AI for Beginners
A Step-by-Step Guide to Learning AI from Scratch in a 12-week, 24-lesson curriculum.
I recently stumbled upon an amazing resource for learning AI as a beginner! Even if you're just starting out and have no experience in ML/AI, this guide will be invaluable.
In this post, I want to share my recommendation on how to use this resource, explain its use case, and also share some new job opportunities.
Overview of AI-For-Beginners
This resource is structured as a 12-week, 24-lesson curriculum specifically designed for beginners learning AI. It covers key AI/ML concepts, practical coding exercises, and real-world applications, making it one of the best places to start your AI journey.
What You’ll Learn
Foundational AI Concepts
Linear algebra, probability, and calculus basics for machine learning
Machine Learning
Supervised & unsupervised learning, neural networks, and model training
Deep Learning
Neural networks, CNNs, RNNs, and transformers
Natural Language Processing (NLP)
Sentiment analysis, text classification, and language models
Computer Vision
Image classification, object detection, and segmentation
Reinforcement Learning
Training agents with reward-based learning
Ethical AI & Responsible AI
Bias detection, fairness, and explainability
One of the standout features of this curriculum is its focus on hands-on learning. Each lesson not only provides theoretical knowledge but also includes coding exercises that reinforce concepts. By working through real-world problems, you'll gain practical experience that will be invaluable when applying AI techniques in your projects or career.
Another key advantage of this curriculum is its accessibility. Many AI resources assume prior knowledge of advanced mathematics or programming, which can be discouraging for newcomers. However, this guide is designed to ease learners into complex topics, providing step-by-step explanations and hands-on projects to ensure comprehension. With structured lessons and real-world applications, you'll gradually build confidence in working with AI technologies.
This resource is also ideal for students, developers, and professionals looking to transition into AI-related roles. Whether you're aiming for a career in data science, machine learning engineering, or AI research, this guide will help you build a solid foundation.
I recommend dedicating at least 3-5 hours per week to go through the lessons and implement the coding exercises. By staying consistent, you’ll build a strong AI foundation in just a few months.
Additional Resources to Deepen Your Knowledge
Building a solid AI foundation requires continuous learning and exposure to diverse concepts. To supplement the AI-For-Beginners curriculum, I recommend exploring additional resources that cover specialized AI domains and their real-world applications. These resources will help you develop a well-rounded understanding of AI and enhance your practical skills.
To complement this AI-For-Beginners guide, here are other valuable learning resources that I highly recommend:
[Generative AI for Beginners] – Learn about generative models, large language models (LLMs), and how to build AI-generated text, images, and videos. This resource is excellent for those interested in AI creativity, chatbots, and content generation.
[AI Agents for Beginners] - Understand how AI agents work, their decision-making processes, and how to build autonomous systems. This guide dives into reinforcement learning, decision trees, and multi-agent interactions, making it ideal for those interested in robotics or AI-driven automation.
[Data Science for Beginners] – Covers essential data analysis, visualization, and statistics. Data science is a critical skill for AI engineers, helping them preprocess data, identify patterns, and create meaningful insights from large datasets.
[ML for Beginners] – Focused on foundational machine learning techniques such as regression, classification, clustering, and optimization. This resource is perfect for understanding core ML algorithms and how they power AI applications.
[Web Dev for Beginners] – Great for those looking to integrate AI with web development. This guide walks through how to use AI APIs, build intelligent web applications, and deploy machine learning models on the web.
These resources provide a comprehensive learning experience that will help you master AI fundamentals and practical applications. Whether you want to specialize in AI research, build AI-powered applications, or explore AI’s creative potential, these guides will accelerate your learning journey.
New Job Opportunities
I also want to share some new job postings from my teams at Microsoft and TikTok.
Microsoft AI Job Postings
Member of Technical Staff, Backend Growth Engineer: Apply Here
Member of Technical Staff, Infrastructure Engineer: Apply Here
Member of Technical Staff, Copilot Prompt Engineer: Apply Here
Member of Technical Staff, Platform Engineer: Apply Here
Member of Technical Staff, Machine Learning Engineer: Apply Here
TikTok Job Postings
Backend Software Engineer, Content E-commerce: Apply Here
Machine Learning Engineer, Search E-commerce: Apply Here
Machine Learning Engineer, Intern: Apply Here
Software Engineer Intern, Privacy and Security: Apply Here
If you're interested in any of these roles, feel free to reach out at hi.armankhondker@gmail.com! I can provide direct referrals for roles at Microsoft AI to those who demonstrate a great fit. For TikTok roles, I have ex-teammates who are willing to help with referrals as well!
Closing Thoughts
AI is one of the most exciting fields to be in right now, and having the right resources is crucial for success. I hope to provide more resources for software engineers, data scientists, machine learning engineers, students, or anyone interested in AI.
If you found this useful, let me know! I’d love to hear about your AI learning journey and any resources that have helped you along the way. And if you're job hunting, don’t hesitate to reach out!
Happy learning,
-Arman
How to join,is it free
awesome post!