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

The importance of Artificial Intelligence (AI) is becoming increasingly significant, with 97% of business owners expecting ChatGPT to bring positive changes in their business.

Tools like ChatGPT, Midjourney, and Bard are bringing AI into the mainstream, making the art and science of AI more relevant than ever before.

This guide is for aspiring data scientists, machine learning engineers, AI researchers, and enthusiasts, detailing how to learn AI from scratch and exploring how businesses can leverage AI in today's landscape.

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is a computer science field that focuses on creating systems capable of performing tasks that would typically require human intelligence, such as understanding natural language, recognizing patterns, making decisions, and learning from experience.

AI is a broad field with various subfields with unique objectives and specializations, and can be distinguished from machine learning in a separate article.

What are the different types of artificial intelligence?

As AI grows in popularity, the technology is discussed in various ways. To simplify the remainder of the article, it’s important to look at the different types of AI. AI can be categorized into three levels based on its capabilities:

• Artificial Narrow Intelligence (ANI): This is the most common form of AI we interact with today. ANI is designed to perform a single task, like voice recognition or recommendations on streaming services.
• Artificial General Intelligence (AGI): An AI with AGI possesses the ability to understand, learn, adapt, and implement knowledge across a wide range of tasks at a human level. While large language models and tools such as ChatGPT have shown the ability to generalize across many tasks—as of 2024, this is still a theoretical concept.
• Artificial Super Intelligence (ASI): The final level of AI, ASI, refers to a future scenario where AI surpasses human intelligence in nearly all economically valuable work. This concept, while intriguing, remains largely speculative.

Difference between data science, artificial intelligence, machine learning & deep learning

AI, a broad field with subsets such as Machine Learning (ML) and Deep Learning (DL), is gaining consensus on its broad scope.

While there is no official definition for these terms, experts argue over their exact boundaries.

The terms are often used interchangeably, but they are distinct from each other in the context of AI.

• AI refers to computer systems that can behave intelligently, reason, and learn like humans.
• Machine learning is a subset of AI, focusing on algorithms that learn without explicit programming.
• Deep learning, a subset of machine learning, is responsible for AI news stories like self-driving cars and ChatGPT.
• Data science is a cross-disciplinary field that uses data analysis, statistics, and visualization to gain insights from data.

Why Learn Artificial Intelligence Right Now?

Artificial Intelligence is a transformative technology that is transforming our lives and work. With the increasing demand for AI skills due to the explosion of data, it's the perfect time to start learning AI.

1. AI is a fast-growing field

The World Economic Forum's Future of Jobs report highlights AI and machine learning specialists as the top fast-growing jobs over the next five years.

As industries adopt AI technologies to streamline operations and make better decisions, the demand for AI specialists is expected to increase.

2. AI is a high-paying job

The increasing demand for AI skills has led to a rise in attractive compensation, with the average salary for an AI engineer in the US being $128,479 per annum, with potential bonuses and profit sharing.

Machine learning and data scientists also earn similar salaries, reflecting the value and impact of AI skills in the market.

3. AI is intellectually challenging

Artificial Intelligence is not just about high-paying jobs and market demand, but also offers intellectual stimulation through complex problem-solving algorithms, human intelligence simulation models, and creative application of technologies in real-world scenarios.

AI professionals continuously learn, adapt, and innovate, creating a dynamic field for those who thrive on challenges and continuous learning.

The field is constantly evolving, offering new challenges and opportunities for improvement.

How Long Does it Take to Learn AI?

Learning AI can be self-taught or through formal education. Self-taught learning can take months to a year, depending on prior knowledge and resources.

Self-paced online courses and tutorials can accelerate the learning process. On the other hand, university education typically takes three to four years, focusing on computer science or data science.

A bachelor's degree in these fields provides comprehensive training in AI and related subjects.

Regardless of the route, continuous learning, practical application, and staying updated with advancements are essential for pursuing a career in AI.

How to Learn AI From Scratch in 2024

Learning AI in 2024 requires a clear roadmap, the right resources, and a strategic approach. Despite its broad field and numerous subtopics, it can be effectively navigated with the right resources.

