There is no doubt that artificial intelligence is a very appealing and promising field today. Salaries in AI are frequently much higher than those of other tech professions, which is why more and more companies invest in AI. However, the beginnings appear to be the toughest for those new to AI – there are many online courses and resources, but it isn’t easy to know where to begin. But don’t worry – we are approaching this topic step by step. There is also a plan to follow that consists of 5 steps for learning AI about which we provide a tutorial explaining which courses will teach you the main objectives that target future specialists.
Step 1: Get Familiar with Coding Languages – Pay Attention to Python
Why Begin from Here?
Programming is the first and foremost skill in AI. This language is suitable for beginners and has a large number of libraries related to AI.
Useful Websites:
- Python for Everybody by Dr. Charles Severance (Coursera)—This is a great course for beginners and a great introduction to the language.
- What I did to learn Python and US Politics – introduction to Computer Science of Harvard (edX) – This course teaches elementary concepts of computer science in Python and many others, which is good for learning basic approaches to problem definition.
Step 2: Form Your Math Base – Start With Linear Algebra, Calculus, and Probability
Where is this focus coming from?
Math is fundamental to AI. To be properly trained and optimized for particular tasks, neural networks quantitatively and qualitatively require the intervention of linear algebra, differentiation, and optimization using calculus, while measures of probabilities are used where integrals are desired.
Suggested Resources:
- Essence of Linear Algebra (3Blue1Brown YouTube Series) – Explaining concepts and theorems of linear algebra in a straightforward manner using visual aids.
- Khan Academy – Information on calculus and probability available free of charge for users with any level of knowledge.
- Mathematics for Machine Learning offered by Imperial College London (Coursera) – One of the best courses in terms of level of coverage.
Step 3: Enter into the Zone Of Machine Learning Basics
Why is effective to Begin with Machine Learning?
Well, ML or Machine Learning as some might refer to it is a subset of Artificial Intelligence focus towards training computers to derive knowledge out of some data. So, grasping the concept of ML is a good start point to go further into researching AI’s work.
Top Courses to Consider:
- Machine Learning by Andrew Ng (Coursera) – A highly recommended course that covers the basics of machine learning, including supervised and unsupervised learning.
- Intro to Machine Learning with Python (Udacity) – A more hands-on approach using Python to build ML models.
Step 4: Deep Learning Fundamentals
Why Deep Learning?
Machine learning is an innovative area in AI that makes it possible to develop sophisticated services corresponding to image identification and voice recognition. You learn skills that will improve the possibility of implementing and working with other forms and levels of AI.
Recommended Courses
- Neural Networks; Convolutional Networks; Sequence Models – A complete set of courses offered by Andrew Ng in Coursera that teaches everything about deep learning starting from neural networks up to convolution and sequence.
- Fast.ai – This is also a great course in deep learning but is famous for explaining deep learning as practically as possible while working with the data.
Step 5: Working on Some Real Life Projects to Aspire Your Innovativeness
Why do Projects Matter?
Yes, we gain theoretical knowledge, and that is OK, but getting down to building actual projects help cement the knowledge and enrich your portfolio. In the field of AI job experience plays an important role in getting a job.
Project ideas:
- Chatbot – As a form of AI application, one should incorporate easy techniques like the natural language processing (NLP) to develop a simple chatbot.
- Predictive Modeling – Develop a model that will be able to predict stock prices, housing prices, or some other related interesting data set.
Resources for Datasets:
- Kaggle – Provides a great number of datasets and competitions for AI initiatives.
- Google Dataset Search – It works more like Google where users can type their interest in datasets with specific characteristics in order to obtain results that are not easy to come across.
