Coursera Machine Learning Courses & Certifications

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. 

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 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.

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. 

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.

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. 

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.

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. 

Reinforcement learning is a machine learning method that helps to understand how the software agents should take actions according to the situations and requirements. Helps solve all the problems that have large or high dimensional and potential limitless spaces available. It helps one to investigate how policy prediction and evaluation methods like TD and Monte Carlo can be used to help function approximation settings. 

Prediction and Control with Function Approximation certification course by Coursera include features construction techniques for representational learning through a neutral network, RL and backdrop. This course is designed for the candidates who have completed the first and the second part of the reinforcement learning and specialization training as it is a continuation of the same.

By doing the Prediction and Control with Function Approximation training course they will be a field ready and gain knowledge that will help them master their skills and help upgrade their portfolio which will, in the end, help them towards their successful career. 

The course Fundamentals of Reinforcement Learning certification course is offered by Coursera by collaboration with the University of Alberta to make the aspirants skilful with machine learning and applications of Artificial Intelligence. The course is free to enrol and can access all the course materials free of cost. Therefore aspirants can use the opportunity for growing their career with a course completion certificate and increase the chances of hiring.

With the digital connection over the world, Business Intelligence over decision making is made essential in organizations. Decision making increases the prosperity of the business. Therefore the use of decision making with Artificial Intelligence and machine learning became most popular when the programme comes into existence. Organizations are looking forward to candidates who are skilled in this type of Artificial Intelligence, Robotics, Machine learning and, the Internet of things (IoT) as well

Coursera is the digital platform to learn the concepts that are offered for free. Candidates can learn throughout their lifetime and can avail of certificates after completion of the Fundamentals of Reinforcement Learning Training course. 

The Introduction to Trading, Machine Learning & GCP online course is an intermediate level of course. Students can look up the programme on the official website of Coursera. In this programme, students will learn about trading, its features, trends, and aspects related to trading. The course will discuss the different quantitative strategies that are considered for trading and trading structure. 

The course also talks about Google cloud and machine learning aspects in the programme. The course is a free programme that does not require any admission fee or registration charges and allows the candidates to enjoy the learning experience at their own speed. The Introduction to Trading, Machine Learning & GCP certification syllabus of the program is categorized on a weekly basis and demands four weeks for the program. 

Students are free to set their schedules according to their willingness and the entire program can be completed in 9 hours. Through the assignments and exercises, the students are able to understand every concept properly. At the end of the Introduction to Trading, Machine Learning & GCP certification course, students can also complete the procedure of getting a certificate if they want to be a certified user of the program.

The online program on Advanced Machine Learning and Signal Processing by Coursera is a part of the series of programs that are offered in the domain of “advanced machine learning and signal processing”. The Advanced Machine Learning and Signal Processing training is part of the courses that are offered by IBM on advanced data science specialization. In the course, the frameworks for python using SparkML and Scikit-Learn will be introduced. The Advanced Machine Learning and Signal Processing certification syllabus will be covered on the digital platform within a time frame of 28 hours.

The Managing Machine Learning Projects with Google Cloud certification course is designed as a beginner level programme. The programme can be joined from the platform of Coursera. It offers a self-paced learning experience and students can learn from the available study material without any restrictions. 

The programme does not charge any amount for joining and learning from the course. Participants will be able to complete the whole Managing Machine Learning Projects with Google Cloud certification syllabus in approximately fourteen hours as it contains eight modules that will take up to a month. Students will be able to gain information regarding machine learning with respect to business problems. 

Students will be able to obtain a certificate if they complete the Managing Machine Learning Projects with Google Cloud programme within the given time. Candidates have the option to reset the deadlines according to the schedule of their suitability. The course also includes quizzes, readings, discussions, and assignments in order to provide a better learning experience for the students. The topics included in the Managing Machine Learning Projects with Google Cloud certification can be completed in the duration of one to three hours. 

The Machine Learning for Data Analysis certification course is a 10 hours course taught by Wesleyan University, and Coursera. If students want to learn about predicting outcomes of the suture with data available today then this Coursera course is the best option that they can avail. The process of machine learning taught through this programme will help in testing, then applying, and finally applying algorithms that are predictive for achieving different kinds of goals. 

The Machine Learning for Data Analysis training is course 4 of 5 of the main Data Analysis and Interpretation Specialization programme. After this course gets completed, the candidates will be learning ways of interpretation algorithms of machine learning. These concepts will be serving as alternative methods that will help in giving answers to research questions.

