Online Machine Learning Courses & Certifications

The Machine Learning Course will help you become a competent Machine Learning Engineer and open a wide range of job opportunities for you. The course covers all the important concepts related to advanced machine learning. The curriculum includes topics like Statistics Fundamentals, Classification, Recommender Systems, and other machine learning relevant topics.

As a part of the Machine Learning course, you will get 39+ hours of live classes, 13 in-depth modules, 4 hands-on projects, access to 50+ placement partners, 1-1 career mentoring, and 13+ assignments. The assignments provided by Digital Vidya will help you become industry-ready.

Additionally, Digital Vidya offers various capstone projects on Machine Learning to provide experience via hands-on and best possible methods. What’s more, candidates can benefit from Digital Vidya’s placement cell, which provides a 100% interview guarantee.

Most importantly, the Machine Learning certification course will equip you with the core skills to master Python and Jupyter notebooks. The skills gained from this course will help you establish your place in the growing Machine Learning job market.    

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.

Throughout this programme, the candidates will enhance their skills in machine learning and will learn to apply these skills to problems of the real world, related to the field of business. This online programme of Imperial Machine Learning for Decision Making is provided to the candidates through the platform of Emeritus and is brought to them by the Imperial College Business School. 

The participating candidates will gain exposure to analytical techniques, for example, regression, classification and clustering and learn how to apply these techniques to real-life issues arising in the field of managing a business. The students will be provided with opportunities to have conversations with experts who have deep technical expertise about the subject, which will ultimately help them in having a greater impact on the company they work in. 

This course is for a duration of 10 weeks wherein the students will not only be learning the theoretical know-how but also the practical applications of the concepts learnt.  

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. 

With machine learning and artificial intelligence gaining immense popularity, the skills of future-ready analysts, engineers, technologists, and data managers also must expand, shift,  and advance. Machine Learning: Fundamentals and Algorithms online course will provide you with ample technical knowledge and analytical methods to prepare you for the next gen of innovation.

Organised around 10 modules, the Machine Learning: Fundamentals and Algorithms course help you deepen and broaden your Python programming skills for ML applications. You can further apply this technical knowledge to the industry verticals currently integrating machine learning and AI into their digital drivers.

The Machine Learning: Fundamentals and Algorithms programme is designed for participants with Python programming experience who want to learn more about the underlying mathematics. Upon programme completion, you will receive a verified digital certificate from Carnegie Mellon University. 

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.

Machine Learning in Business by the Rotman School of Management, University of Toronto is a program that will make a data science team better and more proficient and give the data analysts the opportunity to traverse and communicate their language and mindset to their businesses which in turn will increase the chances of greater business benefits.

Machine Learning in Business being a subsection of Artificial Intelligence implements means that assist the data analyst in examining huge sets of data in every other field whether in healthcare or finance. This course is a thorough run-through to the candidates about machine learning and its numerous methodologies. 

This program ensures that candidates address the managerial doubts but at the same dive into the world of machine learning and learn the various consequences it can have on a business in the future. Machine Learning in Business program is designed to locate fast-growth areas of business via machine learning techniques which includes but is not limited to Support Vector Machines (SVMs) and Natural Language Processing (NLP).

The Machine Learning Foundation certification course is an online course of three-month live projects and six days or three weekends training that covers Python, machine learning algorithms, and deploying machine learning models. The whole Machine Learning Foundation training will take place via an online platform and the candidate will have complete access to that after enrolling in the course. The candidate can get Machine Learning Foundation certification at the end of the course accredited from IABAC. The complete curriculum of the Machine Learning Foundation certification syllabus is structured according to the emerging demand of the industries. 

The Coursera Probabilistic Graphical Models 3: Learning certification course covers probabilistic graphical models, commonly known as PGMs. They are a rich framework used for encoding probability distributions over complex domains. They form the foundation of state-of-the-art methods in different applications such as speech recognition, natural language processing, medical diagnosis, image understanding, and many more. They are also instrumental in formulating many machine learning problems. 

The Probabilistic Graphical Models 3: Learning training is the third course in a series of three. Where the first course focused on representation, and the second focused on inference and the final course helps in addressing questions related to learning. The course discusses the main problems of parameter estimation in both directed and undirected models. 

Moreover, students can receive a course completion certificate for the Coursera Probabilistic Graphical Models 3: Learning programme. Candidates can attach the certificate to their LinkedIn profile or their resume/CV.

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.

Machine learning, which combines the areas of statistics, optimization, computing, and data mining, is being embraced across sectors due to its capacity to solve issues in the availability of large data sets. The Machine Learning: Practical Applications online course covers a wide spectrum of machine learning methodologies, with a focus on artificial intelligence in current business analytics.

