Top 20+ Best Free Online Courses for Machine Learning & Artificial Intelligence Today
What is Machine Learning? Why is this concept so often mentioned in all areas of life? Is it possible to self-study Machine Learning? Where should you study? What is the Machine Learning roadmap like?
Machine Learning or machine learning, this is a concept in the field of artificial intelligence and computer science. Machine learning is concerned with the study and development of techniques to help systems learn from data to solve specific problems.
Machine Learning focuses on using algorithms and data to replicate how humans learn, gradually improving accuracy.
Speech recognition also known as ASR (Automatic Speech Recognition) uses natural language to process human speech into written form. Currently, many mobile devices integrate this feature to perform voice search tasks.
Image Recognition: This is one of the most popular applications of Machine Learning. This feature is also used to detect faces in photos of multiple people.
Customer service: Salespeople are gradually not needing to directly chat with customers but can use online chatbots. Chatbots help businesses answer frequently asked questions about pricing, shipping, personalized advice, size recommendations, and more. You can see this in message bots on social media, e-commerce sites, and more.
Recommendation engine: AI algorithms can use a user’s past consumption behavior to provide recommendations to customers during the purchase or checkout process for an online retailer.
Computer vision: AI allows computers and systems to use meaningful information from images, videos, etc. Thanks to the support of complex neurons, computer vision has applications in social media tagging, radiography, and the self-driving car industry.
Automated stock trading: In order to optimize the portfolio, artificial intelligence-driven high-frequency trading platforms can execute hundreds, millions of trades a day without any intervention. of human.
The fundamentals of the Supervised Machine Learning course are mainly about: Regression and classification with popular Python libraries.
You will be introduced to machine learning applications, examples, and how to build linear and logistic regression models on Jupyter Notebook. Furthermore, you will learn about feature engineering, gradient descent, cost function, decision boundaries and regularization.
Perks: Taught by industry experts. The course comes with interactive exercises and hands-on learning projects.
Topics covered: building regression and classification models using popular machine learning libraries NumPy & scikit-learning.
Georgia Tech’s Machine Learning Course is a focused course on: supervised, unsupervised, and reinforcement learning. You will learn from video lessons, quizzes and interactive exercises.
Prerequisite: Proficiency in Probability Theory, Linear Algebra and Statistics. Have some experience with the Python programming language.
Timeline: 4 months
Skill level: Intermediate
Perks: Taught by industry experts.
Topics covered: supervised, unsupervised and reinforcement learning with code.
Another free Google online course that you should consider taking is the Machine Learning Crash Course. This course teaches the basics of machine learning through a series of lessons that include video lectures from researchers at Google, lessons specifically designed for those new to Machine Learning.
This free Google course is targeted at people who have an idea for programming, but know little or nothing about Machine Learning Crash.
You can get a free Google Artificial Intelligence course online with a certificate and no complicated math or programming is required when you choose this course.
Elements of AI provides an introduction to the basics allowing you to realize how AI affects your life, while understanding the underlying technologies including networks and machines, and the key impacts of AI.
To be certified, you need to complete at least 90% of the exercises given and must do 50% of the exercises correctly, including multiple choice questions, number exercises and questions that require you to answer written word.
Udemy is one of the best online learning platforms for nearly every subject, with thousands of courses available at very low prices. Every now and then, you’ll come across some totally free gems in their list of offerings, and this course by AI Sciences is one of them. It’s one of the best free online machine learning courses available, with two hours of on-demand video, lifetime access, and the ability to take the course on any platform (including TV and mobile). It is also the only platform on our list that provides a free completion certificate, making it our top pick.
The course itself focuses on theory rather than practical application and is ideal for beginners who want to learn about machine learning. It includes 18 short lectures that cover topics such as machine learning models, performance, and best practices. There are no prerequisites for this course other than basic mathematics, so students of all levels are welcome to participate.
This specialization, also taught by Andrew Ng, is a more advanced course series for anyone interested in learning about neural networks and Deep Learning and how they solve a variety of problems.
Each course’s assignments and lectures use the Python programming language and the TensorFlow neural network library. This is a natural follow-up to Ng’s Machine Learning course because you’ll get a similar lecture style but will now be exposed to using Python for machine learning.
Tableau is one of the most popular tools in the data scientist world. It is a tool that allows you to explore, test, repair, prepare and present data easily, quickly and beautifully. This course will walk you through using Tableau 10 step-by-step. This course will teach you step-by-step how to use Tableau 10 for Data Science. Like the 3 courses above, this is also a very quality course that many people choose.
If you want to start a career in data science or are curious about the capabilities of machine learning algorithms, this is the course for you. This course is ideal for beginners because it provides a simple overview of common topics such as how to use different languages for data science and what it means to build a model from scratch.
Although you do not need any prior knowledge of machine learning to take this course, a basic understanding of the Python programming language is recommended. You can also use this course as a foundation for further intermediate machine learning courses.
For those interested in intelligent tools, the Introduction to Artificial Intelligence course provides a more comprehensive overview of AI. You’ll begin with a thorough understanding of the world of AI and what it means to create your own intelligent models. The course includes a plethora of videos and resources created by AI mentors.
Everything from fundamental AI workflows and concepts to more complex deep learning ideas is covered. Among the topics covered in the program are:
This course will provide you with a fundamental understanding of machine learning and its various algorithms. You will learn about Machine Learning algorithms such as Support Vector Machines, Logistic Regression, Unsupervised Learning, Linear Regression with One and Multiple Variables, and so on during this course.
You will also learn how to use data analysis and topic modeling to uncover hidden meaning in large amounts of data. This machine learning course focuses on statistical machine learning theory rather than practical machine learning applications. You will receive a shareable certificate upon completion of this course demonstrating your proficiency in Machine Learning for Data Science and Analytics.
