Looking for best online courses to learn during COVID-19, we’re highlighting here the top 10 online courses recomended by Coursera and other Top MOOC providers. Check below courses that will help you in enha ofncing your skill.
- Google IT Automation with Python by Google
- The Science of Well-Being by Yale
- IBM Data Science by IBM
- Machine Learning by Stanford
- Python for Everybody by University of Michigan
- Google IT Support by Google
- Deep Learning by deeplearning.ai
- Data Science by Johns Hopkins University
- Fundamentals of Engineering Exam Review by Georgia Tech
- Social Norms, Social Change I by University of Pennsylvania and UNICEF
- 1 Google IT Automation with Python Professional Certificate
- 2 The Science of Well-Being
- 3 IBM Data Science Professional Certificate
- 4 Machine Learning By Stanford | Andrew NG
- 5 Python for Everybody Specialization
- 6 Google IT Support Professional Certificate by Google
- 7 Deep Learning by deeplearning.ai
- 8 Data Science Specialization by Johns Hopkins University
- 9 Fundamentals of Engineering Exam Review by Georgia Tech
- 10 Social Norms, Social Change I by University of Pennsylvania and UNICEF
Google IT Automation with Python Professional Certificate
Learn in-demand skills like Python, Git, and IT automation to advance your career.
This new beginner-level, six-course certificate, developed by Google, is designed to provide IT professionals with in-demand skills — including Python, Git, and IT automation — that can help you advance your career.
Knowing how to write code to solve problems and automate solutions is a crucial skill for anybody in IT. Python, in particular, is now the most in-demand programming language by employers.
This program builds on your IT foundations to help you take your career to the next level. It’s designed to teach you how to program with Python and how to use Python to automate common system administration tasks. You’ll also learn to use Git and GitHub, troubleshoot and debug complex problems, and apply automation at scale by using configuration management and the Cloud.
- Automate tasks by writing Python scripts
- Use Git and GitHub for version control
- Manage IT resources at scale, both for physical machines and virtual machines in the cloud
- Analyze real-world IT problems and implement the appropriate strategies to solve those problems
The Science of Well-Being
In this course you will engage in a series of challenges designed to increase your own happiness and build more productive habits. As preparation for these tasks, Professor Laurie Santos reveals misconceptions about happiness, annoying features of the mind that lead us to think the way we do, and the research that can help us change. You will ultimately be prepared to successfully incorporate a specific wellness activity into your life.
THE SCIENCE OF WELL BEING WAS PRODUCED IN PART DUE TO THE GENEROUS FUNDING OF THE DAVID F. SWENSEN FUND FOR INNOVATION IN TEACHING.
USP – What you will learn from this course
-Why take this course?
Misconceptions About Happiness
-What do we think will make us happy?
Why Our Expectations are so Bad
-Why do we mispredict what makes us happy?
How Can We Overcome Our Biases
-How we counteract our annoying features of the mind?
Stuff that Really Makes Us Happy
-What can we do to improve our happiness?
Putting Strategies into Practice
-How can we intentionally put these strategies into practice and build healthier habits?
Start Your Final Rewirement Challenge
-What rewirement will you commit to for the next 4 weeks?
Continue Your Rewirement Challenge
-How can you rely on others to help you change your behaviors?
Continue Your Rewirement Challenge
-How can you design your environment to help you change your behaviors?
Submit Your Final Assignment
-What mindset can you have to appreciate your progress so far and continue your progress beyond the course?
About IBM Data Science Professional Certificate course
Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning.
This program consists of 9 courses providing you with latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets.Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.
It is a myth that to become a data scientist you need a Ph.D. This Professional Certificate is suitable for anyone who has some computer skills and a passion for self-learning. No prior computer science or programming knowledge is necessary. We start small, re-enforce applied learning, and build up to more complex topics.
Upon successfully completing these courses you will have done several hands-on assignments and built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in Data Science. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Data Science.
IBM Professional Certificate course Details
The courses in the Data Science Professional Certificate include:
- What is Data Science
- Tools for Data Science
- Data Science Methodology
- Python for Data Science
- Databases and SQL for Data Science
- Data Visualization with Python
- Data Analysis with Python
- Machine Learning with Python
- Applied Data Science Capstone
Each of the courses also gives you an opportunity to earn an IBM open badge, which allows you to build your digital resume and makes you discoverable (by opting in) to potential employers.