Guided Tour of Machine Learning in Finance is offered by Coursera for intermediate-level students and learners to know the application of machine learning in finance. The online programme is the first programme in the Machine Learning and Reinforcement Learning in Finance Specialization offered by New York University and the Guided Tour of Machine Learning in Finance Certification Course can also be taken as a separate single programme. 

Provided by Coursera, Guided Tour of Machine Learning in Finance Training will shed light on machine learning and the application of ML to cope with various financial problems. Though the Guided Tour of Machine Learning in Finance Certification by Coursera is offered for intermediate learners, it is very needed for the learners to have an understanding and experience of Python, linear algebra, basic probability theory, and basic calculus to successfully complete the assignments. 

Introduction to Machine Learning in Sports Analytics is an intermediate-level online course provided by Coursera which is one of the leading online course providers.  The online certification programme will guide the learners on how to use machine learning in sports analytics. Offered by Coursera, Introduction to Machine Learning in Sports Analytics Certification Course is the last course in the Sports Performance Analytics Specialization offered by the University of Michigan. 

Introduction to Machine Learning in Sports Analytics Certification Syllabus will discuss the machine learning techniques, the prediction of athletic outcomes with machine learning, and many more. Introduction to Machine Learning in Sports Analytics Certification by Coursera can be taken by learners who have familiarity with Python. 

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.

End-to-End Machine Learning with TensorFlow on GCP is the successor of the first course Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization. This programme is included in a 5-course specialization that focuses on the topics of advanced machine learning that uses Google Cloud Platform. This course would give real-life experience by improving, establishing, and measuring production.

Through this course, the candidates would learn to build innovative, correct, and manufacturing-ready models for organised data, time series, natural language text, and image text. The course will end on recommendation systems. This programme does not have any schedule adherence; the candidates can look at the study materials according to their availability.  Along with theoretical concepts, the candidates would also learn about the practical approach of advanced machine learning.

The Launching into Machine Learning programme is a part of the Machine Learning with TensorFlow specialisation, offered by Google Cloud. This intermediate-level online professional course takes about 22 hours to complete. The course of instruction in English and subtitles are also provided for ease of grasping. 

The Launching into Machine Learning online course is offered by industry-leading professionals who have had multiple years of work experience in Machine learning and Tensorflow. Thus, you can be assured of being trained by the best in class. Through this course, you will understand the essence of deep learning and how to optimise it to mitigate common problems in machine learning. 

The Launching into Machine Learning certification syllabus provides you with repeatable and scalable test datasets perfect for training and evaluation. Moreover, you will learn to evaluate the models through loss functions and performance metrics. What’s more, the flexible schedule allows you to learn at your own pace and convenience. 

The Introduction to TensorFlow certification course is thoroughly an online programme that is available for free for a specific period of time. The main focus of the programme is to make students aware of the Tensor flow. The training will revolve around the topics of datasets, designing and building of Tensorflow, and other features of the programme, etc. The course will take up to nineteen to twenty hours of learning.

The Introduction to TensorFlow online course is created for the students who want to learn from the course at their own pace so that they can take as much time as required to completely understand every topic. There are no last dates for the programme and it has no limitations with regards to attendance percentage or timings of attending the sessions.

The course provides an intermediate level of learning and requires no academic qualifications to get admitted into the course. Students also get an opportunity to become a certified user of the course and obtain a verified certificate when the Introduction to TensorFlow certification syllabus is completed by the participant. The language of the programme is set for English, however, participants who have a language barrier can use the subtitles that are available in Russian, Spanish, Portuguese (European), English, and French languages.

The Feature Engineering certification course is of an intermediary level of training which is a hundred percent online course and it can be viewed on the website of Coursera. The programme is within the easy reach of the enrolled students at zero cost. The training is a part of the course named Machine Learning with TensorFlow on Google Cloud Platform Specialization that is available on Coursera. Aspiring participants who join the online course of Feature Engineering will be able to comprehend several features used for distinct functions in order to make an accurate model of machine learning.

In the Feature Engineering online course, students are not limited to follow a particular set of reading materials and they can learn from the external study matter along with the one provided by the platform within the programme. The programme does not limit students with any fixed schedules or deadlines and participants are allowed to set their own study schedules according to their preferences and requirements.

This intermediate level of the programme can be concluded by spending a total of approximately 14 hours. The Feature Engineering certification syllabus of the programme is arranged in a weekly manner and is split up into a total of 4 weeks or 1 month. If the participant successfully completes the curriculum, he or she will be able to receive a certificate for the online course from the platform.