Machine Learning and Data Science Certification

Machine Learning is one of the fastest-growing technologies of the 21^st century. The scope of machine learning technology and applications is rapidly increasing in all industries such as healthcare, marketing, finance, banking, trading, education, infrastructure, etc. Due to ML's popularity, the demand for ML engineers is also exponentially increasing in companies. Everyone wants to implement ML technologies into their business and make ML a pivotal product feature. ML professionals are much in demand and are offered unexpected packages in their careers.

Machine learning is the subset of artificial intelligence that makes machines capable of learning by using algorithms & statistical models through experience. Image recognition is one of the best examples of Machine learning that helps differentiate between multiple images, grouping them based on their categories such as color, location, etc.

Further, data science is the field of study that helps us extract useful data from structured and unstructured formatsdata format. Later, this extracted data is used to train machine learning models. Hence, we can say data science is the study of cleaning, preparing, and analyzing the data, whereas Machine learning is the subfield of data science. When we talk about a career in data science and Machine learning, then yes, both these technologies have a great future scope with tremendous jobs in the IT & software domain. Although various institutions and organizations offer so many certification courses, we have listed a few repudiated certification courses for ML and data science that will surely help you boost your career.

Best Machine learning and Data Science certifications

1. IBM Data Science Professional Certificate

One of the best IT companies, IBM, offers this course under different instructors. It helps you jump-start your career in data science and machine learning. Further, it helps you build data science skills, learn Python, SQL, and build ML models.

This course will help you to learn:

• Data science introduction, roles & responsibilities of data scientists, and methodology to think and work like a data scientist.
• Import and clean data sets, analyze and visualize data, and build and evaluate machine learning models and pipelines using Python.
• Theoretical and Real-time projects exposure based on tools, programming languages, libraries, etc.

Course offered in this certification:

• What is Data Science?
• Tools for Data Science
• Data Science Methodology
• Python for Data Science, AI & Development
• Python Project for Data Science
• Databases and SQL for Data Science with Python
• Data Analysis with Python
• Data Visualization with Python
• Machine Learning with Python
• Applied Data Science Capstone

Benefits of this certification:

• It provides various courses, lectures, and videos related to data science and ML. Further, along with Professional Certificate from Coursera, you'll also receive a digital Badge from IBM recognizing your proficiency in data science.
• This certification course is remotely available so that you can learn instantly on your schedule.
• This course is available with various subtitles such as English, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Turkish, Spanish, Persian, Korean.
• Hands-on projects that will help you to build a portfolio that showcases your job readiness to potential employers

Link for the course: Click here

2. Data Science and Machine Learning Developer Certification

Udemy sponsors this course under a team of the best ML and data science experts. This course helps you learn the powerful tools used in Data science and ML to solve real-world problems. This certification helps you gain in-depth knowledge and skills in data science, ML, and deep learning. It covers all concepts related to these technologies, requirements in projects, and practice sets to implement your skills in real-time scenarios. This course covers all fundamental concepts to solve complex problems with the help of lectures and interactive online labs. The training uses open-source tools - and helps you develop your judgment and intuition; to address actual business needs and real-world challenges.

Prerequisites of this certification:

• Basic understanding of Python coding
• Beginner level knowledge of mathematical concepts such as linear algebra but not mandatory

Modules of courses are as follows:

1. Introduction to ML
2. Exploring and Using data sets
3. Review of Machine Learning Algorithms
4. Machine Learning with Scikit
5. Deep Learning with Keras and TensorFlow
6. Building a Machine Learning Pipeline

Benefits of this course:

• You will be provided a certification of completion, which you can expose to your resume for job searching.
• You can access this course remotely from your mobile and your TV.

Who is this certification for?

• This is one of the best courses for anyone who wants to become a data scientist or machine learning engineer.
• This course gives you analytics skills so you can lead a team of analysts.
• Business analysts (BA) want to learn data science and ML techniques.
• Information architects who need expertise in machine learning algorithms
• Analytics professionals who work in machine learning or artificial intelligence
• Graduates are looking to build a career in data science and machine learning.

Link for the course: Click here

3. Data Science and Machine Learning: Making Data-Driven Decisions

This course is offered by one of the most popular learning platforms, "Great Learning," under the guidance of the best MIT faculty and mentorship from industry practitioners. This certification provides you with the skills and knowledge of ML and data science techniques that help make data-driven decisions. The curriculum of this course is of 12 weeks with 3 industry-relevant hands-on projects and 15+ case studies you can expose in your portfolio. This course helps you implement ML and data science concepts into real-world examples through practical applications and exposures. Further, this course covers various programming languages and tools such as Python, NumPy, Keras, TensorFlow, Scikit learns, Matplotlib, etc.

