How to Learn AI From Scratch in 2023: A Complete Guide From the Experts


We’re living through what is quite possibly a pivotal point in human history, where the importance of Artificial Intelligence (AI) is becoming increasingly undeniable. Just consider this statistic: 97% of business owners expect that ChatGPT will bring about positive changes in at least one area of their business, according to a survey by Forbes Advisor. Moreover, tools like ChatGPT, Midjourney, and Bard are ushering AI into the mainstream. This makes the art and science of AI more relevant than ever before.

If you’re an aspiring data scientist, machine learning engineer, AI researcher, or simply an AI enthusiast, this guide is for you. Throughout this article, we’ll detail how to learn AI from scratch and offer insights from industry experts to help steer your journey. As well as covering the skills and tools you’ll need to master, we also explore how businesses can leverage AI in today’s landscape.

What is Artificial Intelligence (AI)?

AI, or Artificial Intelligence, is a branch of computer science focused on creating systems that can perform tasks that would normally require human intelligence. These tasks range from understanding natural language, recognizing patterns, making decisions, and learning from experience. AI is a broad field with numerous subfields, each with its unique objectives and specializations.

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 2023, 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.

The difference between data science, artificial intelligence, machine learning & deep learning

If you are new to this topic, you may also see the terms “machine learning,” “deep learning,” “data science,” and others creep into AI discourse. AI is a broad field with several subsets, including Machine Learning (ML) and Deep Learning (DL).

While there isn’t an official definition for any of these terms, and while experts argue over the exact boundaries, there is a growing consensus on the broad scope of each term. Here’s a breakdown of how these terms can be defined:

  • Artificial intelligence refers to computer systems that can behave intelligently, reason, and learn like humans.
  • Machine learning is a subset of artificial intelligence focused on developing algorithms with the ability to learn without explicitly being programmed.
  • Deep learning is a subset of machine learning. It is responsible for many of the awe-inspiring news stories about AI in the news (e.g., self-driving cars, ChatGPT). Deep learning algorithms are inspired by the brain’s structure and work exceptionally well with unstructured data such as images, videos, or text.

Data science is a cross-disciplinary field that uses all of the above, amongst other skills like data analysis, statistics, data visualization, and more, to get insight from data.

Why Learn Artificial Intelligence Right Now?

Artificial Intelligence is more than just a buzzword; it’s a revolutionary technology transforming how we work, live, and interact. With the explosion of data and the need to make sense of it, the demand for AI skills is skyrocketing. There’s no better time than now to start learning AI. Here’s why:

AI is a fast-growing field

Artificial Intelligence isn’t the future; it’s the present. The number of AI jobs has seen significant growth over recent years. According to the World Economic Forum’s Future of Jobs report, ​​AI and machine learning specialists top the list of fast-growing jobs over the next five years. As industries continue to adopt AI technologies to streamline their operations and make better decisions, the demand for AI specialists will likely only increase.

This prediction is further emphasized in the projected AI market size growth between 2021 and 2030, as seen in the graph below.

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AI is a high-paying job

Naturally, the surge in demand for AI skills comes with attractive compensation. According to data from Glassdoor, as of 2023, the average salary of an AI engineer in the United States is $128,479 per annum, with the potential for bonuses and profit sharing. Machine learning engineers and data scientists are similarly well-paid, with average salaries of $133,111 and $126,888 per annum, respectively. This financial compensation reflects the value and impact of AI skills in the marketplace.

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AI is intellectually challenging

Artificial Intelligence is not just about high-paying jobs and significant market demand. It’s also an intellectually stimulating field that promises to challenge you in exciting ways. It involves building algorithms to solve complex problems, designing models that simulate human intelligence, and creatively applying these technologies to various real-world scenarios.

AI professionals continuously learn, adapt, and innovate. The field is constantly evolving, meaning there’s always something new to learn, a problem to solve, or a system to improve. This dynamic nature makes AI an exciting field for those who thrive on challenges and continuous learning.

How Long Does it Take to Learn AI?

The time it takes to learn AI will often depend on the route you take; whether it’s self-taught or through formal education such as a university program.

In a self-taught route, the duration can vary significantly as it largely depends on your prior knowledge, dedication, and available learning resources. It can take several months to a year or more to gain a solid understanding of AI concepts, programming languages such as Python, mathematics, and various machine learning algorithms through self-study. Self-paced online courses, tutorials, and practical projects can accelerate the learning process.

