How To Learn AI From Scratch

We are living through what may be a defining moment in human history, as the significance of Artificial Intelligence (AI) grows ever more apparent. Forbes Advisor found that 97 percent of business owners anticipate positive changes in at least one area of their business as a result of ChatGPT. ChatGPT, Midjourney, and Bard are just a few of the tools that are helping to bring AI into the mainstream. As a result, the study and practice of artificial intelligence are more important than ever.

This book is for you if you want to be a data scientist, machine learning engineer, AI researcher, or just an AI enthusiast. In this article, we’ll explain how to get started with AI education from scratch and provide advice from professionals to help you along the way. How businesses can benefit from AI in the present day is also discussed, in addition to the necessary knowledge and tools.

What exactly is AI, or “artificial intelligence”?

Artificial intelligence (AI) is a subfield of computer science that aims to design intelligent machines to carry out human-level tasks. These include things like reading comprehension, pattern recognition, decision making, and experiential learning. Artificial intelligence encompasses many subfields, each with its own goals and areas of expertise. Explore our comprehensive What is AI? guide today. for further investigation. Separate from this one, you can read about the distinctions between AI and machine learning.

What are the various AIs available today?

The increasing interest in AI has led to a wide range of discussions about it. Looking at the various AIs available will help clarify the rest of the article. There are three tiers of AI, each representing a different set of capabilities.

The majority of the AI we interact with today is of the artificial narrow intelligence (ANI) variety. Single-purpose ANI systems, such as those used for voice recognition or content recommendation, are the norm.

The term “artificial general intelligence” (AGI) refers to a computer program that can think and reason like a human being would. However, as of 2023, this is still largely a theoretical concept, even though large language models and tools like ChatGPT have demonstrated the ability to generalize across many tasks.

Last but not least, we have artificial super intelligence (ASI), which describes a possible future in which machines outperform humans at virtually all tasks with economic value. While intriguing, this idea is still mostly theoretical at this point.

Understanding the Distinctions Between Data Science, AI, ML, and DL

Some other terms, such as “machine learning,” “deep learning,” “data science,” and others, may also appear in AI discussions, which can be confusing to those who are unfamiliar with the field. Artificial intelligence (AI) is a broad field that includes specialized fields like Machine Learning (ML) and Deep Learning (DL).

A growing consensus is emerging on the general scope of each term, even though there is no official definition and experts argue over the precise boundaries. Here’s a breakdown of what each of these words means:

The term “artificial intelligence” is used to describe computer programs with human-like cognitive abilities.

The field of artificial intelligence known as “machine learning” is concerned with the creation of self-teaching algorithms.

The field of machine learning to which deep learning belongs. It’s the brains behind many of the fascinating advances in artificial intelligence that have made headlines recently (e.g., self-driving cars and ChatGPT). The structure of the brain serves as inspiration for deep learning algorithms, which excel when presented with unstructured data like images, videos, or text.

Data science is an interdisciplinary field that employs these and other methods for gaining insight from data, such as data analysis, statistics, data visualization, and more.

Why Should You Start Learning AI Right Now?

AI is more than just a catchphrase; it’s a game-changing innovation that’s changing the way we live, work, and communicate. The need to make sense of this data has led to a dramatic increase in the demand for people with expertise in artificial intelligence. There has never been a better time to begin studying AI. Reasons why

AI is a rapidly expanding industry.

AI is already here, not something to look forward to. There has been a dramatic increase in the availability of AI-related jobs in recent years. Experts in artificial intelligence (AI) and machine learning (ML) are projected to have the fastest job growth over the next five years, per the World Economic Forum’s Future of Jobs report. The need for trained AI professionals is only expected to grow as more and more businesses realize the benefits of using AI to improve efficiency and accuracy of decision-making.

This forecast is supported by the predicted expansion of the AI market from 2021 to 2030, as shown in the graph below.

Jobs in artificial intelligence tend to pay well.

The increased value of AI expertise naturally leads to higher pay. Glassdoor estimates that in 2023, the average salary for an AI engineer in the United States will be $128,479 per year, not including benefits or bonuses. The average salary for a machine learning engineer is $133,111 per year, and the average salary for a data scientist is $126,888 per year. The monetary reward reflects the market value and significance of AI abilities.

Data Origination

There is more to AI than just a promising job market and a growing industry. It’s a fascinating area that will stretch your mind in new and interesting ways. It entails coming up with algorithms to tackle difficult problems, creating models that mimic human intelligence, and applying these technologies in novel ways to a wide range of real-world contexts.

