If you’re in search of the top-notch no-cost online courses for Machine Learning and Artificial Intelligence, this article is what you need. It presents the ten best courses you can take for free from different platforms.
This article offers not just free courses but also streamlines the process of searching for various free courses related to machine learning and artificial intelligence.
Let’s begin right away without any delay.
Best FREE Artificial Intelligence (AI) Online Courses
1. Intro to Artificial Intelligence– Udacity
It will take four months to complete.
Intermediate level
You can acquire knowledge on artificial intelligence for free through taking this course. It covers fundamental concepts such as Statistics, Uncertainty, Bayes networks, Machine learning, Logic, and planning.
Additionally, you will acquire knowledge on how to utilize artificial intelligence in the areas of image processing, computer vision, robotics and robot motion planning, natural language processing, and information retrieval.
Who Should Enroll?
- Those who have an understanding of probability theory.
2. AI Fundamentals– Udacity
Duration of Completion – 30 Days
Novice level
If you’re new to the world of artificial intelligence, this course provides an easy introduction to the basics. Through this course, you’ll acquire an understanding of the core concepts of AI and machine learning, as well as how to work with Azure to study AI fundamentals.
In this course, you will acquire knowledge on training and assessing models through Azure Machine Learning, along with the utilization of Azure Cognitive Services for managing critical computer vision tasks such as image classification, object detection, face detection, form processing, and text analysis.
As a part of this course, you will gain knowledge in natural language processing. You will be able to scrutinize text and speech to comprehend their purpose and interpret speech and text from one language to another.
By the conclusion of this course, you will be able to create a Conversational AI.
Who Should Enroll?
- Those who are familiar with basic Python, basic linear algebra, probability, and statistics knowledge.
3. Intro to Game AI and Reinforcement Learning– Kaggle
The duration of completion is four hours.
Novice level
On Kaggle, there is a course consisting of four lessons that can be accessed for free. The initial lesson will instruct you on creating personalized intelligent agents for gameplay, followed by instruction on conventional techniques for constructing game AI.
The final lesson will teach you how to create an intelligent agent without relying on a heuristic by utilizing reinforcement learning. You may assess your agents’ capabilities by engaging with agents developed by other users.
Who Should Enroll?
- Those who are familiar with Python Programming.
4. Artificial Intelligence for Robotics– Udacity
It will take 2 months to complete.
Advanced level
An intricate course is available to acquire skills in artificial intelligence necessary for robotics. The course entails lessons on programming the key systems of a self-driving car from the expert who headed Google and Stanford’s self-driving divisions.
In this course, the main objective is to teach you about robotics, specifically focusing on probabilistic inference, planning, and search, localization, tracking, and control.
Programming involves the use of Python, and to model the motion and perception of robots, some fundamental object-oriented concepts are employed.
Who Should Enroll?
- Those who have programming knowledge and are familiar with concepts in probability.
5. Artificial Intelligence by Georgia Tech– Udacity
The duration for completion is 4 months.
Intermediate level
This is an additional course that is available for free which teaches the basics of Artificial Intelligence and extensive analysis of fundamental principles such as traditional search methods, probability, machine learning, logic, and planning.
Throughout the duration of this course, you will acquire knowledge on the utilization of artificial intelligence algorithms to address practical issues like gaming, location determination, recognition of sign language, and more.
Who Should Enroll?
- Those who are comfortable in Python and algorithms and data structures.
These are the top 5 free online courses on Artificial Intelligence (AI). Additionally, let’s explore the best free courses on Machine Learning.
Best Free Machine Learning Courses/ Machine Learning Courses FREE
1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning– Coursera
The rating is 4.7 out of 5.
It takes 31 hours to finish.
Auditing this course is free but obtaining a certificate requires a payment. The course covers topics such as TensorFlow best practices, building a basic neural network using TensorFlow, training neural networks for computer vision applications, and utilizing convolutions to enhance your neural network skills.
By the conclusion of this course, you will have the ability to enhance the outcomes of your deep neural network through the integration of a convolution neural network.
To access the course material without charge, click on “Enroll for Free” followed by “Audit the Course.”
2. End-to-End Machine Learning with TensorFlow on GCP– Coursera
The rating is four and a half out of five.
The duration required for completion is 13 hours.
It is possible to enroll in the course for no cost. The course curriculum focuses on the principles of machine learning through the use of Tensorflow. Throughout the program, participants will be introduced to Datalab and BigQuery to analyze large datasets, and will gain familiarity with Pandas in Datalab, as well as practice working with a sample dataset for local development.
This course guides you through the process of building a Tensorflow machine learning model and implementing it. Additionally, it illustrates how to preprocess large volumes of data for machine learning and how to train a Cloud AI Platform machine learning model on a large scale.
3. Intro to Machine Learning
It will take 1 week in order to complete.
Udacity offers a free machine learning course tailored for beginners. Participants can expect to gain a comprehensive grasp of basic machine learning concepts. Prior experience is not a prerequisite for taking this course; it is open to anyone who is new to the subject.
4. Machine Learning by Georgia Tech
Duration of Completion – 4 Months
On Udacity, there is an additional machine learning course available at no cost. The course is classified as intermediate and will educate you on Supervised Learning, Unsupervised Learning, and Reinforcement Learning. You will have the opportunity to work on real-world projects created by industry professionals during the course.