On the other hand, a university route typically involves pursuing a formal education in computer science, data science, or related fields. A bachelor’s degree in these disciplines typically takes around three to four years to complete, during which students receive comprehensive training in AI and related subjects.

Whichever route you choose, continuous learning, practical application, and staying up-to-date with advancements are essential for pursuing a career in AI.

How to Learn AI From Scratch in 2023

Learning AI can be an exciting endeavor, but it’s not without its challenges. It’s a broad field with many subtopics. However, with a clear roadmap, the right resources, and a strategic approach, you can navigate this landscape effectively. Here’s how to learn AI in 2023:

1. Master the prerequisite skills

Before diving into AI, you’ll want to have a grounding in certain areas. These foundational skills will form the backbone of your AI understanding and capabilities:

  • Basic mathematics. AI relies heavily on mathematical concepts, particularly its subfields like machine learning and deep learning. Of course, you don’t have to be a mathematician to succeed in AI, but a basic understanding of linear algebra, calculus, and probability is essential. For instance, concepts such as matrices and linear transformations from linear algebra are frequently used in AI algorithms.
  • Basic statistics. AI makes a lot of sense when you understand statistics. Knowing how to interpret data and derive insights is crucial in this field. Concepts like statistical significance, distribution, regression, and likelihood play a significant role in different AI applications.
  • A willingness to learn. AI is a rapidly evolving field with new advancements, techniques, and tools constantly emerging. Therefore, a proactive mindset and an enthusiasm for learning and adapting to new knowledge and technologies are pivotal for anyone looking to break into and advance in AI.

It’s important to note that the depth of understanding and mastery required in these prerequisite areas can vary depending on the AI role you aim to pursue. For instance, a data scientist might not need an in-depth understanding of every mathematical concept used in AI, but a research scientist aiming to create new AI algorithms might need a more profound grasp of mathematics.

The key is to align your learning path with your career goals and adjust the depth of your learning in different areas accordingly.

2. Develop specialist AI skills

Now that we’ve covered the prerequisites, let’s delve into the essential skills you need to develop to master AI. Similar to the prerequisite section above—the level of mastery you’ll need for these skills largely depends on what type of role you wish to pursue.


Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. It provides the backbone for understanding and working with data in AI.

Learn more about statistics in the resources below:

  • Statistics Fundamentals with Python Skill Track, where you’ll learn the four fundamentals of statistics using Python, including summary statistics and probability, statistical models such as linear and logistic regression, techniques for sampling, how to perform hypothesis tests, and draw conclusions from a wide variety of data sets.
  • Introduction to Statistics Course, which teaches the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing.
  • Introduction to Statistics in R Course, for learning how to work with variables, plotting, and standard deviation in R.


As discussed earlier, certain areas of mathematics form the foundation of AI algorithms. Linear algebra, calculus, probability, and differential equations are all mathematical tools that will be used in your AI journey.

Learn more about mathematics in the resources below:


AI implementation requires a sound understanding of programming. Knowing how to write code allows you to develop AI algorithms, manipulate data, and use AI tools and libraries. Python is currently the most popular language in the AI community due to its simplicity, flexibility, and availability of data science libraries.

  • Python Programming Skill Track will help you improve your Python programming skills. You’ll learn how to optimize code, write functions and unit tests, and use software engineering best practices.
  • R Programming Skill Track, similarly, here you’ll level up your R programming skills by learning how to work with common data structures, optimize code, and write your own functions.

Data structures

Data structures allow you to store, retrieve, and efficiently manipulate data. Therefore, knowledge of data structures like arrays, trees, lists, and queues is essential for writing efficient code and developing complex AI algorithms.

Data manipulation

Data manipulation involves cleaning, transforming, and manipulating data to prepare it for further analysis or feeding it into AI models. Skills in using libraries like pandas for data manipulation are essential for working in AI.

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.

Machine learning

Machine Learning is a subfield of AI where machines learn from data to improve their performance or make accurate predictions. It’s essential to understand different machine learning algorithms, how they work, and when to use them.

Deep learning

Deep Learning, a subset of machine learning, uses neural networks with multiple layers (hence ‘deep’) to model and understand complex patterns in datasets. It’s behind many of the most advanced AI applications today, from voice assistants to self-driving cars.