This course defines Machine Learning in layman’s terms. It goes over supervised learning in depth, with cool examples, and then moves on to reinforcement learning to show how AI systems have beaten professional players in certain games. It is extremely theoretical.
It concludes by discussing Deep Learning and some of the misconceptions and issues surrounding Machine Learning. Each of the 13 chapters includes a short video as well as a quick quiz to test your knowledge.
This is a very introductory and theoretical course that is great for getting a basic understanding of what Machine Learning is and what it can be used for without getting too deep into math or specific models.
Practical Deep Learning for Coders is a course designed for learners with programming knowledge who want to learn and apply deep learning to solve real-world problems.
The benefit of the course is that it is taught by a teacher Jeremy Howard, who has dedicated his life to making machine learning accessible to everyone for free.
Perks: Taught by Jeremy Howard. The course comes with quizzes, coding examples, community-based learning, and projects.
Topics covered : model deployment, neural networks, NLP, creating a model from scratch, random forest, CNNs and data ethics.
Non-technical aspects of machine learning will be covered in this course. It is intended for beginners to help them understand the terminology and the process of how things work using machine learning.
This course has no prerequisites and does not require any programming knowledge.
Consider this a true “101 level” course that will provide an introduction to this new topic and set you on the path to learning much more about it.
Machine Learning for Everyone is available at Datacamp, the leading platform for online tech courses. Lis Sulmont, Hadrien Lacroix, and Sara Billen created it, which includes 12 videos and 36 exercises. This course is taken by over 118k students worldwide and has a rating of 4.6 out of 5 based on over 26k reviews.
This AI course provides a comprehensive, step-by-step description of using TFX to perform sentiment analysis, a classic and simple Machine Learning problem. It was designed by Tomasz Makowiak, Data Scientist at DLabs.AI.
Once you finish it, you’ll know how to:
This sentiment analysis course provides an easy-to-understand explanation of all the key concepts and tools used to create a sentiment analysis model, pipeline, and helper library codebase that can be used as a template in your project.
This course will teach you about Unsupervised Learning. You will be able to glean insights from data sets that do not contain a target or labeled variable. You will learn several unsupervised learning clustering and dimension reduction algorithms and choose the best one. The course will teach you the best unsupervised learning practices
Introduction to Artificial Intelligence with Python in CS50 | edX teaches you the skills you’ll need to become a machine learning engineer. You will learn machine learning algorithms that have given rise to technologies such as game-playing engines, image classification, machine translation, and stock price predictions in this course. Although the course is free, you can pay to obtain a certificate, complete access to course work, and interactive projects.
Hands-on experience with machine learning frameworks, graph search algorithms, adversarial search, knowledge representation, logical inference, probability theory, Bayesian networks, Markov models, constraint satisfaction, machine learning, reinforcement learning, neural networks, and natural language processing are all included in the course. Working on portfolio projects will also teach you how to design intelligent systems.
This is an introductory course for learning the fundamentals of artificial intelligence. This course will teach you AI Fundamentals with Azure as well as core AI and machine learning concepts.
Then you’ll learn how to use Azure Machine Learning to train and evaluate models, as well as how to use Azure Cognitive Services to handle key workloads in computer vision such as object detection, image classification, face detection, text analysis, and form processing.
You will also learn natural language processing, how to analyze text and speech for intent, and how to translate text and speech between languages in this course.
MLOps stands for Machine Learning Engineering for Production and is intended for experienced data scientists and machine learning engineers. The course taught me production techniques such as developing model pipelines, managing metadata, project scoping and design, concept drift, and human-level performance, as well as the data-centric approach to optimizing model performance. You can audit the courses for free, which allows you to view video tutorials, take quizzes, and read course content.
The courses will prepare you to excel in your career and improve the performance of AI products by incorporating advanced tools. You will learn about data drift, concept drifts, and data-centric approaches, as well as how to build end-to-end machine learning systems, data pipelines, machine learning operations, and advanced techniques for continuously monitoring production systems.
This course will teach you how to build an end-to-end Machine Learning pipeline that will take you from idea to value. It accomplishes this through the use of the R programming language and libraries such as Caret. You’ll go over everything from data collection and preparation to modeling with algorithms like regression, Naive Bayes probabilistic models, and ensembles.
We recommend purchasing a subscription because this course is highly practical and contains a lot of exercises: it does not only cover the theory, but it places a strong emphasis on transforming the theory that you have learned into a practical application that can be used somewhere.
This Caltech MOOC is one of the best free online machine learning courses we’ve found. It is taught by Caltech Professor Yaser Abu-Mostafa and consists of 18 lectures and 10 weeks of assignments, as well as a graded final exam. It provides a solid foundation in machine learning, as well as comprehensive theory and plenty of practical exercises to help you understand what you’re learning. Students can discuss various aspects of the class in an online forum, and the course textbook can be purchased for a small fee on Amazon.
This free online machine learning course is self-paced and requires no registration. You are free to begin whenever you are ready.
You are welcome to audit the course for free. Tensorflow will be used to teach machine learning concepts in this course. In this course, you will use Datalab and BigQuery to explore a large dataset, as well as learn how to use Pandas in Datalab and sample a dataset for local development.
Then, using Tensorflow, you will create a machine learning model and operationalize it. Finally, this course explains how to preprocess data for machine learning at scale and allows you to train a machine learning model at scale on the Cloud AI Platform.
Above are the courses that can help you gain more knowledge to become a complete AI Engineer within 3 to 4 months, isn’t that great? Experience the course and find the best one suitable for yourself. Let me know if you want any suggestions or guidance regarding
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