Machine Learning Certificate Courses

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.

The Machine Learning for Engineering and Science Applications certification course offers a duration is 12 weeks which focuses on providing a a broad overview of modern algorithms in ML for engineers. This course covers the key aspects of machine learning such as the basics of ML, Linear Algebra, Numerical Computation, Neural, and Autoencoders.

Students gain hands-on training from industry experts in applying ML algorithms in the engineering field. The Machine Learning for Engineering and Science Applications certification by NPTEL intended audience is postgraduate students in all engineering and science fields and mature senior undergraduate students can also pursue this course.

Also Read: Online Machine Learning Courses & Certifications

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 Engineer with Microsoft Azure online course is designed to deliver the knowledge of Azure Machine Learning over the cloud covering the use and benefits of Machine Learning in the cloud. The course teaches how to manage and choose computer resources to train Machine Learning models for quick and accurate predictive results.

The Machine Learning Engineer with Microsoft Azure syllabus explores training of Machine Learning Models in Azure including supervised model, unsupervised model and Reinforcement model. The program helps to learn using various types of tools in Microsoft Azure to train and deploy a machine learning model for pipeline automation. The nano degree course covers key concepts to analyze data and construct data sets suitable for machine learning.

The Machine Learning Engineer with Microsoft Azure nano degree program in collaboration with Microsoft focuses on Configuring cloud-based machine learning model with the help of Microsoft Azure services and tools, training a model on datasets using Automated Machine Learning and Analyzing the performance of the trained models. The Course covers in-depth knowledge of shipping machine learning models into production.

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 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 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. 

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 Machine Learning for Earth System Sciences certification course offers a comprehensive exploration of spatio-temporal statistics and data mining, focusing on their applications in earth system sciences. Intended for final-year undergraduates, postgraduates, and research students, it covers earth system observations, data analytics, and modelling in domains like hydrology and climate.

Prerequisites include mandatory machine learning knowledge, optional deep learning expertise, and a working understanding of earth system science domains. The Machine Learning for Earth System Sciences certification by Swayam caters to both undergraduate and postgraduate levels, fostering a solid grasp of statistical concepts in analysing earth system phenomena.

Also Read: Online Machine Learning Certification Courses

The Practical Machine Learning with Tensorflow certification course offers a comprehensive curriculum that covers the key aspects of Tensorflow and Machine Learning such as data input, Tensorflow preprocessing, and prediction. This certification course lasts 8 weeks, and its prerequisites are Programming, Data Mining, Machine Learning, or Data Science. 

The students gain hands-on training in the subject from industry experts. The Practical Machine Learning with Tensorflow certification by NPTEL equips students with the required skills and knowledge to build ML models using Tensorflow. The candidates also learn the application of techniques, principles, and strategies in real-world projects.

Also Read: Online Machine Learning Courses & Certifications

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. 

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.