Roadmap to study AI, Machine Learning, and Deep Machine Learning

AI also known as Artificial Intelligence, Machine learning in short written as ML, and deep learning (DL) are a few of the top three fast-emerging, great, and intriguing technological disciplines containing a wide range of implementations i.e. applications like self-driving automobiles and face recognition systems. Because of their complexities, understanding these topics may appear difficult. Yet, success in these domains requires a solid foundation in computer science, mathematics, and statistics. Moreover, familiarity with common libraries and modeling tools is required.

This article outlines a learning route for AI, ML, and DL, outlining key ideas, tools, and methodologies. This roadmap provides a clear path for starting your learning journey and equips you with the abilities needed to flourish in these subjects, without repeating any knowledge from other sources.

Road Map

Here is a roadmap to help you get started −

1. Understand the Basics

Before delving into the more complicated components of AI, it is critical to grasp the fundamentals. Linear algebra, calculus, statistics, and probability theory are all included. You should also be comfortable with programming languages like Python, Java, and C++. A solid foundation in mathematics and programming can help you understand AI topics more readily.

2. Learn the Foundations of AI

You may begin learning the principles of AI once you have a good foundation in mathematics and programming. Understanding the many forms of learning, such as supervised, unsupervised, and reinforcement learning, is essential. You'll also need to familiarise yourself with decision trees and clustering methods. On these topics, there are several free online courses and tutorials accessible.

3. Study Machine Learning

When you've grasped the fundamentals of AI, you may progress to Machine Learning. You'll need to understand the methods for regression, classification, and clustering. You'll also need to understand how to preprocess data, do feature engineering, and choose a model. There are also several online courses and tutorials available on these subjects.

4. Understand Deep Learning

Deep Learning is a major Machine Learning (ML) attempt that learns data using neural networks inspired by the human brain. Backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders are all topics that must be understood before diving into Deep Learning. Tensorflow and PyTorch are two popular deep-learning libraries. Understanding deep learning is crucial since it is used in many disciplines, including natural language processing, computer vision, and many more.

5. Learn About Natural Language Processing

It is a branch of AI that can be solved with the help of ML and deep learning. It deals with the understanding by computer systems that what the language is trying to say i.e. understanding it and interpreting words and phrases. Tokenization (splitting sentences into tokens), stemming (turning each word to its basic form), part-of-speech tagging (assigning a part of speech to each dish), and named entity identification are all abilities you'll need. The NLTK library is a well-known NLP library. Learning NLP may help you design chatbots, sentiment analysis, and other applications.

6. Study Computer Vision

Computer Vision is the study of pictures and movies. You'll need to learn about picture categorization, feature extraction, and object detection. OpenCV is a well-known computer vision library. Image and video processing has become a crucial ability for AI specialists due to the proliferation of cameras.

7. Practice, Practice, Practice

It is vital to put your newfound knowledge into action. Work on small projects and apply your expertise to real-world problems. Kaggle is an excellent platform for discovering datasets and competing against other data scientists. Participating in hackathons and designing applications might help you enhance your skills.

8. Keep up with the Latest Research

AI is a fast-changing topic, and it is critical to stay up to date on the newest research and breakthroughs in the field. Attend conferences and study research papers to keep current. Keeping up with the newest research might help you develop creative solutions.

9. Build a Portfolio

Creating a portfolio of your work and achievements will help you demonstrate your abilities and stand out to potential employers. You may build a website for your portfolio or upload your creations to GitHub. Possessing a portfolio showcases your practical talents and can help you find a job.

10. Network with Other

Now, it's important to connect with other people in the field of AI because they can motivate you, provide you with great opportunities, etc. To build your network you have to attend meetings, conferences, events, etc where you may meet other professionals in your area. Participate in online forums and join various online groups and ask questions. Never stops asking the question, it is one of the best ways to make a connection in groups and if you receive comments do acknowledge them. Try to answer as many questions as possible in the groups. Networking may assist you in finding career prospects as well as learning from people in the sector.

Conclusion

Learning AI, machine learning, and deep learning can seem overwhelming, but a systematic approach can help. By building a strong foundation in computer science, mathematics, and statistics, and learning to use popular libraries and tools, one can develop the skills needed to excel in these exciting and rapidly evolving fields. Following this roadmap can help you start your learning journey and equip you with the knowledge and expertise to thrive in AI, ML, and DL.