Experts in AI are always expanding their knowledge and developing new methods. Due to the dynamic nature of the industry, there is always room for development in terms of knowledge, skillset, and infrastructure. Those who enjoy new challenges and expanding their knowledge will find AI to be a fascinating field.

The Time Required to Master AI?

The time it takes to become proficient in AI can vary widely, depending on whether you choose to teach yourself or enroll in a university course.

The time it takes to learn something on your own will depend heavily on how much you already know, how committed you are, and what resources you have at your disposal. Self-studying artificial intelligence (AI) can be a lengthy process, taking several months to a year or more depending on how quickly you learn concepts, languages, and algorithms. The learning process can be sped up with the help of self-paced online courses, tutorials, and practical projects.

But if you opt for higher education, you can earn a degree in computer science, data science, or a related field. In the three to four years it takes to earn a bachelor’s degree in these fields, students receive in-depth education in artificial intelligence and related topics.

No matter what path you take, a career in AI requires a commitment to lifelong learning, hands-on experience, and awareness of industry trends.

Learning AI can be a thrilling adventure, but it also comes with its fair share of difficulties. Numerous subfields can be found within it. If you have a solid plan, sufficient resources, and a strategic mindset, you can successfully negotiate this terrain. How to Study Artificial Intelligence in the Year 2023

1. Learn the basics first.

You should learn the fundamentals of certain disciplines before delving into AI. These fundamental abilities will serve as the cornerstone of your AI knowledge and proficiency:

Simply doing the math. Many branches of artificial intelligence, such as machine learning and deep learning, rely heavily on abstract mathematical ideas. A familiarity with linear algebra, calculus, and probability is helpful, but it is not required for success in AI. Examples of linear algebra concepts frequently used in AI algorithms include matrices and linear transformations.

Simple numbers. Understanding statistics is essential to grasping AI. The ability to make sense of data and draw conclusions is essential in this industry. Many uses of AI rely heavily on statistical ideas like probability, significance, and distribution.

A hunger for knowledge. New discoveries, methods, and resources emerge frequently in the field of AI. Therefore, those who wish to enter and progress in the field of artificial intelligence would do well to cultivate a proactive mindset, as well as a passion for learning and adapting to new information and technologies.

It’s worth noting that the level of expertise in these areas you’ll need can vary depending on the type of artificial intelligence job you’re interested in. In contrast to a research scientist whose goal is to develop new AI algorithms, a data scientist may not need an in-depth understanding of every mathematical concept used in AI.

The key is to tailor the breadth and depth of your education to your specific career objectives.

2. Become an AI expert.

Now that we’ve established the groundwork, let’s dive into the core competencies you’ll need to acquire to become an AI expert. The level of proficiency you’ll need in these areas is similarly context-dependent to the prerequisites listed above.

Statistics

The statistical process includes gathering data, cataloging it, analyzing it, drawing conclusions from it, and presenting the results. It’s the backbone of AI’s data understanding and manipulation capabilities.

Read the following to gain a deeper understanding of statistics:

Learn the four pillars of statistics with Python in the Statistics Fundamentals with Python Skill Track, covering topics such as probability and summary statistics, statistical models like linear and logistic regression, sampling methods, hypothesis testing, and data analysis.

The course covers the basics of statistics, such as central tendency and dispersion measures, probability distributions, and hypothesis testing.

The Standard Deviation, Plotting, and Working with Variables in R: An Introduction Course.

Mathematics

As was previously mentioned, AI algorithms are based on specific branches of mathematics. The mathematical tools at your disposal include linear algebra, calculus, probability, and differential equations.

Check out the links below for some additional math reading material:

Understanding the Math Behind Data Science and Deep Learning, which delves into the foundational mathematical ideas that underpin these fields.

Course covering linear algebra fundamentals for data scientists using R, including matrix-vector equations, eigenvalue and eigenvector analysis, and principal component analysis.

The concepts of random variables, the mean, the variance, and more are covered in depth in the Foundations of Probability in Python Course.

Programming

The ability to program effectively is a necessity for implementing AI. To create AI algorithms, manipulate data, and make use of AI tools and libraries, you need to know how to write code. Due to its ease of use, adaptability, and abundance of data science libraries, Python has quickly become the language of choice in the AI community.

You can hone your Python programming abilities by following the Python Programming Skill Track. Software engineering best practices, including code optimization, function and unit test writing, and more will be covered.

Similarly, in the R Programming Skill Track, you’ll learn advanced programming techniques like optimizing code, creating your own functions, and working with common data structures.

