Free Machine Learning Courses & Certifications

The Machine Learning for Trading program will introduce you to the real-world challenges people face while implementing machine learning-based trading strategies. These strategies include algorithm steps for information gathering to preparing orders.

The Machine Learning for Trading course will familiarise you with applying probabilistic approaches to Machine Learning to trading decisions. The course will cover topics including trade signal generation and asset management, among others. You will learn how to apply statistical approaches like KNN, Regression Trees, and Linear Regression to actual stock trading situations.

The Machine Learning for Trading course is a four-month course by Udacity along with Georgia Tech. It is aimed at helping you complete real-world projects designed by industry experts. You are sure to master AI Algorithms for building a career-ready portfolio and excelling in trade. Most importantly, the course is free.

Arpan Chakraborty and Tucker Balch will be the instructors for the Machine Learning for Trading online programme.

Machine Learning is a highly in-demand curriculum for this generation. Machine Learning gives computers the ability to learn from experience without being programmed explicitly. EdX’s Machine Learning certification course is concerned with both the theoretical and practical aspects. The curriculum will present the candidates with statistical unsupervised and supervised learning methods for data remodeling and data analysis.

Georgia Institute of Technology offers the Machine Learning online course by EdX, and the university’s Executive Associate Dean Charles Isbell teaches the programme. It is an intermediate-level course that covers various theoretical concepts like the Inductive bias, the minimum description length principle, Ockham’s razor, etc. The training includes several projects and programming assignments for candidates to get a more concrete understanding of the topics. 

Students learn about various topics such as randomised search algorithms, reinforcement learning, Bayesian learning methods, etc.  After completing the Machine Learning training, the candidates will get three credits and a certificate of completion after paying the fee for certification. It will take around 14 weeks to complete the programme, and classes will be conducted online through video lectures. 

Machine learning certification course by edX primary stage starts from the core development of computer programs that can acquire data and use it to learn for themselves. The course served by edx will guide the candidate through various topics that include, matrix factorization, classification and regression, topic modelling,  sequential models, clustering methods, and model selection. In the first half of the course, we will cover guided learning techniques for regression and classification. 

We will explore several fundamental methods which will be essential for performing this task and optimizing it with algorithms. In the second half of the Machine learning training course, we will shift to unguided learning techniques, where the end output will be more unpredictable based on a corresponding input. Any student or professional with knowledge of undergraduate probability, statistics, linear algebra, calculus, probability, and statistical concepts can enroll in this course. The course has a tenure of 12 weeks, where only 8-10 hours per week can push the candidate towards excellence. Under the guidance of ColumbiaX (Columbia University) after successful completion, every student will get a verified certificate with the institution's logo.

Also Read: Machine Learning Practical Applications

Candidates looking for an undergraduate or postgraduate course in machine learning should enroll in the Introduction to Machine Learning program. It is an elective twelve-week course in the field of computer science and engineering, programming, artificial intelligence, and data science. A collaboration of IIT Madras and NPTEL, this course is an initiative by the Ministry of Education, Government of India, and is available on the learning platform Swayam.

Introduction to Machine Learning certification will be taught by professor Balaraman Ravindran. The syllabus includes various topics from the data-driven disciplines such as machine learning and analytics. Candidates will be introduced to different learning paradigms and some popular algorithms and architectures used in these paradigms.  

Introduction to Machine Learning is an online free audit course that offers an e-certificate for interested participants. Candidates will attend the online lectures, and submit assignments periodically. There is also an optional offline examination for participants who want to achieve certification.

Machine Learning is a highly demanded skill in the 21st Century. ML is the application of Artificial Intelligence  that provides systems with the ability to learn and improve from experience, without any explicit programming and human intervention. It is the study of computer algorithms that automatically improve through experience. ML has now become an integral part of our lives, and data science and AI professionals are looking to upskill in this. In Coursera’s Machine Learning training course, you will learn the most effective ML techniques and gain experience in their practical applications with precision. 

The Coursera Machine Learning programme teaches the extensive theories behind Machine Learning so that you can quickly learn to apply the techniques to new problems. Besides Machine learning, this course also provides an introduction to data-mining, artificial neural networks, logistic regression, and statistical pattern recognition. 

This Machine Learning certification course is offered by Stanford University and is taught by one of the top educators of Stanford. It takes approximately 60 hours to complete this course. It has 18 modules rife with pre-recorded online lectures, readings and practice exercises for self-evaluation. 

In the Machine Learning Fundamentals certification course by edX using real-world problem statements students will learn to figure out the features of topics in the corpus, classify the given images, group people according to various profiles depending on their personality and also capture the semantic structure of words available. With the information learnt in the Machine Learning Fundamentals training, the students will learn to analyze different types of data and also they will be able to build predictive and descriptive models. The Machine Learning Fundamentals certification has the following included in its curriculum- conditioned probability, linear models, random forests, reduction, bagging, and many other topics that deal with the domain of machine learning. 

