Getting Started in AI
If you are interested in the AI field but haven't jumped in yet because you're worried about how to get started, how much it may cost to learn the fundamentals, or not even sure if you have enough time to learn something new...guess what? Stop worrying!
Machine learning, deep learning and all the other subsets of artificial intelligence (AI) have a wealth of resources. And many don't even cost a dime to learn! There are plenty of online courses that you can pay for to get certificates of completion or full-blown degrees, but if you just want to learn you can do that for free in many cases. And, at your own pace (although courses will help nudge you along with frequent reminders).
We've put together a list of some of the best online classes and sites that will do more than just have you dipping your toes in. These are free, although if you want an actual certificate you will need to pay. Some will show that you have to pay, but you can take the course in audit mode for free which gives you access to all course materials except graded items like quizzes and reviews (and certificates of completion).
We've broken up these courses into the following sections:
- Free Online Machine Learning and AI Courses
- Free Online Deep Learning Courses
- Free Online Data Science Courses
- Free Online TinyML Courses
- Free Online Life Sciences Courses
- And, a little section on what to do after you grasp the fundamentals!
So browse through the list below, and good luck on your potential new career path!
Free Online Machine Learning and AI Courses
Machine Learning from Stanford University, taught by Andrew Ng
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. The full class is offered free with no certificate, but only costs $79 if you prefer a certificate of completion.
https://www.coursera.org/learn/machine-learning
Intro to Machine Learning
Learn the core ideas in machine learning, and build your first models. Plus a bonus lesson on Intro to AutoML.
https://www.kaggle.com/learn/intro-to-machine-learning
Introduction to Machine Learning from Duke University, taught by Lawrence Cain
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
https://www.coursera.org/learn/machine-learning-duke
Introduction to Data Science in Python from University of Michigan, taught by Christopher Brooks
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
https://www.coursera.org/learn/python-data-analysis
Machine Learning for All from University of London
A beginner course designed to introduce you to Machine Learning without needing any programming. That means that it doesn't cover the programming based machine learning tools like python and TensorFlow.
https://www.coursera.org/learn/uol-machine-learning-for-all
Introduction to Machine Learning with scikit-learn
Learn the fundamentals of Machine Learning in Python with this free 4-hour course. This is the perfect course for you if:
- You're new to Machine Learning
- You have Machine Learning experience, but you're new to scikit-learn
- You've used scikit-learn, but you don't know if you're doing things the "right" way
https://courses.dataschool.io/introduction-to-machine-learning-with-scikit-learn
AI For Everyone from DeepLearning.AI
AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn:
- The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science
- What AI realistically can--and cannot--do
- How to spot opportunities to apply AI to problems in your own organization
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI
Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.
https://www.coursera.org/learn/ai-for-everyone
Code Free Data Science from UC San Diego
The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements. Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data. Predicting future trends and behaviors allows for proactive, data-driven decisions. During the class learners will acquire new skills to apply predictive algorithms to real data, evaluate, validate and interpret the results without any pre requisites for any kind of programming. Participants will gain the essential skills to design, build, verify and test predictive models.
https://www.coursera.org/learn/code-free-data-science
A Crash Course in Data Science from Johns Hopkins University
This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.
This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.
After completing this course you will know.
- How to describe the role data science plays in various contexts
- How statistics, machine learning, and software engineering play a role in data science
- How to describe the structure of a data science project
- Know the key terms and tools used by data scientists
- How to identify a successful and an unsuccessful data science project
- The role of a data science manager
https://www.coursera.org/learn/data-science-course
Computer Vision Basics from University of Buffalo
By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks.
This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables).
https://www.coursera.org/learn/computer-vision-basics
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning from DeepLearning.AI
This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
https://www.coursera.org/learn/introduction-tensorflow
Fundamentals of Machine Learning for Healthcare from Stanford University
This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare.
https://www.coursera.org/learn/fundamental-machine-learning-healthcare
Introduction to Artificial Intelligence with Python
Learn to use machine learning in Python in this introductory course on artificial intelligence.
https://online-learning.harvard.edu/course/cs50s-introduction-artificial-intelligence-python?delta=0
Free Online Deep Learning Courses
Up next is a 5-course deep learning specialization track from DeepLearning.AI. For this you should ideally take them in order but it’s up to you based on your skill set and what you already know.