Modern machine learning achievements include its use in commercial activities like recommendation systems (like Amazon and Netflix), search engines, and promotion. In financial companies, machine learning technologies are rapidly being utilized for algorithm-based trading, forecasting consumer behaviour, risk, and compliance. The Machine Learning: Practical Applications Training from the London School of Economics provides a systematic approach to artificial intelligence in current business statistics.

The Machine Learning: Practical Applications syllabus engages with real-world challenges by implementing various machine learning models to large datasets in R, interpreting the predictions, and evaluating these predictions to guide the business.

The Deep Neural Networks with PyTorch certification course will teach candidates, how to use Pytorch to create deep learning models. It is a part of the IBM AI Engineering Professional Certificate. There are a total of 6 courses in that specialisation. The Deep Neural Networks with PyTorch training course is the fourth one of them. Artificial intelligence or AI is revolutionising whole industries by transforming the way businesses exploit knowledge to make decisions across sectors. Organisations need skilled AI engineers who use cutting-edge approaches such as machine learning algorithms and deep learning neural networks to provide their companies with data-driven actionable intelligence in order to remain competitive. This Technical Certificate of 6 courses is designed to equip candidates with the tools which are needed to excel as an Artificial Intelligence or Machine Learning engineer in their career. 

Using programming languages such as Python, candidates will master the basic principles of machine learning and deep learning, which includes supervised and unsupervised learning. Candidates will also obtain a digital badge from IBM acknowledging their proficiency in AI engineering in addition to receiving a Technical Certificate from Coursera.

Machine Learning Certification Course online training is designed by Board Infinity, a virtual learning environment for trainees that would aid them in obtaining excellence and fulfilling their professional goals. Machine Learning Certification Course online training by Board Infinity is for participants who desire to acquire the scientific study of algorithms and statistical models that machine learning utilizes to become professional machine learning engineers.

Machine Learning Certification Course online classes cover a wide range of machine learning topics, including natural language processing, deep learning, supervised and unsupervised learning, artificial neural networks, probability, and statistics, convolutional neural networks, and include more than 40 hours of comprehensive learning, as well as case studies and projects. This course also includes sections on Python, pandas, OpenCV, Numpy, Anaconda, ANOVA, Matplotlib, Seaborn, and other programming languages.

The Mathematics for Machine Learning: Multivariate Calculus course provides a brief introduction to multivariate calculus which is necessary for constructing several popular techniques for machine learning. The aim of the course is to provide an intuitive understanding of calculus, as well as the language required to look up concepts when candidates get lost. Candidates can still come away with the courage to dive into some more oriented machine learning courses in the future without going into too much detail. 

The Mathematics for Machine Learning: Multivariate Calculus course is the second one of the 3 courses in mathematics for machine learning specialisation. The goal of this specialisation is to bridge the gap then bring candidates up to speed in the underlying mathematics, develop an intuitive understanding, and relate it to data science and machine learning. The specialisation is pointed at what linear algebra is and how it applies to data in the first course on Linear Algebra. It will then draw attention to what exactly are matrices and vectors and how one can operate them. 

According to a Hackernoon report, 74% of decision-makers, corporate owners and IT executives believe machine learning has the ability to transform their work and sector. Companies that use machine learning to enhance organizational performance and improve the decision-making process have clear benefits over those that do not. The Machine Learning online course aims to provide candidates with the knowledge and skills needed to comprehend the effect of machine learning and effectively articulate its integration.

It has never been simpler for businesses of all sizes to leverage the value of data. Introduction open-source and free Artificial intelligence and machine learning technologies. Embedding machine learning technologies into an organisation effectively necessitates a practical grasp of its concepts. The Machine Learning training teaches about the various types of modelling concepts used in machine learning. The Machine Learning syllabus focuses on determining the models, methodologies, and ethical implications of machine learning techniques, as well as presenting an economic case for using them in the firm.

Machine Learning in Business certification is basically a 6 weeks small course designed by  MIT Sloan School of Management and the MIT Computer Science and Artificial 

Intelligence Laboratory (CSAIL)along with its content partner being GetSmarter. With this programme, candidates can learn about how machine learning can be capitalized on by businesses.

The Machine Learning in Business certification syllabus is divided into 6 weeks which is taught online, and it will help in answering questions related to machine learning which is a branch of Artificial Intelligence. Basically here the programming of Computer Science will be used to improve data performance in businesses.

Machine Learning in Business training is an online executive programme that is guided by the MIT faculty to help the participants understand the foundational level capabilities of Machine Learning along with its advantages and limitations so that its’ potential can be unlocked within businesses. This certification course will give an opportunity for designing a roadmap towards the integration of business needs with machine learning successfully.