1. Master the prerequisite skills

• A basic understanding of mathematical concepts, particularly linear algebra, calculus, and probability, is crucial for success in AI.
• Linear transformations and matrices are common in AI algorithms, demonstrating the importance of understanding these concepts.
• Understanding statistics, such as statistical significance, distribution, regression, and likelihood, is also essential for AI applications.
• A proactive mindset and enthusiasm for learning are crucial for those looking to advance in AI, as the field is constantly evolving with new advancements and techniques. Therefore, a basic understanding of these concepts is essential for success in AI.

AI roles require varying levels of understanding and mastery in prerequisite areas, depending on the role.

Data scientists may not require an in-depth understanding of all mathematical concepts, while research scientists aiming to create new AI algorithms may need a more profound grasp of mathematics.

Aligning learning paths with career goals allows for adjustments in learning depth in different areas.

2. Develop specialist AI skills

AI is a rapidly evolving field that requires a deep understanding of various skills, which can be developed through various prerequisites, depending on the specific role one wishes to excel in.

2.1 Statistics

Statistics is a crucial discipline that involves data collection, organization, analysis, interpretation, and presentation, serving as the foundation for AI understanding and data management.

• The Statistics Fundamentals with Python Skill Track teaches four fundamental statistics using Python, including summary statistics, probability, linear and logistic regression models, sampling techniques, hypothesis tests, and data set conclusions.
• The Introduction to Statistics Course covers measures of center and spread, probability distributions, and hypothesis testing. The Introduction to Statistics in R Course covers variables, plotting, and standard deviation in R.

2.2 Mathematics

AI algorithms are based on mathematical tools such as linear algebra, calculus, probability, and differential equations, which are essential in the development of AI.

• Demystifying Mathematical Concepts for Deep Learning
• Linear Algebra for Data Science in R
• Fundamentals of linear algebra including matrix-vector equations, eigenvalue/eigenvector analyses, PCA
• Foundations of Probability in Python
• Covers random variables, mean, variance, etc.

2.3 Programming

Understanding programming is crucial for AI implementation, enabling the development of algorithms, data manipulation, and use of AI tools and libraries.

Python is the most popular language due to its simplicity, flexibility, and availability of data science libraries.

Python Programming Skill Track

• Enhances code optimization, function writing, and unit tests.
• Utilizes software engineering best practices.

R Programming Skill Track

• Levels up R programming skills.
• Learns to work with common data structures.
• Promotes code optimization and function writing.

2.4 Data structures

Understanding data structures like arrays, trees, lists, and queues is crucial for writing efficient code and developing complex AI algorithms.

The Introduction to Data Structures and Algorithms Course provides a comprehensive understanding of various data structures in Python, including linked lists, stacks, queues, hash tables, and graphs, and the Python Data Structures with Primitive & Non-Primitive Examples Tutorial covers data types.

2.5 Data manipulation

Data manipulation involves cleaning, transforming, and manipulating data for analysis or AI models, requiring skills in libraries like pandas.

• Python: Transform, sort, filter data in DataFrames for quick analysis.
• R: Covers same approach in R programming language.
• Pandas: Manipulate DataFrames for real-world dataset extraction, filtering, and transformation.

2.6 Data science

Data Science is a blend of various tools, algorithms, and machine learning principles aimed at discovering hidden patterns from raw data.

As an AI professional, understanding the process of extracting insights from data is crucial.

• The Data Scientist with Python Career Track focuses on Python skills, while the Data Scientist with R Career Track focuses on R Programming skills.
• The Data Science provides a comprehensive understanding of data science and its importance.

An Example AI Learning Plan

Below, we’ve created a potential learning plan outlining where to focus your time and efforts if you’re just starting out with AI. Remember, the timescales, subject areas, and progress all depend on a wide range of variables. We want to make this plan as hands-on and practical as possible, which is why we’ve recommended projects you can work on as you progress.

Month 1-3: Basics of mathematics, programming, data structures and manipulation

1. Mathematics and statistics: Start with the basics of linear algebra, calculus, statistics, and probability. This will give you a strong foundation for what’s to come.
2. Programming: Learn Python, the most widely used language in AI. Start with the basics and then move to more advanced concepts. Take our Python Fundamentals Skill Track and Data Manipulation with Python Skill Track to cover the essentials, including packages like NumPy.
3. Data manipulation: Start learning about data manipulation and analysis. Get familiar with Python libraries like pandas and NumPy, which you’ll use for data manipulation. Learn how to clean and prepare data, which is a crucial part of any AI or machine learning project.

