21 Courses and Certifications

Coursera Machine Learning Courses & Certifications

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Machine Learning

Machine Learning is a highly demanded skill in the 21st Century. ML is the application of Artificial Intelligence which 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 which 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 in 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. 

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11 Weeks
Free
Skills Covered:
Machine learning
Deep Neural Networks with PyTorch

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Certificate

Deep Neural Networks with PyTorch

The Deep Neural Networks with PyTorch 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 course is the fourth one of them. Artificial intelligence or AI is revolutionizing 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.

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7 Weeks
Intermediate
Skills Covered:
Knowledge of deep learning
Certificate

Introduction to Machine Learning

The Introduction to Machine Learning 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. 

Introduction to Machine Learning Course will allow you to work on a real-world hands-on project that is required in the fast-developing technological world. 

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6 Weeks
Intermediate
Free
Skills Covered:
Natural Language Processing Machine learning
Certificate

Mathematics for Machine Learning Multivariate Calculus

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. 

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6 Weeks
Beginner
Skills Covered:
Mathematical skill
Introduction to Deep Learning and Neural Networks with Keras

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Certificate

Introduction to Deep Learning and Neural Networks with Keras

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 to deploy as well. In an Introduction to Deep Learning & Neural Networks with Keras programme, 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 the 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 programme 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. 

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5 Weeks
Intermediate
Free
Skills Covered:
Machine learning Knowledge of Artificial Intelligence Knowledge of deep learning
Certificate

Getting Started with TensorFlow 2

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 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 Imperial College London, instructs the certification course. 

An intermediate-level course, the Getting Started With TensorFlow 2 online course 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 Tensor Flow 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. 

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5 Weeks
Intermediate
Skills Covered:
Machine learning Knowledge of deep learning
Getting Started with Amazon Web Services Machine Learning

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Certificate

Getting Started with Amazon Web Services Machine Learning

We live in a digital era now, powered by Artificial Intelligence and Machine Learning. World over, developers are constantly striving to apply ML to systems in order to solve complex problems, facilitate effective decision-making and benefiting the society at large. With Big Data surrounding in almost every possible means, machine learning has assumed great significance because of its simplicity, flexibility and applicability to analyse this data and produce significant outcomes.

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 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 is an online course 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.

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5 Weeks
Intermediate
Free
Skills Covered:
Machine learning Knowledge of Artificial Intelligence
Certificate

Probabilistic Graphical Models 3: Learning

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 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.

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5 Weeks
Expert
Skills Covered:
Knowledge of Algorithms
Advanced Recommender Systems

Offered by

Polytechnic University of Milan, Milan , EIT via Coursera
Certificate

Advanced Recommender Systems

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.

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5 Weeks
Intermediate
Free
Skills Covered:
Mathematical skill

Offered by

University of Alberta via Coursera
Certificate

Sample Based Learning Methods

The University of Alberta and Alberta Machine Intelligence Institute offer the Sample-based Learning Methods 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.

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4 Weeks
Intermediate
Skills Covered:
Machine learning Knowledge of Artificial Intelligence

Offered by

University of Alberta via Coursera
Certificate

Prediction and Control with Function Approximation

Reinforcement learning is a machine learning method which helps to understand how the software agents should take actions according to the situations and requirements. Helps solving 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 functioning approximation settings. 

It includes feature 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 course they will be field ready and gain knowledge that will help them master their skills and help upgrade their portfolio that will at the end help them towards their successful career. 

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4 Weeks
Intermediate
Skills Covered:
Machine learning Knowledge of Artificial Intelligence

Offered by

University of Alberta , Alberta Machine Intelligence Institute via Coursera
Certificate

Fundamentals of Reinforcement Learning

The course Fundamentals of Reinforcement Learning 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 for free of cost. Therefore aspirants can use their 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 the organizations. Decision making increases the prosperity of the business. Therefore the use of decision making with Artificial Intelligence and machine learning became most popular where the programme comes into existence. Organizations are looking forward to the candidates who are skilled in this type of Artificial Intelligence, Robotics, Machine learning and, 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 certificate after completion of the course. 

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4 Weeks
Intermediate
Skills Covered:
Decision making skills Machine learning Knowledge of Artificial Intelligence
Using Machine Learning in Trading and Finance

Offered by

New York Institute of Finance, New York , Google via Coursera
Certificate

Using Machine Learning in Trading and Finance

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.

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4 Weeks
Intermediate
Skills Covered:
Machine learning
Introduction to Trading, Machine Learning and Google Cloud Platform

Offered by

New York Institute of Finance, New York , Google via Coursera
Certificate

Introduction to Trading, Machine Learning & GCP

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.

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4 Weeks
Intermediate
Free
Skills Covered:
Knowledge of Algorithms Machine learning Knowledge of cloud computing
Machine Learning Rapid Prototyping with IBM Watson Studio

Offered by

Certificate

Machine Learning Rapid Prototyping with IBM Watson Studio

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 nine 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.

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4 Weeks
Intermediate
Free
Skills Covered:
Machine learning
Supervised Machine Learning Classification

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Certificate

Supervised Machine Learning Classification

A part of the multiple series programme, the online session on Supervised Machine Learning: Classification by Coursera provides the students with a detailed emphasis on the main types of modelling that are involved with the genre of machine learning. The students will learn elaborately to train the predictive models that are involved and used across various domains. The Supervised Machine Learning: Classification certification syllabus will be covered on the online platform by the students over a time frame of 11 hours. The course will train the students professionally in the domain of machine learning.

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4 Weeks
Intermediate
Free
Skills Covered:
Machine learning
Advanced Machine Learning and Signal Processing

Offered by

Certificate

Advanced Machine Learning and Signal Processing

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 27 hours.

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4 Weeks
Expert
Free
Skills Covered:
Machine learning

Offered by

Certificate

Launching into 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. 

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4 Weeks
Intermediate
Skills Covered:
Machine learning

Offered by

Certificate

Managing Machine Learning Projects with Google Cloud

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. 

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4 Weeks
Beginner
Skills Covered:
Machine learning

Offered by

Certificate

Feature Engineering

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 18 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.

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4 Weeks
Intermediate
Skills Covered:
Machine learning

Offered by

Certificate

End-to-End Machine Learning with TensorFlow on GCP

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.

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3 Weeks
Beginner
Skills Covered:
Machine learning Knowledge of cloud computing

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