For a Self-reliant India, to fulfil demand of highly skilled Artificial Intelligence and Cyber Security professionals in the country, Defence Institute of Advanced Technology, DIAT, Pune is conducting the nationwide Online Training and Certification Courses (OTCC) in Cyber Security, Artificial Intelligence & Machine Learning(AI & ML). The School of Computer Engineering and Mathematical Sciences of DIAT has completed two batches of these courses and more than 1600 candidates are successfully trained and certified. The 3rd batch of AI & ML course is on-going. Now DIAT is launching next batches of 16-weeks Online Course on Cyber Security, and 12-weeks Online Course on Artificial Intelligence & Machine Learning (AI & ML)in December 2022. The Graduating students, professionals, or any graduate person can apply for these courses.

At the end of each week, you'll reflect on your learning and plot. Next steps to apply what you've learned in your professional practice. This is an important part of the course that we hope you'll use as a roadmap to better manage your team's data science projects. Now let's talk about what this course is all about. The aim of the course is to equip executives with the knowledge that will enable them to work productively with data scientists.

Online learning platforms provide learning materials and answers to students' academic questions by experts, peers, or systems. This paper explores question-type identification as a step in content understanding for an online learning platform. The aim of the question-type identifier is to categorize question types based on their structure and complexity, using the question text, subject, and structural features. We have defined twelve question-type classes, including Multiple-Choice Question (MCQ), essay, and others. We have compiled an internal dataset of students' questions and used a combination of weak-supervision techniques and manual annotation. We then trained a BERT-based ensemble model on this dataset and evaluated this model on a separate human-labeled test set. Our experiments yielded an F1-score of 0.94 for MCQ binary classification and promising results for 12-class multilabel classification. We deployed the model in our online learning platform as a crucial enabler for content understanding to enhance the student learning experience.

Andrew Ng is one of the biggest names in Artificial Intelligence and Machine Learning, after team-founding and -leading stints at Google Brain, Baidu, and elsewhere, and as founder of Coursera and Landing AI. His online courses have attracted millions of views. AI has huge potential outside of consumer software and internet apps, he believes. I think the biggest potential of AI still lies ahead of us, to use it for all the other industries other than just consumer software and internet. But candidly, when I walk around everywhere from factories to hospitals, they just seek mentors.

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course?

This specialization is intended to familiarize learners with a broad range of financial technologies. While finance has always been at the forefront of technological innovation, the financial industry is changing rapidly in the face of new technology. In the past, at the forefront of innovation in finance were central governments and financial institutions. Today, information technology firms and professionals are leading innovation in the financial industry. Our goal is to show learners the genesis and use cases of the technology.

Would you like to take advantage of the best online courses for accelerating your career, taught by qualified professionals with job assistance? Well, you've come to the right place! First, I am starting discussion about Clinical SAS and then one by one will cover all. If you are among those who in 2023 have decided to face the challenge of presenting yourself to some oppositions of the Health Care, Clinical Research or Pharmaceutical organization this Clinical SAS knowledge can help you. Statistical Analysis System or SAS is mainly a statistical software that is used for Business analytical purpose, Data management, and in Predictive analysis also.

In this paper, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots embedded with fiber Bragg grating (FBG) sensors. Developments of 3-D shape perception and control technologies are crucial for continuum robots to perform the tasks autonomously in surgical interventions. However, owing to the nonlinear properties of continuum robots, one main difficulty lies in the modeling of them, especially for soft robots with variable stiffness. To address this problem, we propose a versatile learning-based adaptive controller by leveraging FBG shape feedback that can online estimate the unknown model of continuum robot against unexpected disturbances and exhibit an adaptive behavior to the unmodeled system without priori data exploration. Based on a new composite adaptation algorithm, the asymptotic convergences of the closed-loop system with learning parameters have been proven by Lyapunov theory. To validate the proposed method, we present a comprehensive experimental study by using two continuum robots both integrated with multi-core FBGs, including a robotic-assisted colonoscope and multi-section extensible soft manipulators. The results demonstrate the feasibility, adaptability, and superiority of our controller in various unstructured environments as well as phantom experiments.

Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this course, you will learn how to use Azure Machine Learning to create and publish models without writing code. This is the second course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam.

This course is all about data and how it is critical to the success of your applied machine learning model. Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).