Machine Learning: Clustering & Retrieval is the University of Washington-offered online certification programme. The online course will expose the students to the practical understanding of clustering and retrieval aspects of machine learning through the case study of Finding Similar Documents. Machine Learning: Clustering & Retrieval Certification Course facilitates the students to take the course completely in online mode from anywhere in the world. 

Provided by Coursera, Machine Learning: Clustering & Retrieval Certification Syllabus will discuss the similarity-based algorithms for retrieval,  latent Dirichlet allocation (LDA), clustering, and the implementation of expectation maximization (EM) and the like. Machine Learning: Clustering & Retrieval Certification by Coursera is the fourth and last course in the Machine Learning Specialization. 

In the present times where the advances in technology have shifted to IoT (Internet of Things), the concept of Deep Learning can be foreseen as one of the crucial ones, to be understood and well researched. Deep learning is faster and easier to develop and deploy as well. The Introduction to Deep Learning & Neural Networks with Keras certification course, the participants will get a deep insight into the Deep learning models. Furthermore, they will also be able to create models of Deep learning by making use of the Keras library.

The entrant will be introduced to exhilarating applications of Deep Learning. They will be learning about various deep learning algorithms and neural networks which are inspired by the functioning of neurons and the brain with respect to data processing. They will also get an insight into the working of the neural networks to feed the data throughout the network.

The Introduction to Deep Learning & Neural Networks with Keras training will provide knowledge about optimisation of variables as per the defined function, gradient descent algorithm, backpropagation, and updating the weights and biases of neural networks. Furthermore, the entrant will be taught about the activation functions and vanishing gradient problems. 

The Imperial College of London offers the Getting Started With TensorFlow 2 online course in conjunction with Coursera. Achieving certification in courses offered by this esteemed institution gives you an inclusive educational experience and superior digital technology. 

The Getting Started With TensorFlow 2 certification course has modules spread out over five weeks, which candidates can complete in approximately 26 hours. Dr Kevin Webster, a Senior Teaching Fellow in Statistics at the Imperial College London, instructs the certification course. 

An intermediate-level course, the Getting Started With TensorFlow 2 training will teach you the fundamentals concepts of TensorFlow. You will become fully adept in developing deep learning models and using them to evaluate, train, and make predictions. 

Furthermore, the Getting Started With TensorFlow 2 certification course promotes interaction and improvement of one’s core abilities with a comprehensive curriculum and intermittent assignments. You can self-assess through these mediums and grasp the course contents at your own pace. Finally, Coursera awards you with a certificate upon successful completion. 

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 Managing Machine Learning Projects certification course is a lunch of Duke University’s Pratt School of Engineering, and Coursera. This is course number 2 of the main AI Product Management Specialization programme. The course has a main focus on the practical aspects related to the management of projects in machine learning.

The Managing Machine Learning Projects training will take around 18 hours to get completed. This Coursera programme will walk every participant through different processes like data collection, building of a model, data collection, deployment, and maintenance of the systems of production. Apart from the above the candidates will also be taught processes in data science so that decision making in ML systems can be easier. 

With the increasing use of technology and machinery in our daily lives, people have started accepting and learning various methods and techniques. It has become essential to possess the skills and capabilities to beat the competition. Neural Network course certification online teaches various algorithms which enable pattern recognition.

The Neural Networks course by Jigsaw Academy online certification is a framework specially designed and conceptualized for candidates interested in learning advanced machine learning algorithms that can enable pattern recognition based on a large number of inputs. The Neural Networks course will provide a deep dive into the world of neural networks, make you understand the algorithms and techniques, and ultimately gives an insight into deep learning. 

The University of Alberta and Alberta Machine Intelligence Institute offer the Sample-based Learning Methods training programme in conjunction with Coursera. An intermediate-level course, the programme is spread across four weeks, with intensive modules on reinforcement learning. The course will also help you understand how learning from experience can help you attain optimal behaviour. 

The Sample-based Learning Methods certification course will help you understand concepts like Temporal Difference learning, Monte Carlo strategy, exploration, dynamic programming, and more. Moreover, the certification course will provide you with a chance to apply TD algorithms, Expected Sarsa, Q-learning, and more.

Instructors will conclude the Sample-based Learning Methods online course by looking at how algorithms can combine model-based planning and temporal difference updates to fast-track learning. Additionally, the certification course offers the flexibility to learn at your own pace in your own time and set your own deadlines. 

You will also receive a combined shareable certificate from the University of Alberta and Coursera, upon completion of the Sample-based Learning Methods course.