The examples that will be provided in the Machine Learning Fundamentals programme will be in the language of Python and will use Jupyter Notebooks. The candidates will also be practising various types of algorithms using data science and its applied tools. The Machine Learning Fundamentals online course will provide students with both discriminative and generative models. There are 2 tracks namely the audit track and the verified track into which the candidates may enroll. The candidates may access the course for a lifetime by paying a fee which is called the verified track, and the audit track asks for no fees but offers limited period access to the course.

The Introduction to Machine Learning Course is a free programme of ten weeks. This particular course is a part of the Data Analyst Nanodegree. The candidate can opt for that course after the completion of this one. The Introduction to Machine Learning Course provided is of the intermediate level. It will teach the end-to-end procedure and functioning of investigating data with the help of the machine learning lens. The course curriculum is based on self-paced learning. The Introduction to Machine Learning Course syllabus contains ten modules in total. The candidate will be given an Introduction to Machine Learning Course at the end of the course completion. The entire course will be taught by the industrial experts, along with complete training for each concept. 

Artificial intelligence and machine learning advances run hand in hand with the degree to which quantum computing footprint improves. Thus, it is natural to leverage machine learning capabilities with the advent of quantum computing. This Quantum Machine Learning certification course will explore the underlying principles associated with futuristic technologies. 

The program on Quantum Machine Learning encompasses the advantages of machine learning once it is coupled with futuristic quantum technologies. These have the potential to provide the much-awaited advancements of this industry. Some examples include machine learning and select frameworks in Python. 

This Quantum Machine Learning training program is intended to be delivered across 9 weeks along with an input of 6-8 hours per week by the participants on their own time for optimum results. The University of Toronto is the partner institution for this self-paced course. The video transcripts are in English and the students have an option to get a paid plus official certification. 

The candidates who enroll in any track of the course will be able to progress at their speed. Both tracks - audit and verified may be opted for by the candidates for finishing the course. If the candidates are interested in learning for free without a certificate they may choose the audit track which is open for a limited period of access. For unlimited access, the students should switch over to the verified track by paying some fees.

The Machine Learning ML program focuses on the scientific discipline that is related to the genre of Machine Learning and helps the students to develop algorithms to help the students find patterns that are related to make predictions from the domain of empirical data. In the online course on Machine Learning, ML by Swayam, the students would be finding patterns or will be making predictions from the given empirical data. 

The certification program over the course of eight weeks will be focusing on the following- Forms of representation, Examples, demos and exam preparations, Learning enabled by prior theories, Tools and resources + cognitive science, and Machine learning-based artificial neural networks. The Machine Learning ML certification course will be benefiting the students to incorporate the knowledge of machine learning in real-world examples and tree-based inductive learning along with logic programming. The session will be making the candidates professionally ready.

Amazon Web Services (AWS), the most comprehensive and vastly adopted cloud platform across the globe, is a fast, easy and cost-efficient platform to build ML applications. The AWS Machine Learning offers tools and services which cater for the developers and organisations to build ML models more efficiently. Globally, AWS Machine Learning is used by developers to build ML models that can test product designs, assess property damages, improve healthcare operations, analyse customer behaviour, fraud detection and much more.

Getting Started with AWS Machine Learning certification course is an online offered by AWS through the Coursera platform. This certificate programme will assist the participants to gain familiarity and working knowledge of AWS Machine Learning. The course content spreads over 5 weeks and includes module videos covering almost every significant aspect of machine learning. Participants will receive a shareable certificate upon completion of this online certificate programme.

The PyTorch Basics for Machine Learning certification programme is offered by IBM to arm you with the skills for implementing classic machine learning algorithms. You will get to know how PyTorch is used to optimise and create deep learning models. 

During the PyTorch Basics for Machine Learning training, you will learn important models such as linear and logistic regression models. This introductory-level programme will teach you how to train your models and their related concepts like gradient descent, cost and loss.

With the learnings of edX’s PyTorch Basics for Machine Learning course, you will learn to save your training and model in apps like checkpoints, early stopping and cross-validation for hyperparameter selection. Also, basics around the linear object, like their interaction with data with a distinctive number of samples and different dimensions, will be cleared.

The PyTorch Basics for Machine Learning course also includes a final project in the end to assess your skills and performance. You can even get a paid instructor-signed certification after completing the course. The learners also can audit the self-paced programme without paying the fee for a limited duration. 

