How to Learn AI From Scratch in 2024: A Complete Guide From the Experts
10 June 2024

How to Learn AI From Scratch in 2024: A Complete Guide From the Experts

Artificial Intelligence (AI) is transforming industries and redefining the way we live and work. As AI continues to evolve, the demand for skilled professionals in this field is soaring. If you’re looking to dive into AI from scratch in 2024, this guide will provide you with a structured path to acquire the necessary skills and knowledge.

Understand the Basics of AI

1. Understand the Basics of AI

What is AI?

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think and learn. AI encompasses various subfields such as machine learning, deep learning, natural language processing, and computer vision.

Key Concepts

Before diving deep into AI, familiarize yourself with key concepts:

– Machine Learning (ML): Algorithms that enable computers to learn from and make predictions based on data.

– Deep Learning (DL): A subset of ML involving neural networks with many layers.

– Natural Language Processing (NLP): Techniques to understand and generate human language.

– Computer Vision: Techniques to enable machines to interpret and make decisions based on visual data.

2. Learn the Required Mathematics

Mathematics is the backbone of AI. Key areas to focus on include:

– Linear Algebra: Understand vectors, matrices, and operations on them.

– Calculus: Learn about derivatives, gradients, and optimization techniques.

– Probability and Statistics: Grasp concepts like probability distributions, Bayesian inference, and statistical tests.

Online platforms like Khan Academy and Coursera offer excellent courses to build your mathematical foundation.

3. Acquire Programming Skills


Python is the most popular programming language for AI due to its simplicity and extensive libraries. Start with Python basics:

– Variables, Data Types, and Structures

– Control Structures (if-else, loops)

– Functions and Modules

Libraries and Frameworks

Learn to use essential AI libraries and frameworks:

– NumPy: For numerical computations.

– Pandas: For data manipulation and analysis.

– Scikit-Learn: For machine learning algorithms.

– TensorFlow/PyTorch: For deep learning.

4. Study Machine Learning

Core Concepts

Understand the fundamentals of machine learning:

– Supervised Learning: Training models on labeled data (e.g., linear regression, classification).

– Unsupervised Learning: Finding patterns in unlabeled data (e.g., clustering, dimensionality reduction).

– Reinforcement Learning: Training models through trial and error (e.g., game playing, robotics).

Practical Application

Work on practical projects to solidify your knowledge. Kaggle, a platform for data science competitions, offers datasets and challenges to apply machine learning techniques.

Dive Into Deep Learning

5. Dive Into Deep Learning

Understand the architecture and functioning of neural networks:

– Neurons and Activation Functions

– Forward and Backward Propagation

– Loss Functions and Optimization

Advanced Topics

Explore advanced topics like Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.

6. Explore Specialized AI Fields

Natural Language Processing

Learn techniques for text processing, sentiment analysis, and machine translation using libraries like NLTK and SpaCy.

Computer Vision

Study image recognition, object detection, and image generation using libraries like OpenCV and frameworks like YOLO.

7. Engage With the AI Community

Online Courses and Certifications

Platforms like Coursera, edX, and Udacity offer specialized AI courses and certifications from top universities and institutions.

Books and Research Papers

Read foundational books like “Deep Learning” by Ian Goodfellow and keep up with the latest research papers from conferences like NeurIPS and CVPR.

Forums and Communities

Join AI communities on Reddit, Stack Overflow, and specialized forums. Participate in discussions, ask questions, and share your projects.

8. Build Projects and a Portfolio

Personal Projects

Start with small projects and gradually take on more complex ones. Document your work on GitHub to showcase your skills.


Participate in AI competitions on platforms like Kaggle to test your knowledge and learn from others.

9. Stay Updated and Keep Learning

AI is a rapidly evolving field. Follow AI blogs, subscribe to newsletters, and attend webinars and conferences to stay updated with the latest trends and advancements.

Learning AI from scratch in 2024 is an achievable goal with the right approach. By understanding the basics, building a strong mathematical foundation, acquiring programming skills, and continuously engaging with the AI community, you can master this exciting and transformative field. Start your journey today, and open the door to endless possibilities in the world of AI.

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