Each of these skills interconnects with the others, helping you build a broad knowledge of AI concepts. A good way to start is to gain the foundations in each area before exploring the ones that interest you most in more detail. You can blend your approach as you see fit, working on the areas that naturally come up as you study and gain hands-on practice.

3. Learn the essential AI tools and packages

Knowing the right tools and packages is crucial to your success in AI. In particular, Python and R have emerged as the leading languages in the AI community due to their simplicity, flexibility, and the availability of robust libraries and frameworks. While you don’t need to learn both to succeed in AI, below you will find some key libraries and frameworks you need to get familiar with depending on the tool you end up choosing:

Top Python AI tools and packages

Python is a high-level, interpreted programming language known for its readability and versatility. It is widely used in AI thanks to its user-friendly syntax and the plethora of libraries and frameworks available for AI and data science.


pandas is a Python library that provides extensive means for data analysis. Data scientists use pandas for various tasks, including data cleaning, data transformation, and statistical analysis. It works well with incomplete, messy, and unlabeled data, making it a significant tool for preprocessing datasets.


NumPy, short for Numerical Python, is a library for Python that adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. It’s an essential library for any scientific computation, including AI.


Scikit-Learn is a simple and efficient tool for data mining and machine learning. It is built on NumPy, SciPy, and matplotlib, and it’s open-source, meaning it’s freely available to everyone. It features various classification, regression, clustering, and dimensionality reduction algorithms.


PyCaret is a powerful Python library that simplifies the process of building and deploying artificial intelligence models, enabling users to efficiently explore, preprocess, train, tune, and compare multiple machine learning algorithms with just a few lines of code.


PyTorch is an open-source machine learning library based on the Torch library. It’s used for applications such as natural language processing and artificial neural networks. Its biggest advantage is its flexibility and speed, making it suitable for deep learning research.


Keras is a user-friendly neural network library written in Python. It’s built to minimize the time between your ideas and working models, offering a straightforward way for neural network modeling. Keras is also modular, making it incredibly versatile when constructing new models.

Commercially available APIs

Once you’re ready to start getting hands-on with AI, utilizing APIs to access commercially available models is one of the best ways to get started. Commercially available APIs such as the OpenAI API, Cohere API, and Anthropic API are good places to start.

Hugging Face

As your proficiency develops, explore pre-trained models using standard Python packages such as Hugging Face’s transformers and accelerate, which make it easy to utilize GPUs and TPUs.

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.

Recommend Resources & Projects

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.

Recommend Resources & Projects

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.

Recommend Resources & Projects

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.

Remember, this is just a basic roadmap. You should modify it based on your pace and interests. AI is a vast field, and learning is a continuous process.

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 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:

Complete AI projects

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. 

Read some of the best AI books

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:

  • 15 Best Machine Learning Books to Read in 2023
  • Top 11 Deep Learning Books to Read in 2023

Remember, the key to learning AI is consistency and practice. Don’t be afraid to start small and gradually work your way up to more complex concepts and projects. You’ll be amazed at how much you can learn by dedicating just a little time each day to studying AI.

The Different Careers in AI Today

Artificial intelligence has opened up numerous career paths, each with unique responsibilities, tools, and required skills. Let’s delve into three AI careers that are popular at the moment: data scientists, machine learning engineers, and research scientists. Note that we will be mentioning some of the tools they need to master; if you are not familiar with these tools, that’s completely okay—we’ll be covering them in more detail in a later section.

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).

Key skills:

  • Strong knowledge of Python, R, and SQL
  • Understanding of Machine Learning and AI concepts
  • Proficiency in statistical analysis, quantitative analytics, and predictive modeling
  • Data visualization and reporting techniques
  • Effective communication and presentation skills

Essential tools:

  • Data analysis tools (e.g., Pandas, NumPy)
  • Machine learning libraries (e.g., Scikit-learn)
  • Data visualization tools (e.g., Matplotlib, Tableau)
  • Big data frameworks (e.g., Airflow, Spark)
  • Command line tools (e.g., Git, Bash)

Machine learning engineer

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.

Key Skills:

  • Deep knowledge of Python, Java, and Scala
  • Familiarity with machine learning frameworks (like Scikit-learn, Keras, or PyTorch)
  • Understanding of data structures, data modeling, and software architecture
  • Advanced mathematical skills (linear algebra, calculus, statistics)
  • Ability to work in a team and exceptional problem-solving skills


  • Machine learning libraries and algorithms (e.g., Scikit-learn, TensorFlow)
  • Data science libraries (e.g., Pandas, NumPy)
  • Cloud platforms (e.g., AWS, Google Cloud Platform)
  • Version control systems (e.g., Git)

Research scientists

Research scientists are the thinkers of the AI world. They conduct cutting-edge research to advance the state-of-the-art in AI. Their work often involves inventing new algorithms or improving existing ones. They also present their findings at AI conferences and in scholarly articles.

