Machine learning certifications in 2021 will pave new career opportunities
Machine learning is fast becoming an evolving field of study. Companies across industries worldwide seek to leverage the growing features of this technology and pivot its capabilities in their products and services. The increasing significance of machine learning technology results in the demand for professionals with relevant skillsets. As a popular field of artificial intelligence, machine learning augments human capabilities. In-demand job roles in this growing technology include Machine Learning Engineer, Data Scientist, NLP Scientist, Business Intelligence Developer, Machine Learning Designer, and more.
To get started with these roles requires aspirants hands-on skills and knowledge that can help them play with data and algorithms, and derive meaningful insights for business growth.
Here is the list of top 10 machine learning certifications that promise to boost a career in 2021.
Offered by: Google Cloud on Coursera
Duration: 9 Weeks
This program comprises introductory-level lessons and covers machine learning, what kinds of problems it can solve and why is it so popular. The course also covers classes that focus on Tensorflow, an open-source machine learning framework. Consisting of five courses, this specialization will teach students how to frame a business use case as a machine learning problem and convert a candidate use case to be driven by ML. Students will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.
Offered by: IBM on Coursera
Duration: 4 Weeks
This course offers aspirants real-life examples of machine learning and explains how it affects society. The course dives into the basics of machine learning using an approachable, and well-known programming language, Python. It can be applied to multiple specializations or professional certificate programs. Completing this course will count towards students’ learning in any of the programs: IBM AI Engineering Professional Certificate and IBM Data Science Professional Certificate.
Offered by: BerkeleyX on edX
Duration: 4 Months
The Professional Certificate in Foundations of Data Science teaches how to interpret and communicate data and results using a vast array of real-world examples from different domains. In this course, students will learn, how to make predictions using machine learning and statistical methods, computational thinking and skills, including the Python programming language for analyzing and visualizing data. They will also learn how to think critically about data and draw robust conclusions based on incomplete information.
Offered by: IBM on Coursera
Duration: 6 Months
This professional certificate from IBM is designed for anyone interested in developing skills and experience to pursue a career in machine learning. This program involves six courses providing students with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to machine learning. After the completion of this course, learners will receive a digital Badge from IBM recognizing proficiency in machine learning.
Offered by: eCornell
Duration: 3.5 Months
This certificate program equips students to implement ML algorithms using Python. By using a blend of math and intuition, learners will practice framing machine learning problems and construct a mental model to understand how data scientists approach these problems programmatically. This certificate program consists of two self-paced lessons covering the linear algebra computations used in the machine learning curriculum.
Offered by: Harvard University on edX
Duration: 8 Weeks
This course from Harvard University teaches popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. Students will learn about training data, and how to use data sets to discover potentially predictive relationships. They will also learn how to train algorithms using training data to predict the outcome for future datasets.
Offered by: National Research University Higher School of Economics on Coursera
Duration: 10 Months
This specialization gives candidates an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Learners will master their skills by solving various real-world problems like image captioning and automatic game playing throughout the course projects. They will also gain the hands-on experience of applying advanced ML techniques that provide the foundation to the current state-of-the-art in AI. Upon completion of seven courses, learners will be able to apply modern ML methods in enterprise and understand the caveats of real-world data and settings.
Offered by: Georgia Tech on Udacity
Duration: 4 Months
This course involves two modules. The first module covers Supervised Learning, a machine learning task that makes it possible for users’ phone to recognize their voice, email to filter spam, and for computers to learn a bunch of other exciting things. The second module teaches about Unsupervised Learning. Ever wonder how Amazon knows what users want to buy before they do? Or how Netflix can predict what movies users will like? This section answers such questions. Finally, can people program machines to learn like humans? This Reinforcement Learning section will teach learners the algorithms for designing self-learning agents like humans.
Offered by: fast.ai
Duration: 12 Weeks
This course teaches the most significant machine learning models, including how to create them from scratch, as well as key skills in data preparation, model validation, and building data products. Consisting of around 24 hours of lessons, the course is based on the University of San Francisco for the Masters of Science in Data Science program.
Offered by: AWS
Duration: 8 Hours
This course will teach candidates how to get started with AWS Machine Learning. It includes key topics such as Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP) on AWS. Each topic comprises several modules to deep dive into a variety of ML concepts, AWS services as well as insights from experts to put the concepts into practice. Throughout this course, students will learn key problems that machine learning can address and ultimately solve. They will also learn how to build, train and deploy a model using Amazon SageMaker with built-in algorithms and Jupyter Notebook instance, and much more.