How to Start Your AI Journey: A Beginner’s Guide

Greetings Peeps!

So it's 2023! and Artificial Intelligence has largely taken over the big market. From task automation to serving as your personal assistant, AI has been enormously successful in all of these sectors and is quickly progressing.

AI is one of the most exciting and rapidly evolving fields of technology, with applications ranging from entertainment to health care to education. If you are interested in learning AI, you might be wondering how to get started and what skills you need.

In this article, we will provide some tips and resources to help you begin your journey of learning AI. Whether you are a student, a professional, or a hobbyist, you can find something useful and inspiring in this article.

AI for Everyone

As the name suggests, this is one of the introductory course that anyone can take to start off their AI journey. It is specially designed for a non-technical person who wants to become better at using AI at their startup or organization.

The course is taught by one of the finest instructor in the world of AI, Andrew NG. This course will teach you:

The basic concepts and terms of AI, such as neural networks, machine learning, deep learning, and data science

• The strengths and limitations of AI and how to identify potential use cases for your organization

• The steps and skills involved in building AI projects and how to collaborate with AI experts

• The best practices and strategies for implementing AI in your business and how to address ethical and social issues related to AI

How to build your Career in AI

Another beginner and practical guide book from Andrew NG. This book is suitable for anyone who wants to learn about AI, whether you are a technical or non-technical person.

• How to develop core AI skills, such as coding, math, and machine learning

• How to find and work on AI projects that will showcase your abilities and help you learn

• How to prepare for AI interviews and land your dream job

• How to network with other AI enthusiasts and professionals and join the AI community

• How to deal with common challenges and doubts that AI learners face, such as imposter syndrome and ethical dilemmas

Stanford CS221: Artificial Intelligence: Principles and Techniques

This series is a collection of videos from Stanford University’s course on Artificial Intelligence: Principles and Techniques. The course covers topics such as machine learning, search, game playing, Markov decision processes, constraint satisfaction, logic, and natural language processing. The playlist contains 56 videos, each ranging from 10 to 70 minutes in length. The videos are lectures by the instructors of the course, who are experts in the field of AI.

Introductory Specialization Courses from DeepLearning.AI

I have taken their courses and they are amazing. I think DeepLearning.AI offers courses that make complex problems easy to understand and learn. Many people have given positive feedback about the different courses they provide.

Machine Learning Specialization

This course is meant to teach the foundational concepts and practical skills to apply machine learning to complex real-world challenges.

This Specialization will teach you how to:

• Use NumPy and scikit-learn to build and train machine learning models for prediction and classification, such as linear and logistic regression.

• Use TensorFlow to build and train a neural network for multi-class classification.

• Apply best practices for machine learning development to ensure that your models are generalizable and robust.

• Use decision trees and tree ensemble methods, such as random forests and boosted trees, to handle complex and non-linear data.

• Use unsupervised learning techniques, such as clustering and anomaly detection, to discover patterns and outliers in data.

• Build recommender systems that can suggest items to users based on their preferences and behavior.

• Build a deep reinforcement learning model that can learn from its own actions and rewards.

Mathematics for Machine Learning and Data Science

The fundamental mathematics toolkit of machine learning:

• calculus

• linear algebra

• statistics

  Learners will be able to learn the above skills in the beginner-friendly specialization Mathematics for Machine Learning and Data Science. This specialization is suitable for those who want to learn the essential concepts and practical skills to apply machine learning to complex real-world challenges.

Finally,

AI for good

This beginner level course teaches those from any background about making a positive impact on society and the surroundings. It provides the tools and knowledge that you will require to work on AI for good initiatives.

Note: Most of the above courses are taught on coursera which will require a subscription fee of $49 per month. However you can apply for financial aid if you can’t afford the subscription.

Apart from the above courses, there are multiple courses that are available to watch and learn free of cost. Just go over the internet and there will be plenty of resources and materials to guide you on your journey towards AI.

Have a great learning experience!