DEAR Ashifur Rahman

Please consider these references such as

https://www.zendesk.com/blog/machine-learning-and-deep-learning/

Machine learning and deep learning, DOI:10.1007/s12525-021-00475-2

that they are effective for your answer

good luck

As mentioned by Kurte Benke, DL is nothing but an application of Machine Learning for training data. This application is popularized recently for some particular data with big volume of data at hand. If you have data lower than some particular thresholds, DL app for training will have disappointing performance in comparison to other comparative ML applications. The best way to start you journey from the classical ways of ML. I would suggest you the following fantastic books to follow for your build up.

Yes, you can use deep learning, but you need a lot of reading in the field of neural networks

Machine learning is a vast area, and you don't need to learn everything in it. But, there are some machine learning concepts that you should be aware of before you jump into deep learning. It is not mandatory that you should learn these concepts first. Deep learning is mostly used for solving complex problems.

https://machinelearningmastery.com/types-of-learning-in-machine-learning/

I agree with everyone here. The books recommended by Muhammad Ali are an excellent reference for anyone who wants to learn Machine Learning and its underlying mathematics instead of just using an over-the-counter model. Once you're comfortable with classical machine learning concepts, I think having a deeper dive into perceptrons, neural networks then deep learning would be the path I recommend.

The answer is a definitive no. There are so many machine learning fundamentals that are essential to master in order to be a proper machine learning expert. Working with deep learning becomes so much easier once you grasp the fundamentals.

machine learning is prerequisite fir understanding deep learning, so one should have not only the basic knowledge of machine learning concepts but thorough knowledge of machine learning algorithms

Shikha Gupta Sales Aribe Jr. Aroua Gharbi Masoum Mohammadi Gharagoz Dhafar Allayla Muhammad Ali Shima Shafiee Kurt Benke Thanks to all.

It would benefit you to understand machine learning fundamentals before going to deep learning. As mentioned above, shallow models are still more generalized for handling many real-world problems, especially those based on small data. Besides, suitable theories that can assist you to jump quickly into deep learning may be scarce.

The answer is NO. You should not go straight for deep learning. For deep learning, it will be required that you get to know some of the fundamentals and basics concepts of machine learning.

No and Yes. As others have already mentioned, Machine Learning is very vast and you can't learn every ML algorithm even if you wanted to. As to will you be needing ML in order to dive into deep learning, in my opinion, No, Of-course if you were to enroll in a good Deep Learning course that is. But if you learn ML, at least parts of it, it would make your Deep Learning journey very easier and would eliminate the need for an expensive Deep Learning course and you can self-study the concepts because you are doing it the right way and following the roadmap correctly. One little advice is that don't try to learn everything and learn the concepts on the go as you need them. Hope that helps.

Dear Ashifur Rahman,

First of all, I agree with the majority here (especially, Kurt Benke, Masoum Mohammadi Gharagoz, Ali Khalili, Milad Vazan, Shima Shafiee, Stanley Ebhohimhen Abhadiomhen).

If you are a beginner, I would say no. It takes a while to understand the following aspects in details before you applied it in deep learning.

1. Regularization.

2. Underfitting and Overfitting and how to identify them

3. Various cost functions

4. A thorough understanding of stochastic gradient descent

5. Hyper parameter tuning

6. Cross validation techniques

The above ones are the most important. A basic understanding of how neural networks work is enough to get into deep learning. It may be tempting to say that exploring deep learning implicitly covers the above, but the other details in deep learning algorithms may distract the mind. It is worth spending a month thoroughly understanding the above as part of other algorithms such as linear regression and then coming into deep learning.

So, go out there and build something. Good luck!

Answering the question “How do I learn Machine Learning” I will only say that there is no way to learn it, you can only become better at it by practicing it.

So, to start with machine learning, there are some prerequisites that you should know to understand the concepts behind it:

• Linear Algebra
• Differential and Integral Calculus
• Statistics and Deviations
• Programming Language(Python or/and R)

So once you are done with these prerequisites, then you can start learning with the concepts in the same fashion as I have told :

• Regression
• Classification (Both Sample Based and Probabilistic)
• Decision Trees
• Neural Networks

So, after doing some basic research on these topics, I will personally suggest you to go with the Machine Learning and Data Science Course on Coding Ninjas. Now you will ask me why, so now answering that question :

The course contains all the concepts as well as a hands-on practice of all the popular machine learning and data science concepts.

The course contains around 9 projects, including :

Twitter Sentimental Analysis, Price Prediction using Web Scrapping, Text Classification, Music Notes Generator...(The course contains Videos, Multiple choice questions, Hands-On practice tools, Coding Questions and Reading Material.) The course not only focus on Hands-On learning but also focuses on the theoretical part like derivations and conceptual understanding. So, once you get done with this you can start practicing some machine learning skill on a dataset from websites like Kaggle and once you get proficient and comfortable in that then there is nothing stopping you, the sky is the limit, you can write research and scientific papers, take part in live competition and win some laurels as well as cash prizes.

So, go out there and build something.

Good luck and Happy Model Training !

Samawel JABALLI

Before machine learning. One should also know statistics for data analysis

Dear Dr Ashifur Rahman . See the following useful RG link: Article Machine learning and deep learning

Kindly see also the following useful RG link: Article Comparing Machine Learning and Deep Learning Methods for Rea...

Also check please the following very good RG link: Article The Understanding of Deep Learning: A Comprehensive Review

It is best to have understanding on basic concepts first regarding the machine learning instead of fast tracking at deep learning

DEAR Ashifur Rahman

Machine learning is a method of data analysis that automates analytical model building. 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.

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised

Book From Machine Learning to Deep Learning

GOOD LUCK

Machine learning is the science of getting computers to learn automatically. It's a form of artificial intelligence (AI) that allows computers to act like humans, and improve their learning as they encounter more data. Deep learning requires a lot of hammering before you start to see some results. It typically takes years and lots of mistakes until you start seeing any result which resembles what you were hoping for at the beginning when you started your journey in machine learning (or data science). This also means it will take a lot of time, effort, and motivation from the aspiring data scientist to stick with it all the way through till he or she sees decent results on their end. Also you can't learn deep learning without machine learning. Deep learning lives inside of machine learning so theoretically, it's impossible. Focus more on making sure you get the basics across, such as understanding linear algebra and statistics. After that get into data visualization in order to familiarize you with some basic methods for visualizing your data e.g scatter plot, histogram, etc, and so on. It all depends on your end goal, if you want to experience the power of modern computer then go for Deep learning, but in DL you need some basic machine learning concepts. If you want to know how machines predict the weather or make their own artificial intelligence, then learn ML.

Thanks all

Ashifur Rahman

Deep Learning is part for Machine Learning. You have start from machine learning so you will understand deep learning if you learn algorithms in machine learning.

Course for Beginners:

Self-Paced Online Courses (mathworks.com)

https://matlabacademy.mathworks.com/details/machine-learning-with-matlab/mlml

https://matlabacademy.mathworks.com/details/deep-learning-with-matlab/mldl

https://www.coursera.org/learn/ai-for-everyone

https://www.simplilearn.com/learn-machine-learning-basics-skillup?msclkid=9b12f0c7af6211ec877ffb38ee57f7af

List of Courses

https://medium.com/javarevisited/10-free-machine-learning-courses-for-beginners-181f83b4c816

Links

http://www.kdnuggets.com/

Hope, this information is helpful. Keep Learning...

Ashifur Rahman I think all answer is very right. And you need to learn basic machine learning first then go to deep learning.