Prior knowledge in Probability Theory, Linear Algebra, and Statistics is necessary to enlist in this course, as it is not designed for novices. Moreover, expertise in a programming language is a mandatory prerequisite.
5. Intro to TensorFlow for Deep Learning
It will take 2 months to complete.
On Udacity, there is an additional machine learning course for individuals at an intermediate level which is free of charge. The course syllabus will provide instruction on developing TensorFlow deep learning applications. Aspects of this course will include hands-on project work and creating advanced image classifiers, alongside other deep learning models.
Advanced techniques and algorithms of deep learning will be taught, however, prior knowledge of linear algebra and Python programming is required.
BONUS: A learning style classification approach based on deep belief network for large-scale online education
The integration of big data and education has led to a learning revolution that emphasizes the importance of online education in reshaping traditional forms of education. MOOCs, Khan Academy, and Flipped Classroom have had a strong impact on education by making globalized resources, modularized supply, personalized teaching, and independent study more accessible through restructuring and process reengineering. Online education has made significant progress in teaching environments, content presentation, teaching modes, and learning evaluations. MOOCs offer a new personalized learning model that allows learners to select and customize courses based on their purposes and backgrounds. However, the complexity of materials in MOOCs can lead to learning challenges such as “learning Trek” and “cognitive overload.” To enhance the quality of learning experience, it is necessary to analyze individual learning behaviors and recommend personalized resources based on online learning data through adaptive learning.
Individual variances can be easily distinguished among learners in an online educational setting with regards to their preferred learning hours, duration of learning sessions, preferred learning materials, and online interactions. A few learners may choose to accomplish their learning tasks in the morning, whereas others may prefer night-time. A section of learners may participate in group activities or discussions through social-networking forums, whereas some may opt for solitary study. Amongst these individual characteristics, a crucial aspect that triggers differences between learners is their learning style. Therefore, it can be stated that learners possess unique tendencies regarding their learning style.
Researchers have made significant contributions in applying learning styles to online learning, with particular emphasis on learning style identification and prediction [12, 13, 14, 15]. These studies focused on collecting and analyzing the real behavior data of online learners to create a set of network learning behaviors. This data was then processed using algorithms, neural networks or rules to detect learning and yield research findings. However, the main challenge is that these studies were not based on large-scale online learning platforms and are, therefore, insufficient to meet modern online learning platforms’ demands for the effective detection of learning styles based on a large number of complex network learner behaviors.
It is crucial to address certain issues at present, such as determining the learning styles of students, assisting them in creating personalized learning goals and strategies, and recommending suitable learning materials based on their unique requirements and capabilities. Proper identification and classification of learning styles, which can be done by examining their online behavior, is essential to facilitate adaptive learning. Therefore, in a MOOC setting, it is vital to solve the following three key problems in order to achieve accurate identification and classification of learning styles:
- What type of learning style model is suitable in MOOC learning environments?
- What is the relationship between learning behavior and learning styles in MOOC learning environments?
- What classification method can be used to overcome the problem of inaccurate classification due to the high dimensionality of learning behavior data in MOOC environments?
After conducting a thorough analysis of the literature on learning styles, it was determined that the Felder-Silverman model is compatible with the MOOC online learning platform. Furthermore, the correlation between learning styles and behavior traits in online learning is intricate. Our research, along with professional feedback, revealed that variances in student behavior can be categorized into four dimensions: information processing, perception, input, and comprehension. Therefore, the data mining dimension should be based on these four behavioral dimensions to create characteristic indicators.
We suggest utilizing a DBNLS, a learning style classification based on a DBN for MOOCs. Our method adopts the inherent traits and characteristics of learners, which are considered a classification criteria. We initially analyzed and summarized the individual differences and preferences of learners in order to develop a learning style model appropriate for MOOC learning environments. We then identified learning-habit indicators based on expert experience and linked them to learning styles in individual sessions. Our deep learning model, DBN, was employed to learn the high-dimensional learning style features and accurately classify students’ learning styles. In our study, we analyzed weblogs in StarC, a MOOC platform used at Central China Normal University, to collect network learning behavior data. Additionally, we conducted offline empirical research and obtained learning style questionnaire data, which we utilized as training samples to train the DBN model. We applied the trained DBNLS model to categorize students’ learning styles and observed that our proposed method was more effective than traditional methods.
The primary focal points of this paper include:
- Learners’ intrinsic characteristics and learning styles are introduced as an important standard for learner classification. Simultaneously, the explicit attributes of the network behavior are effectively mapped to the intrinsic characteristic learning style indicators. These network behavior indicators serve as important DBN inputs for classifying students’ learning styles.
- We introduce social interaction factors into the learning style model, with the view of including the network learning environment characteristics and the learner interactions in the learning platform. The learning style model established in this paper focuses not only on the static learning resources available to students but also on how they interact with others. For example, some students tend to complete tasks through communication and discussion, while others tend to think and work independently.
- We introduce a deep-learning algorithm into learning classification in the field of education. This approach effectively overcomes the problems of the sharp rise in computational complexity resulting from high-dimensional data in traditional classification methods and data overfitting conditions.
- The research was conducted using a practical online learning activity. We collected and distinguished both online and offline data. The offline data served as training data used to train the model, which subsequently was used to classify students’ online learning behaviors. The results show that the proposed mechanism is considerably more accurate than is the traditional classification model.
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