Structures of Data

The ability to efficiently store, retrieve, and manipulate data is made possible by data structures. Therefore, understanding data structures such as arrays, trees, lists, and queues is crucial for developing sophisticated AI algorithms and writing efficient code.

Linked lists, stacks, queues, hash tables, and graphs are all examples of data structures that can be learned about in an introductory course on data structures and algorithms.

Data types, lists, stacks, and more are just some of the primitive and non-primitive data structures covered in the Python Data Structures with Primitive and Non-Primitive Examples Tutorial.

Manipulation of data

Cleaning, transforming, and manipulating data is part of data manipulation, which is used to get data into shape for analysis or to feed into AI models. Knowledge of data manipulation libraries, such as pandas, is crucial for a career in AI.

Learn how to quickly and easily manipulate data in Python’s DataFrames by taking the Data Manipulation with Python Skill Track.

Learn the aforementioned methodology in the R programming language by following the Data Manipulation with R Skill Track.

This course will teach you how to extract, filter, and transform data sets using pandas by manipulating DataFrames.

Science of Data

Discovering previously unknown relationships within large datasets is the primary goal of Data Science, which employs a wide variety of programs, algorithms, and machine learning principles. In the field of artificial intelligence, knowing how to draw conclusions from data is essential.

Career Path for Data Scientists that Emphasizes Python Covers Core Python Concepts Needed to Succeed in the Field.

The Data Scientist with R Career Path teaches you everything you need to know to be a successful data scientist, including the R programming language.

The course, titled “Understanding Data Science,” explains the basics of the field and explains why it’s important.

Analytical machinery

In the field of artificial intelligence known as Machine Learning, computers use data to train themselves to perform better or make more precise predictions. Knowing the purpose of various machine learning algorithms, as well as how they operate and when to apply them, is crucial.

Prediction, pattern recognition, and the rudiments of deep learning are all covered in depth as part of the Machine Learning Fundamentals with Python Skill Track.

Discover the hidden structure of datasets with unsupervised learning and learn to predict categorical and numeric responses with the help of R’s Machine Learning Fundamentals Skill Track.

The Machine Learning Cheat Sheet compares and contrasts the most popular ML algorithms, outlining each one’s features and potential applications.

To a Great Extent

Deep Learning is a subfield of machine learning that employs multilayered neural networks (hence the name) to model and comprehend intricate data patterns. It’s the brains behind many cutting-edge AI tools like voice assistants and autonomous vehicles.

Learn how to build and optimize neural networks with Python’s Keras, TensorFlow, and PyTorch libraries by following the Deep Learning in Python Skill Track.

This tutorial will walk you through the fundamentals of deep learning, answering any questions you may have along the way.

This course will introduce you to the Keras deep learning framework and teach you how to create your own deep learning models.

Each of these abilities builds on the others, giving you a solid foundation in various areas of artificial intelligence. Learn the basics of everything, then dive deeper into the topics that really grab your attention. You’re free to combine methods in any way that makes sense to you, focusing on whatever emerges as you learn more and gain experience.

3. Acquire Expertise in Foundational AI Software

Successful AI work requires familiarity with relevant software packages and tools. Because of their ease of use, adaptability, and access to mature libraries and frameworks, Python and R have become dominant in the AI community. While mastery of both is not required for success in AI, the following are some essential libraries and frameworks you should be familiar with regardless of the method you employ.

The Best Artificial Intelligence Packages and Tools in Python

Python is an easy-to-read and highly flexible high-level, interpreted programming language. Its friendly syntax and abundance of AI and data science libraries and frameworks have led to its widespread adoption in AI.

pandas is a library for the Python programming language that provides numerous tools for analyzing data. Python is an essential tool for data scientists, who use it for everything from basic data manipulation to complex statistical analysis. It’s a powerful tool for preprocessing datasets because it can handle imperfect, unorganized, and unlabeled information.

Numerical Python, or NumPy, is a library for the Python programming language that provides support for large multi-dimensional arrays and matrices, as well as a wide variety of high-level mathematical functions for working with such data. It’s a must-have library for any artificial intelligence or scientific computation.

Data mining and machine learning have never been easier than with Scikit-Learn. It is open-source, meaning that anyone can download and use it for free, and it is based on the Python packages NumPy, SciPy, and matplotlib. Several algorithms for dimensionality reduction, clustering, and classification are included.

With just a few lines of code, users of the Python library PyCaret can explore, preprocess, train, tune, and compare a wide variety of machine learning algorithms, greatly streamlining the process of creating and deploying AI models.