Key Skills:

  • Solid understanding of machine learning and deep learning
  • Proficiency in Python and other programming languages
  • Extensive knowledge of AI-related mathematical theory (like statistical learning theory)
  • Ability to conceptualize and validate novel AI models
  • Strong writing and public speaking skills


  • Deep learning frameworks (like TensorFlow, PyTorch)
  • Scientific computation tools (like MatLab, Mathematica)
  • Software for writing and presenting (like LaTeX, Google Slides)
  • Cloud computation resources (like AWS, Google Cloud Platform)

Data Scientist

Machine Learning Engineer

Research Scientists

What is it?

Extracts and reports meaningful insights from data to solve business problems.

Designs and deploys machine learning systems to make predictions from data.

Conducts research to advance the state-of-the-art in AI. Publishes findings.

Key Skills

Extracts and reports meaningful insights from data to solve business problems.

Python, Java, Scala, ML Frameworks, Data Structures, Software Architecture, Mathematics, Teamwork, Problem-solving

ML, Deep Learning, Programming, AI-related Math, Conceptualization, Writing, Public Speaking


Pandas, NumPy, Scikit-learn, Matplotlib, Tableau, Airflow, Spark, Git, Bash

Scikit-learn, TensorFlow, Pandas, NumPy, AWS, Google Cloud Platform, Git

TensorFlow, PyTorch, MatLab, Mathematica, LaTeX, Google Slides, AWS, Google Cloud Platform

Each of these careers offers a unique path into the world of AI. They all hold tremendous potential and serve critical roles in the field. Your decision will depend on your interests, strengths, and long-term career goals.

How to Find a Job in AI

A degree can be a great asset when starting a career in AI, but it’s not the only pathway. While we do think it’s worthwhile to get a formal education in AI or an adjacent field, more and more professionals are entering the field through non-traditional routes, proving that with dedication, consistent learning, and a proactive approach, you can land your dream job in AI.

Here’s how to find a job in AI without a degree:

Keep learning about the field

Stay updated with the latest developments in AI. Follow influential AI professionals on Twitter, read AI research papers, and listen to AI-related podcasts, including DataCamp’s DataFramed Podcast.

Some of the AI thought leaders to follow include Yoshua Bengio, Fei-Fei Li, and others. You’ll gain insights into trending topics, emerging technologies, and the future direction of AI.

You should also check out industry events, whether it’s webinars at DataCampdata science and AI conferences, or networking events.

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,

The key to standing out is to show your project made an impact and show that other people cared. Why are we in data? We’re trying to find insights that actually impact a business, or we’re trying to find insights that will actually shape society or create something novel. We’re trying to improve profitability or improve people’s lives using and analyzing data, so if you don’t somehow quantify the impact, then you are lacking impact.

Nick Singh, Co-author of Ace the Data Science Interview

Your portfolio should contain a variety of projects that highlight your proficiency in key AI tools and algorithms. 

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:

60% to 70% of applications get shifted out of consideration before humans actually look at the application.

Jen Bricker, Former Head of Career Services at DataCamp

As such, it’s extremely important to structure resumes as effectively as possible. 

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.

If you want to be seen, then you have to share your work. The analogy I like to use is a music box. If you’ve ever seen a music box, when it’s closed and just sitting on the table, you never actually get to hear what the beautiful sound is inside of it. It’s similar for data scientists that lack communication skills. They may have these amazing skills, but they’re all locked in this box, and no one ever knows about them. You have to open the box, and you do that by being able to tell those stories and communicate those skills. So, it’s really up to you. Do you want people to hear your story and experience your amazing skills and ability? Then you’re going to need the communication skills so that you can open your box.

Sadie St. Lawrence, CEO of Women in Data

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.

Large Language Models like ChatGPT are revolutionizing the way we interact with software. Whether it’s customer service, project management, or data analysis, these AI tools are enhancing efficiency, accuracy, and productivity across all sectors.

Noelle Silver Russel, Global AI Solutions & Generative AI & LLM Industry Lead at Accenture


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