watch this video.

## Should a Machine learning beginners go straight for Deep learning

**The answer is NO. You should not go straight for deep learning. So it would be better if you study machine learning first.**

For deep learning, it will be required that you get to know some of the fundamentals and basics concepts of machine learning. But if you think that without knowing the fundamentals of machine learning, you can understand the concepts of deep learning, then you can go directly to deep learning. But it may be hard for you to get those concepts clearly.

Fields such as self-driving cars, computer vision, artificial neural networks or natural language processing uses deep learning methods. If you are focusing on such fields then you should start from deep learning.

In some fields where deep learning, as well as machine learning, are used equally, you should start with machine learning first.

As I told you earlier, deep learning is used to solve complex problems. **So what is important for you first, to solve simple problems and this is where you will use machine learning techniques.**

## Some Important machine learning concepts for deep learning

As you have known that for deep learning, you need to know the concepts of machine learning. Below are some machine learning concepts that will help you to get started in deep learning.

**Basics Principle of Machine Learning**

If you are starting with machine learning, then first learn the basic principle of machine learning. Any course, if you have enrolled, it will explain to you in the beginning but you may not understand it. So it is possible for you to watch some videos and examples that will give you an understanding of how machine learning works, how machine learning makes predictions using data. **Below is the video you should watch.**

**Machine Learning Algorithms**

There are some machine learning algorithms that you need to know before you dive into deep learning. You do not need to master every algorithm of machine learning.** Just master these algorithms which are commonly used – **Logistics Regression, Linear Regression, Support Vector Machines, Decision Trees, Random Forests and some activation functions.

**Data Sets**

Data sets are very important factors in machine learning. Whenever we perform a deep learning or machine learning model, we need a lot of data. There are many websites on the Internet from where you can collect Data Sets and use them. Kaggle is one of the best platforms where you will get many data sets for your project. This platform could be very useful for you, make sure that you have an account here. Others are – UCI Machine Learning Repository.

**Reinforcement Learning**

It is a type of machine learning in which an agent (A.I.) interacts with the environment and learns from the feedback that their minds give them the order to perform a certain action. In other words, Reinforcement learning learns from the mistake. And it learns from the mistake that it does, so that, that mistake does not recur in the future.

**Supervised and Unsupervised Learning**

In supervised learning, you train the machine using the labeled data(defined data) and you know what your output should be. It helps you correct your algorithm if it gives the wrong answer.

In unsupervised learning, the data collected is unlabelled(**undefined**) and you can’t sure what your output will be. So, your algorithm understands the pattern from the unlabelled data and gives the required output.

See the example of both learning here, Supervised and Unsupervised Learning.

Here this video will explain you every aspect of machine learning. You will get proper knowledge about how machine learning works, steps of machine learning and examples. You may watch it.

## Skills that you need for both machine learning and deep learning

Even if you start with machine learning and then jump for deep learning, it is very important for you to understand these two skills. First your programming and second mathematics.

**Programming**

Yes programming, here Python is the best option for you. Because it is easy to learn and it supports libraries for machine learning.

When you will start implementing the python language in machine learning. You need to know some libraries like – NumPy, Pandas, SciPy, Matplotlib, and more.

If you are looking for the best platform to learn Python, I would recommend the best platform, where it will teach you about Python programming to advance from the basics, whether you are learning for data science or Artificial Intelligence. And a book where you will get everything about python. You must have this book, otherwise, buy it now.

learn python programming – **(From Datacamp)**

Python Crash Course – **(Eric Matthes)**

**Mathematics**

In mathematics, **you must have an understanding of linear algebra, calculus, probability, and statistics.**

Now if you have decided to learn machine learning, I would strongly recommend you to learn from this platform. Khan Academy is the best platform where you can learn mathematics. It will help you to learn mathematics better. Therefore, you can understand and apply machine learning algorithms.

When you will learn these courses above then I would suggest you a machine learning and deep learning course by Andrew Ng and a book that you must have.

Machine Learning Course – **(By Andrew Ng)**

Deep Learning Specialization – **(By Andrew Ng)**

Many people usually prefer books to online courses. Don’t worry, I have some recommended books also. The book that I will recommend you is the best book for machine learning. It is one of the best seller books over the internet by **Aurélien Géron**. It will help you to gain an intuitive understanding of the concepts and tools for building intelligent systems. For deep learning, you can go for **Deep learning book by Ian Goodfellow**. This book is used in top universities across the world. You must have these books, otherwise, buy them now.

## Conclusion

I hope you have found the answer. Now you know what you need to do now, try your best in this field and just bust it.

Along with learning machine learning, work on some basics of machine learning projects. Create an account on Kaggle and join its community, complete projects, compete and get the chance to win prizes.

That’s all here. I hope you liked the article. If you have any questions, please let me know in the comment section.

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