DEEP LEARNING SPECIALIZATION (5 COURSES)
1) Neural Networks and Deep Learning from DeepLearning.AI
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.
https://www.coursera.org/learn/neural-networks-deep-learning
2) Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization from DeepLearning.AI
In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.
By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow.
https://www.coursera.org/learn/deep-neural-network
3) Structuring Machine Learning Projects from DeepLearning.AI
In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge.
https://www.coursera.org/learn/machine-learning-projects
4) Convolutional Neural Networks from DeepLearning.AI
In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
https://www.coursera.org/learn/convolutional-neural-networks
5) Sequence Models from DeepLearning.AI
In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.
By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering.
https://www.coursera.org/learn/nlp-sequence-models
Free Online Data Science Courses
The following courses focus more on data science, and will give you a solid foundation for becoming a data scientist.
Data Science: Productivity Tools
Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.
https://online-learning.harvard.edu/course/data-science-productivity-tools
Using Python for Research
Take your introductory knowledge of Python programming to the next level and learn how to use Python 3 for your research.
https://online-learning.harvard.edu/course/using-python-research
Data Science: R Basics
Build a foundation in R and learn how to wrangle, analyze, and visualize data.
https://online-learning.harvard.edu/course/data-science-r-basics
Data Science: Visualization
Learn basic data visualization principles and how to apply them using ggplot2.
https://online-learning.harvard.edu/course/data-science-visualization
Data Science: Wrangling
Learn to process and convert raw data into formats needed for analysis.
https://online-learning.harvard.edu/course/data-science-wrangling
Data Science: Probability
Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.
https://online-learning.harvard.edu/course/data-science-probability
Data Science: Machine Learning
Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.
https://online-learning.harvard.edu/course/data-science-machine-learning
Data Science: Linear Regression
Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.
https://online-learning.harvard.edu/course/data-science-linear-regression
Data Science: Inference and Modeling
Learn inference and modeling: two of the most widely used statistical tools in data analysis.
https://online-learning.harvard.edu/course/data-science-inference-and-modeling
Principles, Statistical and Computational Tools for Reproducible Data Science
Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.
Free Online TinyML Courses
Once you have a grasp of machine learning principles, you can explore TinyML, a field of study in Machine Learning and Embedded Systems that explores the types of models you can run on small, low-powered devices. The following is a list of free courses to get you started.
Fundamentals of TinyML
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
https://online-learning.harvard.edu/course/fundamentals-tinyml
Applications of TinyML
Get the opportunity to see TinyML in practice. You will see examples of TinyML applications, and learn first-hand how to train these models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.
https://online-learning.harvard.edu/course/applications-tinyml
Deploying TinyML
Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.
https://online-learning.harvard.edu/course/deploying-tinyml
Free Online Life Sciences Courses
As a bonus, we are also including some free courses for life sciences. The life sciences include all the branches of science that focus on living organisms.
Many researchers combine the fundamentals of AI with specific applications that help with research for healthcare and drug discovery, among other things. The following are a few courses that can help get you started in this field.
High-Dimensional Data Analysis
A focus on several techniques that are widely used in the analysis of high-dimensional data.
Statistical Inference and Modeling for High-throughput Experiments
A focus on the techniques commonly used to perform statistical inference on high throughput data.
Introduction to Linear Models and Matrix Algebra
Learn to use R programming to apply linear models to analyze data in life sciences.
Statistics and R
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
https://online-learning.harvard.edu/course/statistics-and-r
Now What?
Once you get a solid understanding of the fundamentals of AI, machine learning, data science and anything else you're interested in, there are a ton of additional resources offered by open source libraries and platforms that will give you a great push into learning how to use them.
A few of the most popular these days include:
- TensorFlow - Offers a place to learn (https://www.tensorflow.org/learn); complete, end-to-end examples to learn how to use TensorFlow in their Tutorials section (https://www.tensorflow.org/tutorials); and a number of Resources on their website.
- PyTorch - Offers a number of tutorials on how to get started with PyTorch and use it for different projects (https://pytorch.org/tutorials/), as well as a number of other community Resources on their website.
- Hugging Face - They offer a free course here (https://huggingface.co/course/chapter1), as well as a number of excellent resources on their website.
These seems to be no end to other free resources for learning about machine learning, data science, deep learning, computer vision, NLP, and more! And there are plenty of GitHub repos that offer open source code you can download and work with as you become more skilled.
If you have any other suggestions please feel free to add in the comments section. You can also feel free to contact us at any time.