Suchitra is a professor by profession and learner by passion. She hold a PhD degree in Electronics and Communication Engineering with core competency in computer vision, pattern recognition, Artificial Intelligence,machine learning and deep learning. She is passionate about data science, Artificial Intelligence, natural language processing and firmly believes that future is Artificial Intelligence.

Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions. The main purpose of this course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning and big data.

Attribution for the core content is given to the textbook "Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data" which I would urge you to buy on Amazon

Deep learning is not like any other technology, but it is in many cases the only technology that can solve certain problems. We need to ensure that all people involved in the project have a common understanding of what is required, how the process works, and that we have a realistic view of what is possible with the tools at hand. In order to define AI, we must first define the concept of intelligence in general. Intelligence can be generally described as the ability to perceive information and retain it as knowledge to be applied towards adaptive behaviors within an environment or context. While there are many different definitions of intelligence, they all essentially involve learning, understanding, and the application of the knowledge learned to achieve one or more goals.

The field of CPS draws from several areas in computer science, electrical engineering, and other engineering disciplines, including computer architecture, embedded systems, programming languages, software engineering, real-time systems, operating systems and networking, formal methods, algorithms, computation theory, control theory, signal processing, robotics, sensors and actuators, and computer security. Similarly, over the past 14 years, we have had students from computer science, electrical and computer engineering, mechanical engineering, civil engineering, and even bioengineering. Integrating this bewildering diversity of subject areas into a coherent whole for students with such a wide breadth of backgrounds has been a challenge we had to overcome. One approach would have been to not attempt such an integration. Instead, we could have opted for a collection of courses that together cover all the key areas in CPS.

Automated essay scoring is an important application of machine learning and artificial intelligence to the field of psychometrics and assessment. In fact, it's been around far longer than "machine learning" and "artificial intelligence" have been buzzwords in the general public! The field of psychometrics has been doing such groundbreaking work for decades. So how does it work, and how can you apply it? The first and most critical thing to know is that there is not an algorithm that "reads" the student essays.

Meet Aswini Thota, an Analytics and Artificial Intelligence (AI) leader who solves organisational and business problems leveraging data. He always believed in the power of data and amased what insights we can grasp from it. Over the course of his career, Aswini has developed a skill set in analysing data and he hopes to use his experience and expertise in data science to help people discover the amazing career opportunities that lie ahead in the field of Data Science. He has effectively evolved from a machine learning researcher to an award-winning AI / Data science leader. Aswini holds two master's degrees in Electrical Engineering and Data Science.

Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate from IBM will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills and experience. It's a myth that to become a data scientist you need a Ph.D. Anyone with a passion for learning can take this Professional Certificate – no prior knowledge of computer science or programming languages required – and develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry level data scientist. The program consists of 9 online courses that will provide you with the latest job-ready tools and skills, including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You'll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets. Upon successfully completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science.

This is a complete Free course for statistics. In this course, you will learn how to estimate parameters of a population using sample statistics, hypothesis testing and confidence intervals, t-tests and ANOVA, correlation and regression, and chi-squared test. This course is taught by industry professionals and you will learn by doing various exercises.

As we navigate the roadmap drawn by COVID-19, we know there will continue to be accelerated digital transformation and rapid innovation of education intended to positively impact student outcomes in 2022. This will take many forms, from institutions evolving their operations to students optimizing their learning with technology to caregivers connecting directly with their children's education through edtech services. To support both new and existing solutions, while delivering engaging learning experiences, developing forward-thinking data strategies and building new revenue streams, edtechs are using Amazon Web Services, Inc. (AWS) the world's most comprehensive and broadly adopted cloud platform. Offering over 200 fully featured services from data centers globally, AWS helps these organizations lower costs, become more agile and innovate faster technology solutions that support students and educators every day. Early stage edtechs are also participating in the AWS EdStart program, the AWS edtech virtual startup accelerator, designed to help entrepreneurs build the next generation of online learning, analytics and campus management solutions on the AWS Cloud.