Week wise curriculum of the course:

• Weeks 1-2: Foundations of Data Science
• Week 3: Making Sense of Unstructured Data
• Week 4: Online MasterClass on Regression and Prediction
• Week 5: Regression and Prediction
• Week 6: Online MasterClass: Hands-on Machine Learning with Python
• Week 7: Classification and Hypothesis Testing
• Week 8: Deep Learning
• Week 9: Recommendation Systems
• Week 10: Online MasterClass: Hands-on Machine Learning with Python
• Week 11: Networking and Graphical Models
• Week 12: Predictive Analytics

Upon completing the 12th-week syllabus, you will be provided a certificate of completion from the Massachusetts Institute of Technology (MIT) IDSS. This certification will help you to get a job in leading IT companies. The format of certification is given in the below screenshot.

Who is this certification for?

• Data scientists, data analysts, and professionals wish to turn large volumes of data into actionable insights.
• Early career professionals and senior managers, including Technical managers, Business intelligence analysts, IT practitioners, Management consultants, and Business managers.
• Those with some academic/ professional training in applied mathematics/ statistics. Participants without this experience will have to put in extra work and be provided support by Great Learning.

Benefits of this certification:

• This course lets you learn from the best MIT faculty with recorded video lectures to build industry-valued skills.
• This course also provides the facility of weekend support from other mentors or experts in data science and ML.
• After completing this course, you will become entitled to a certificate of completion from the Massachusetts Institute of Technology (MIT) IDSS.
• This course gives you hands-on exposure to 3 projects and 15+ case studies.

4. Machine Learning with TensorFlow on Google Cloud Platform Specialization

Google Cloud offers this course, which aims to learn ML with Google cloud to solve complex real-world problems. This course is designed to help you understand the basic to advanced level machine learning concepts and neural networks use cases. Further, it focused on various supervised learning methods, generalizable solutions using gradient descent, and the creation of datasets for ML models. Hence, this course gives you practical exposure to end-to-end ML to solve different types of ML problems.

This specialization incorporates hands-on labs using Google's Qwiklabs platform, which you can showcase in your CV to find multiple jobs in leading organizations.

Upon completing this course and hands-on projects, you will be provided a certificate that you can share with prospective employers and your professional network.

This entire specialization is divided into 5 courses as follows:

• How Google does Machine Learning
• Launching into Machine Learning
• TensorFlow on Google Cloud
• Feature Engineering
• Art and Science of Machine Learning

Extra Benefits:

• This course gives you a Shareable Specialization and Course Certificate.
• Self-paced learning option with video lectures and notes.
• Practice quizzes and graded quizzes with feedback to boost your confidence in the ML industry.
• Graded programming assignments with experts feedback

Registration: Every 2 months on Coursera

Course Duration: 5 months

Mode of Teaching: Online

Prerequisites: Before starting this course, you must have a computer science & engineering background.

Link for the course: Click here

5. Harvard University Machine Learning

This course comprises the core concepts of machine learning algorithms, PCA, regularization techniques for a movies recommendation system, etc. Further, you will know about training/sample data and how to use it in the training process to predict accurate outputs for the future. Also, you will learn how to use a data set to discover potentially predictive relationships. Further, this course also helps you learn overfitting and techniques to avoid it, such as cross-validation.

When to register: You can register for this certification anytime through the edx website.

Course Fee: Available at no cost.

Course Duration: Almost 8 weeks

Mode of Learning: Online

Prerequisites: This course requires a basic understanding of Python programming language.

Key Benefits

Along with a globally valid certificate, you will also know various core areas of machine learning, such as:

• Introduction to basics of machine learning
• Knowledge of overtraining and how to avoid it using cross-validation concepts.
• Various popular machine learning algorithms and applications in real-world examples.
• How to build a recommendation system
• What are regularization techniques in machine learning, and why is it important?

6. eCornell Machine Learning Certificate

This certification course is specially designed to give you exposure to learning various ML algorithms and how to deploy them using Python. Using a combination of math and intuition, students learn to frame machine learning problems and construct a mental model to understand data scientists' approach to these problems programmatically. Various machine learning algorithms explore the implementation of concepts such as k-nearest neighbors, naive Bayes, regression trees, and others.

This program enables you to implement the live data algorithms and practice debugging and improving models with the help of SVM(support vector machines) and ensemble methods. Moreover, this course also offers you the internal working of neural networks and their construction and adoption of neural networks for different data types. This program uses Python and the NumPy library for code exercises and projects. Projects can be submitted and performed in Jupyter Notebooks.

Registration: throughout the year

Fee: $3,600 or $565/month

Course Duration: 3.5 months

Mode of Teaching: Online

Prerequisites: Python