Machine Learning finds widespread application in providing recommendations and making better predictions in software by deploying opinions from users and constructing a model automatically while eliminating the need to consider all the details of that model. Advanced Recommender Systems Certification by Coursera will expose learners to advanced machine learning techniques that can be used to create sophisticated recommender systems. 

Offered by EIT Digital and Politecnico di Milano, both of which are top-ranked in their respective domain, this course boosts one's creativity and innovation skills and implores them to think beyond the thinkable. Advanced Recommender Systems Certification Syllabus will deal with the management of hybrid information and a combination of various filtering techniques. This course will ensure that learners derive the best from each approach taught to them.

At the course conclusion, candidates will have been well acquainted with factorisation machines and how to represent input data accordingly. They will learn all the essential methods that can be used to solve the cross-domain recommendation problem. Finally, identifying new challenges and trends in giving recommendations for numerous innovative application contexts will also be highlighted in Advanced Recommender Systems Certification Course.

The Introduction to Machine Learning certification course is offered by Duke University. It will help you know your strengths and apply your skills in the most efficient manner to get work done. It will give you additional knowledge that will make you more confident in your work and perform better at your workplace or start your own business. 

The course will give you a brief overview of machine learning models like natural language processing, logistic regression, convolution neural networks, and multilayer perceptrons, while also explaining how these models can be put to use for solving technical problems in various industries like medical, image recognition, and text prediction. 

It is a combination of practical as well as theoretical knowledge about Machine learning, that will provide you with a framework for understanding how you can implement data science models and machine learning algorithms. The  Introduction to Machine Learning Training Course will allow you to work on a real-world hands-on project that is required in the fast-developing technological world. 

Amazon Web Services (AWS) is offering the Amazon SageMaker: Simplifying Machine Learning Application Development certification course. It will act as your guide for using Amazon SageMaker for the integration of ML or machine learning into your apps. Machine learning has surfaced as one of the most sought-after technological domains in recent times. Thus, the course can help you develop your skill-set in just a span of 4 weeks.

The Amazon SageMaker online course by edX will empower you with the knowledge of training a model with the help of a Jupyter Notebook. You will learn under the able guidance of AWS Training and Certification expert educators. Furthermore, you will have the assistance of video lectures, hands-on exercises, and demos to help you achieve fluency in the course concepts.

Moreover, the Amazon SageMaker training will allow you to hone your prior skills and help you gain new ones. You will have improved career opportunities at hand after completing this course. Although it is free of charge programme, you need to purchase the Verified Certificate track to access graded assessments and the programme certificate.

Apply Creative Machine Learning certification is a month-long course that only requires a student’s 2 hours in a week time. Developed by UAL Creative Computing Institute, and Institute of Coding, this certification course can be found on the FutureLearn platform. Among all the industries, machine learning is actually causing a revolution in the creative industries ranging from Music to Photography.

Apply Creative Machine Learning certification syllabus is all about the concepts that come under IT, and Computer Science. Any technology we use to interact is actually being changed by Artificial Intelligence. The main thing that this course teaches is the main concepts in machine learning using applied examples in real life.

With this Apply Creative Machine Learning training candidates will come to know how exactly machine learning works, and how simple classification systems are built. This system can help make a discrimination between the information types, and the regression systems that are used for mapping interactions to different outputs. The candidates will be understanding the full capacity of the abilities of Machine Learning keeping in mind its relationship with creative industries.

The Machine Learning Rapid Prototyping with IBM Watson Studio certification course will be allowing the students to work in the emerging technologies of AI. The syllabus is included in the online course Machine Learning Rapid Prototyping with IBM Watson Studio by Coursera. The online programme is  100% online programme and the course is having a duration of eight hours. The Machine Learning Rapid Prototyping with IBM Watson Studio certification syllabus includes the chapters of- Classification Prep Demo, Regression Prep Demo, Multi-armed Bandit Approach, Demo Classification: Making Changes to the Models, Automated Data Preparation, DAUB Algorithm, The model selection problem and others.

Machine Learning - Linear Regression certification course is developed by Leaps Analyttica TreasureHunt and is delivered by FutureSkills Prime, which is aimed at participants who are looking for a comprehensive training program to learn the functionalities of linear regression in a variety of machine learning and data science activities, as well as being used by organizations for future projections. Machine Learning - Linear Regression online course provides the expertise to design and validate a viable linear regression model on legitimate datasets.

Machine Learning - Linear Regression online classes by FutureSkills Prime will take the participants through the fundamental concepts of machine learning and linear regression including both simple and multiple linear regressions. This course also covers topics like exploratory data analysis, model iterations, variable selection, variable transformations, overfitting, and R square. By the end of this training, participants will be able to pre-process data and evaluate linear models as well as acquire an understanding of various stages of predictive modeling.