How to Learn AI and ML for Beginners

Learning AI and ML can seem daunting at first, but it's actually a very exciting field that's constantly evolving. You can start by understanding the basics, such as supervised and unsupervised learning.

Supervised learning is a type of machine learning where the algorithm is trained on labeled data, whereas unsupervised learning involves training the algorithm on unlabeled data. Understanding these concepts will give you a solid foundation to build upon.

To get started, you'll need to learn some programming skills, such as Python, which is a popular language used in AI and ML. Python is easy to learn and has many libraries that make it suitable for beginners, including TensorFlow and Keras.

Learning from online resources, such as Coursera and edX, can also be a great way to get started. These platforms offer courses and tutorials that cover the basics of AI and ML, and some even offer certifications upon completion.

Recommended read: Unsupervised Machine Learning Methods

You can start your new career in AI and ML faster with a curated career path that gives you all the knowledge and experience you need.

Assess if you're ready to apply for jobs, then build your confidence with code challenges and practice questions.

Showcasing your skills with a professional certification can connect you with employers directly.

Before diving into a class, develop a learning plan that includes a tentative timeline, skill-building goals, and the activities, programs, and resources you'll need to gain those skills.

To create a learning plan, ask yourself questions like your level of knowledge of artificial intelligence, how much time you can spend learning, how much money you can spend, and how you want to learn.

Here are some questions to consider:

You can start learning AI today with a free 7-day trial of DeepLearning.AI's AI For Everyone, taught by top instructor Andrew Ng, which provides an excellent introduction to the fundamentals of AI.

Building a strong foundation in AI and ML starts with learning the basics. You can learn foundational AI knowledge in just a few hours with popular courses on Coursera, such as DeepLearning.AI's AI for Everyone.

To understand and apply complex AI concepts, you need a solid theoretical foundation in mathematics, statistics, and data. A robust foundation accelerates your ability to tackle advanced AI topics and increases comprehension efficiency.

You'll want to have a foundation in the following areas before starting your learning journey: basic statistics, basic math, and curiosity and adaptability. Basic statistics includes concepts such as statistical significance, regression, distribution, and likelihood, all of which play a role in AI applications.

Here are the prerequisite skills you'll need to learn:

In as little as seven hours, you can explore and practice different prompt engineering techniques with IBM's Generative AI: Prompt Engineering Basics.

Programming is an essential skill for anyone looking to learn AI and ML. It's the key to turning theoretical concepts into functional algorithms.

Python and R are especially popular programming languages due to their robust libraries explicitly designed for AI, ML, and DL. You should start by learning R and Python programming fundamentals.

Knowing how to code is essential to implementing AI applications because you can develop AI algorithms and models, manipulate data, and use AI programs. Python is one of the more popular languages due to its simplicity and adaptability.

Some popular tools and libraries specifically for Python include NumPy, Scikit-learn, Pandas, TensorFlow, and Matplotlib. These libraries can help you streamline your AI development process.

Here's a list of popular Python libraries for AI and ML:

You should also be familiar with which programming languages these tools align with, since many tools are dependent on the language used.

As you progress in your AI and ML journey, you'll need to think about your career and specialization. To become a Machine Learning/AI Engineer, you'll want to have a job-readiness checker to evaluate your skills and experience against job postings. This can be done using AI-powered tools.

Having a strong portfolio is also essential, so be sure to apply what you're learning to create recruiter-ready projects. You can use AI to identify strengths and see how to improve your interviewing skills with an interview simulator.

Here are some job titles relevant to the field of artificial intelligence:

Remember, the level of education required for different AI jobs can vary, and some employers may require a bachelor's or master's degree in computer science or a related field.

Once you've acquired fundamental AI knowledge, you can select your field of specialization. This can be natural language processing (NLP), large language models (LLMs), computer vision, robotics, or building chat applications.

There are plenty of jobs in AI, each requiring a slightly different set of skills and qualifications. You can start learning AI skills once you've covered the prerequisites.

To get started, consider taking a course like AI For Everyone, taught by top instructor Andrew Ng, which provides an excellent introduction to AI in just 10 hours or less. This can give you a solid foundation in AI and how it exists in society.

Some AI jobs may require a bachelor's degree or a master's degree in computer science or a related field. However, others may be open to individuals who have completed a boot camp program.

Here are some AI job titles and their related duties:

Collaboratively learning with others can be a game-changer for your career in AI. Expanding your learning network through community involvement or team projects can expose you to diverse problem-solving approaches and feedback.

This exposure is invaluable for broadening your understanding and fostering innovative thinking in AI applications. By working with others, you can gain new insights and perspectives that might not have occurred to you otherwise.

In fact, learning collaboratively can even help you develop a more well-rounded approach to problem-solving, which is essential in the ever-evolving field of AI.

Project-Based Learning is a game-changer for learning AI and ML. It's a hands-on approach that helps you reinforce theoretical knowledge and enhance problem-solving skills.

You can bring your skills into the real world with projects that showcase your expertise. For example, you can create a portfolio project that implements an end-to-end workflow and turns it into a machine learning pipeline.

Practice projects are also a great way to learn. You can use pipelines to create reusable, succinct code to predict patient survival for the bone marrow transplant dataset, or perform data transformations on a clothing company customer review dataset.

Real-world projects like these not only help you learn, but also give you a sense of accomplishment and boost your confidence. As one Codecademy learner put it, "I felt like I learned months in a week. I love how Codecademy uses learning by practice and gives great challenges to help the learner to understand a new concept and subject."

Here are some examples of project ideas to get you started:

Remember, the key to successful project-based learning is to choose projects that interest you and challenge you to learn new skills. With persistence and dedication, you can achieve great things and become proficient in AI and ML.