How to Automate Machine Learning Workflows with PyCaret: A Tutorial

PyTorch

The Torch library serves as the foundation for PyTorch, an open-source machine learning library. Natural language processing and artificial neural networks are just two of the many areas where it finds use. Because of its adaptability and speed, it is well-suited for studies in deep learning.

Learning Deep Neural Networks with PyTorch

How to Create a Basic Neural Network Using PyTorch

Keras

Keras is a Python library that makes working with neural networks simple. It is designed to speed up the process of turning your ideas into working models and provides a simple approach to modeling neural networks. Because of its modular design, Keras can be used for a wide range of model creation scenarios.

One of the best ways to get started with AI is to use APIs to gain access to commercially available models. Start with commercially available application programming interfaces like the OpenAI API, Cohere API, or Anthropic API.

Python integration of OpenAI’s GPT-3.5 and GPT-4 APIs

Standard Python packages like Hugging Face’s transformers and accelerate make it simple to use GPUs and TPUs; as your expertise grows, you can experiment with pre-trained models using these libraries.

How to Use Transformers and the Hugging Face in Your Everyday Life

Methodology for Teaching AI to Learn

If you are just getting started with AI, we have outlined a potential learning plan to help you prioritize where to put your time and energy. Keep in mind that many factors will affect the timelines, topics, and development. We’ve included suggested projects you can work on as you advance because we want this plan to be as hands-on and practical as possible.

Months 1-3 cover the fundamentals of math, programming, and data manipulation.

Get your feet wet with the foundational concepts of linear algebra, calculus, statistics, and probability. A solid groundwork for the future awaits you here.

Programming 101: Master Python, the industry standard for artificial intelligence. Learn the fundamentals first, then advance to the more complex ideas. Learn the basics, including how to use packages like NumPy, by enrolling in our Python Fundamentals Skill Track and Data Manipulation with Python Skill Track.

Manipulation of data: initiate training in this area. Learn the ins and outs of the Python libraries you’ll be using for data manipulation, such as pandas and NumPy. Mastering data cleaning and preparation is an integral part of any artificial intelligence or machine learning endeavor.

Deepen your understanding of AI and ML in months.

Understanding AI’s definition, its evolution, and its various subfields is a necessary first step. Our AI Fundamentals course is a great place to begin.

Learn more about the various machine learning algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning, to round out your understanding of machine learning. Join us on our Machine Learning Scientist with Python Track and learn about the various models available, how to validate them, and how to optimize their hyperparameters. It goes over deep learning frameworks like TensorFlow and Keras, as well as more advanced topics.

Learn the fundamentals of MLOps, the practice of adapting DevOps methodologies for use with machine learning infrastructures. Model deployment, monitoring, and orchestration are all part of this.

Choose a subfield to focus on, such as natural language processing, computer vision, reinforcement learning, or another area that appeals to your interests and career goals.

Keep in mind that this is only a rough outline. You need to adjust it to fit your needs. Learning is a never-ending process, and AI is a vast field.

A.I. Mastery: Five Essential Steps

Embarking on the journey to learn AI can be daunting, but having a strategic approach can make the process more structured and manageable. If you want to learn AI, here are five steps to follow:

1. Pick your battles

Prioritize your efforts in accordance with your long-term professional objectives. 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 DC Certified 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.

The Best Ways to Learn AI

A rich abundance of resources is available to help you learn AI, from structured courses and textbooks to hands-on projects. Choosing the right resource can help you get off to a strong start with your AI learning. Here are our top picks to get you started on your AI learning journey:

Take the top AI courses to get started

DataCamp is a great platform that offers interactive courses specifically tailored for aspiring data scientists. The courses range from beginner to advanced levels and are designed with hands-on exercises. Here are some of the best AI-related courses on DataCamp:

Learning AI is a journey that goes beyond understanding theories and coding techniques. It involves hands-on practice, and this is where completing AI projects comes in.

Start by identifying a suitable project based on your proficiency level, be it a predictive model or a generative AI application. The right tools, such as Python and its extensive libraries, will be your cornerstone. Understanding and preparing your data is crucial, as it directly impacts your model’s effectiveness.

Below are some ideas for AI projects you can build to help you on your way to AI mastery. Be sure to check out our articles on AI projects for all levels, generative AI projects and machine learning projects for all levels for further info.

Classify Song Genres from Audio Data. Apply machine learning methods in Python to classify songs into genres.

Naïve Bees: Image Loading and Processing. Work with image data, build classifiers using traditional techniques and leverage the power of deep learning for computer vision.

ASL Recognition with Deep Learning. Build a convolutional neural network to classify images of letters from American Sign Language.

