Hello guys, if you want to learn Deep Learning and looking for a complete RoadMap to find out all essential skills, tools, and technologies a professional Deep Learning expert should know then you have come to the right place. Earlier, I have shared The 2024 Python Programmer RoadMap, The 2024 Machine Learning RoadMap and Data Engineering RoadMap and in this article, I am going to share with you The 2024 Deep Learning RoadMap. You can consider the deep learning field as a subset of machine learning that uses neural networks to gain experience from the data instead of the built-in algorithm in machine learning. The application of this field is endless. You can make computer vision applications, understand the human language, translate languages, and more. This article will help you become a deep learning engineer step by step.

__The 2024 Deep Learning RoadMap for ML Engineer__

Here is the roadmap which you can use to learn Deep Learning concepts in 2024 and become a Machine Learning Engineer in 2024

### 1. Programming Languages

There are many programming languages that you can use to build your deep learning model, such as Python, R, C++, etc. I would suggest learning only these two languages, Python and R, since they are the most used among developers.

**2.1. ****Python**

If you choose to learn Python for Deep learning then I suggest you to join the **Python for Everybody course** on Coursera. This is a fantastic course offered by the Michigan University to learn python language for beginners to be intermediate users in just a few months.

You will start with the basics of this language, like variables, data structure, and statements. Then use this language to access the web, interact with the database, and more.

**2.2. ****R Programming**

And, if you choose to use R programming language then you can start with the R programming course on Coursera. This course aims to teach beginners this language called R to become an advanced users in more than 10 hours of video content. You will learn to manipulate and analyze data with R, install external libraries to add more functions to your code, plot data, the basics of R, etc.

Apart from Python, and R, you can also use other programming language like Java, Scala, and C++ for creating Machine Learning and deep learning model but these two are preferred. I have also shared them on my earlier article about 5 best programming language for AI and deep learning

### 2. Math Skills

The first thing a deep learning developer should understand before even starting to be involved in this field has skills in math. You don’t need to have a Ph.D. degree in math, but at least you need to understand the basics of a few things such as probabilities, calculus, and linear algebra.

**1.1. ****Mathematics for Machine Learning**

This course will help you understand the basics of the math skills needed for learning machine learning. The Deep learning field is a subset of machine learning, and you will gain these skills in 2 months and target beginners.

You will learn linear regression & algebra, vector calculus, gradient descent, etc. I also recommend programmers to learn Maths again especially if they want to become a Data Engineer.

### 3. Working With Data

Another critical skill is working with data, such as cleaning the data before feeding it to the deep learning model, editing them, removing duplicate values, empty values, null values, etc. You could also analyze this data to get more understanding about them.

**3.1. ****Data Analysis with Pandas**

This library called pandas is used with python language and is used a lot among data science and data analyzers. And, if you want to learn Pandas then you can join Data Analysis with Pandas course on Coursera.

This 21 hours course will help you learn hundreds of pandas commands to work with data, perform filtering, work with text data, etc.

The previous course about the R language will also teach you how to work with data, and if you are planning to take python for deep learning, you also need to take this pandas course.

### 4. Machine Learning

Before you jump to learn deep learning and making neural networks, you must learn machine learning. This field will teach you the basics of algorithms built for making a machine learning model and learning from the data. Many machine learning libraries are available to use, but I will suggest learning the scikit-learn.

**4.1. ****Machine Learning 101 with Scikit-learn**

This is a miniature course of 5 hours video and will teach you the basics of this library and how to create many different models such as linear regression, logistic regression, and cluster analysis. This library is more than what you will learn in this course, but you will have to explore it more if you want a deep understanding of machine learning.

### 5. Deep Learning

Now that you understand machine learning algorithms and how they work and learn from data, it’s time to start making your own deep learning model or artificial neural networks to learn from the data. There are a lot of libraries you can use for making a deep learning model, such as TensorFlow, Keras, PyTorch, CNTK, and more.

**5.1. ****Tensorflow 2.0**

This comprehensive course will teach you the TensorFlow library in 22 hours of videos. You will gain the skills to make many different deep learning models such as convolutional neural networks, learning deep reinforcement learning, making computer vision programs, recurrent neural networks, and more.

### Conclusion

That's all about** 2024 Deep Learning RoadMap**. If you want to become a Machine Learning Engineer and want to learn Deep Learning then you can follow along this RoadMap. Apart from essential concepts and tools, I have also mentioned useful resources to learn Deep Learning along the way.

Once again thanks for reading! We’ve seen the steps you need to take to be a deep learning engineer, and some companies may ask you to have more skills such as data visualization and a deep understanding of statistics or any other skills, but what you’ve learned in this article can make you a beginner in this field.

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