Coursera Machine Learning Courses & Certifications

Machine learning and artificial intelligence are new technological steps that can be applied in numerous fields to bring innovative breakthroughs. Machine learning and artificial intelligence has also an extraordinary scope and application in the medical and healthcare field. Fundamentals of Machine Learning for Healthcare will help the students to understand the significance of machine learning and artificial intelligence in the medical field and their potential to bring about transformations. 

Fundamentals of Machine Learning for Healthcare Certification Syllabus will introduce the learners to the basic concepts of machine learning and its applications in medicine and healthcare. By taking the course offered by Stanford University, the students will learn healthcare, health policy, pharmaceutical development, data science, and many more. 

Fundamentals of Machine Learning for Healthcare Certification Course, administered by Coursera, is a beginner-level programme that can be taken by the candidates at their own pace. Fundamentals of Machine Learning for Healthcare Certification by Coursera, instructed by Matthew Lungren who is the Associate Professor at the Department of Radiology, and Serena Yeung who is the Assistant Professor at Biomedical Data Science, is the third course in AI in Healthcare Specialization. 

The Launching into Machine Learning programme is a part of the Machine Learning with TensorFlow specialisation, offered by Google Cloud. This Beginner-level online professional course takes about 14 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 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 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. 

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. 

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 Using Machine Learning in Trading and Finance online course is providing a platform for the students to learn and study about the basics that are involved in developing advanced strategies that are used in modern machine learning techniques. 

In the online programme on  Using Machine Learning in Trading and Finance by Coursera, the applicants will be studying the key components that are common to all the trading strategies irrespective of the platforms and dimensions. 

In the Using Machine Learning in Trading and Finance certification syllabus, the enrolled students will be covering the comprehensive chapters of-  Regularization: L1, L2, and early stopping, Regularization: dropout, Lab Intro: Keras functional API, Regularization: the basics, Neural networks with Keras functional API, Collect the data, Backtest on unseen data, Selecting a machine learning algorithm, Lab Intro: momentum trading, Creating features, Define the problem, Split the data and others.

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.

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. 

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. 

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 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 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 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.

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. 

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 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.

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. 

Machine Learning With Big Data is an online certification programme administered by the University of California San Diego to help the students explore machine learning with big data concepts. Machine Learning With Big Data  Certification Syllabus, developed by Mai Nguyen, the Lead for Data Analytics, and Ilkay Altintas, the Chief Data Science Officer, will give the students a clear understanding of strategies of machine learning for the analysis and exploration of data. 

In the Machine Learning With Big Data Certification Course, administered by  Coursera, the candidates will learn to build machine learning models with numerous algorithms and tools as well various concepts of big data. Machine Learning With Big Data Certification by Coursera is the fourth in six programmes of the Big Data Specialization.