Books offer in-depth knowledge and insights from experts in the field. Here are some of the most influential books on AI that you might find helpful:

Data scientist

Data Scientists investigate, extract, and report meaningful insights into an organization’s data. They communicate these insights to non-technical stakeholders and have a good understanding of machine learning workflows and how to tie them back to business applications. They work almost exclusively with coding tools, conduct analysis, and often work with big data tools.

Data Scientists are the detectives of the data world, responsible for unearthing and interpreting rich data sources, managing large amounts of data, and merging data points to identify trends. They utilize their analytical, statistical, and programming skills to collect, analyze, and interpret large datasets. They then use this information to develop data-driven solutions to challenging business problems. A part of these solutions is developing machine learning algorithms that generate new insights (e.g., identifying customer segments), automate business processes (e.g., credit score prediction), or provide customers with new-found value (e.g., recommender systems).

Machine learning engineers are the architects of the AI world. They design and deploy machine learning systems that make predictions from organizations’ data. They also solve problems like predicting customer churn and lifetime value and

are responsible for deploying models for the organization to use. Machine learning engineers usually work exclusively with coding-based tools.

Develop a portfolio

A strong portfolio that showcases your skills and projects can make you stand out from other candidates. More importantly, a project attempting to solve a real-world problem will impress hiring managers.

As Nick Singh, author of Ace the Data Science Interview, said on the DataFramed Careers Series podcast,

Your portfolio should contain a variety of projects that highlight your proficiency in key AI tools and algorithms. Check out our article on how to build a great data science portfolio for more information and tips.

Develop an effective resume

In today’s digital age, your resume isn’t just read by human eyes; it must also pass through Applicant Tracking Systems (ATS). These are automated software used by many companies to filter out resumes that don’t meet certain criteria. Consequently, it’s crucial to tailor your resume to be ATS-friendly while still catching the eye of hiring managers.

According to Jen Bricker, former Head of Career Services at DataCamp:

As such, it’s extremely important to structure resumes as effectively as possible. You can read more about building a stand-out data scientist resume in a separate article.

Get noticed by hiring managers

Being proactive on social platforms can help attract the attention of hiring managers. As Sadie St. Lawrence, CEO of Women in Data, points out on the DataFramed Careers Series, sharing your projects and ideas on LinkedIn or Twitter, engaging with AI communities, and contributing to open-source projects can increase your visibility and showcase your passion for AI.

Remember, breaking into AI takes persistence, continuous learning, and patience. But with these steps, you’re setting yourself up for success. Good luck with your AI journey!

How to Master AI Tools for Business

While this article has primarily focused on an in-depth understanding of AI for aspiring AI practitioners, it’s equally crucial for business professionals to familiarize themselves with AI tools. Generative AI tools like ChatGPT are increasingly becoming integral to various business operations.

Moreover, as Noelle Silver, Global AI Solutions & Generative AI & LLM Industry Lead at Accenture, mentioned on the DataFramed podcast on how organizations can leverage AI, Large Language Models are becoming increasingly embedded in most of the software interfaces professionals use on a daily basis.

Learn more about generative AI

Firstly, get acquainted with the different AI tools available to you. There’s a broad spectrum of tools designed for various business functions, such as marketing, sales, customer service, and data analysis. Explore the AI tool landscape and understand the capabilities of each tool. How can they benefit your role or business?

For instance, consider our Introduction to ChatGPT course, which offers a comprehensive understanding of this powerful generative AI tool. We also have an introduction to Generative AI Concepts course which covers a rang of other tools as well.

Apply generative AI tools at work

Once you understand the AI tools, the next step is to apply them in your daily workflows. Whether you want to automate data analysis, improve customer interactions, or streamline business processes, AI tools can benefit you, your teams, and the wider organization.

We’ve compiled several resources that provide practical guidance on how to use AI tools effectively in your work:

While AI tools are incredibly useful, they’re not infallible. It’s important to review and edit the outputs generated by these tools. Understand their limitations and adjust their use accordingly. Remember, these tools are designed to assist you, not replace your decision-making. Understanding the ethics of AI is essential.

Mastering AI tools in business aren’t just about understanding the technology; it’s about knowing how to leverage them effectively to drive success in your role and business. By learning, applying, and refining their use, you can stay ahead of the curve in the AI-driven business world.

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.

If you’re just starting out on your AI learning journey, we’ve outlined loads of helpful resources to help you get started, as well as an example learning plan for some of the key themes you’ll need to master on your way to become proficient in artificial intelligence.