Machine Learning By Stanford | Andrew NG
Rating :- 4.9stars (129,337 ratings)|97%
About Machine Learning By Stanford | Andrew NG
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Artificial Neural Network
Machine Learning (ML) Algorithms
About Instructor – Andrew Ng
CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
Python for Everybody Specialization
Rating :- 4.8stars(168,247 ratings)
This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.
- Create your own applications for data retrieval and processing
- Describe the basics of the Structured Query Language (SQL) and database design
- Explain the basics of programming computers using Python
- Understand fundamental programming concepts such as data structures
Google IT Support Professional Certificate by Google
The launchpad to a career in IT. This program is designed to take beginner learners to job readiness in under six months.
This 5-course certificate, developed by Google, includes innovative curriculum designed to prepare you for an entry-level role in IT support. A job in IT can mean in-person or remote help desk work in a small business or at a global company like Google. The program is part of Grow with Google, a Google initiative to help create economic opportunities for all Americans. Learn more.
Upon completion of the certificate, you can share your information with top employers like Cognizant, GE Digital, Hulu, Infosys, Intel, KForce, MCPc, PNC Bank, RICOH USA, Sprint, TEKSystems, Veterans United Home Loans, Walmart and their subsidiaries, and of course, Google. You can also earn a CompTIA and Google dual credential when you complete the Google certificate and pass the CompTIA A+ certification exams.
Through a mix of video lectures, quizzes, and hands-on labs and widgets, the program will introduce you to troubleshooting, customer service, networking, operating systems, system administration and security. You’ll hear from Googlers with unique backgrounds whose own foundation in IT support served as a jumping off point for their careers. By dedicating ~5 hours a week, you can complete in under six months.
Deep Learning by deeplearning.ai
Deep Learning Specialization. Master Deep Learning, and Break into AI
Rating :- 4.8stars(200,672 ratings)
About Deep Learning Specialization Course
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
USP/Skills you gain
- Convolutional Neural Network
- Artificial Neural Network
- Deep Learning
Data Science Specialization by Johns Hopkins University
Launch Your Career in Data Science. A ten-course introduction to data science, developed and taught by leading professors.
Rating :- 4.5stars(66,297 ratings)
About Data Science Specialization Course
This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
USP / What will you learn
- Use R to clean, analyze, and visualize data.
- Navigate the entire data science pipeline from data acquisition to publication.
- Use GitHub to manage data science projects.
- Perform regression analysis, least squares and inference using regression models.
Fundamentals of Engineering Exam Review by Georgia Tech
Rating :- 4.7stars(289 ratings)|94%
The purpose of this course is to review the material covered in the Fundamentals of Engineering (FE) exam to enable the student to pass it. It will be presented in modules corresponding to the FE topics, particularly those in Civil and Mechanical Engineering. Each module will review main concepts, illustrate them with examples, and provide extensive practice problems.
Social Norms, Social Change I by University of Pennsylvania and UNICEF
Rating :- 4.6stars(1,206 ratings)|98%
This is a course on social norms, the rules that glue societies together. It teaches how to diagnose social norms, and how to distinguish them from other social constructs, like customs or conventions. These distinctions are crucial for effective policy interventions aimed to create new, beneficial norms or eliminate harmful ones. The course teaches how to measure social norms and the expectations that support them, and how to decide whether they cause specific behaviors. The course is a joint Penn-UNICEF project, and it includes many examples of norms that sustain behaviors like child marriage, gender violence and sanitation practices.
This is Part 1 of the Social Norms, Social Change series. In these lectures, I introduce all the basic concepts and definitions, such as social expectations and conditional preferences, that help us distinguish between different types of social practices like customs, descriptive norms and social norms. Expectations and preferences can be measured, and these lectures explain how to measure them. Measurement is crucial to understanding the nature of the practice you are facing, as well as whether an intervention was or was not successful, and why. In Part 2, we will put into practice all we have learned in Part 1.
USP/SKILLS YOU WILL GAIN
- Social Psychology
- Research Methods
- Qualitative Research