Month 4-6: Dive deeper into AI and machine learning

1. Basics of AI: Understand what AI is, its history, and its different branches. Courses like our AI Fundamentals can provide a good start.
2. Deepen your machine learning knowledge: Learn about different types of machine learning algorithms - supervised, unsupervised, semi-supervised, and reinforcement learning. Take our Machine Learning Scientist with Python Track, which covers the most important model types, model validation, and hyperparameter tuning. It covers packages including TensorFlow and Keras and touches on advanced deep learning.

Month 7-9: Specialization and advanced topics

1. Deep learning: Understand neural networks and deep learning.
2. MLOps basics: Learn about MLOps, which is about applying DevOps principles to machine learning systems. This includes model versioning, model deployment, monitoring, and orchestration.
3. Specialization: Based on your interests and career aspirations, specialize in one area - it could be natural language processing, computer vision, reinforcement learning, or any other field.

Month 10-Ongoing: Keep learning and exploring

1. Specialize further: Depedening on career paths
2. Stay Up-to-date: Regularly follow AI-related blogs, podcasts, and journals. Join communities to exchange ideas with other AI practitioners.
3. Ethics in AI: As you learn more about AI, be sure to also learn about the ethical considerations in AI.

Top 5 Tips For Learning AI

Embarking on the journey to learn AI can be daunting, but having a strategic approach can make the process more structured and manageable. Here are five steps to guide you through your AI learning journey:

1. Choose your focus

Start by deciding where to focus your efforts based on your career goals. We discuss the various careers in AI further in this article; each role has a different focus and requires a unique skill set.

For example, if you're interested in a more applied role like a data scientist or machine learning engineer, focus more on programming, data science, and understanding various machine learning algorithms. Your aim here is to become proficient in using AI techniques to solve real-world problems.

Alternatively, if you're leaning towards a research role, you should delve deeper into the theory behind AI and machine learning. You'll need a solid grasp of mathematics, statistics, and theoretical computer science.

This is not a rigid distinction; rather, it’s a starting point to help you decide where to put your initial focus.

2. Start learning

Once you've decided on your focus, it's time to start learning. The learning resources suggested in the skills section and the AI learning plan above are useful places to start. Remember, mastering AI is a marathon, not a sprint. Take your time to understand each concept thoroughly before moving on to the next one.

3. Apply your skills to projects

There's no better way to learn than by doing. Applying the skills you learn in real-world projects solidifies your understanding and gives you practical experience that can enrich your portfolio. This could be as simple as creating a machine learning model to predict house prices or as complex as developing a deep learning model for image recognition. We’ve included example projects throughout this article.

4. Join a community

Join AI communities, both online and offline. Participating in forums like Stack Overflow or GitHub, joining AI groups on LinkedIn, or attending AI meetups and conferences can offer invaluable learning opportunities.

Once you’ve earned a DataCamp certification, you can join our Community, where you can connect with other certified learners, access exclusive content and events, and make the most of your newly acquired skills.

By joining communities, you can stay updated with the latest trends, get help when you're stuck, and network with other AI enthusiasts.

5. Keep iterating

AI is a rapidly evolving field. Once you get the basics, it's important to keep learning and improving your skills. Follow AI blogs, read research papers, take advanced courses, and always be on the lookout for new ways to challenge yourself. This iterative process will turn you from a novice into an expert.

Remember, the journey to learning AI is challenging but immensely rewarding. Don't get discouraged if you encounter obstacles along the way; they're part of the learning process. Keep your end goal in mind, and stay committed to your journey.

Conclusion

Learning AI is a rewarding pursuit that opens doors to a world of innovative technologies and exciting career opportunities. The knowledge and expertise gained through this process go beyond textbooks and lectures. It involves a dynamic cycle of learning, applying, experimenting, and improving. Embracing a hands-on approach, particularly through courses and AI projects, accelerates learning and cultivates essential skills in problem-solving, critical thinking, and creativity.

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