There are lot of Machine Learning tutorials present online however, we always face one issue (the one I faced:P) that is the order of tutorial. And I believe that "Learning without a vision is a waste". Because there are so many things to learn in this world and if we gather info & learn anything without a goal, we will be going to waste our time and eventually forget what we learnt. Everyone has different opinion but this is what I believe. Hence, I have curated a list of free Machine Learning Ebooks from different sources.

New Delhi, September 10, 2020: As part of its ongoing efforts to promote skilling as a national priority, NASSCOM FutureSkills and Microsoft have joined hands to launch a nation-wide AI skilling initiative. The initiative aims to skill 1 million students in AI by 2021. The collaboration will provide Microsoft's AI, machine learning and data science expertise to students through easy to consume modules including live demos, hands on workshops and assignments. These introductory sessions on AI will be available for undergraduate students at no cost and will cover the basics of data science, machine learning models on Azure, and understanding of cognitive services to build intelligent solutions. The partnership with NASSCOM FutureSkills is an extension of Microsoft's global skilling initiative to help 25 million people worldwide acquire new digital skills, needed to thrive in a digital economy.

How is AI Ethics and Responsible AI currently being taught in Computer Science and Engineering Curriculums across Africa? What issues related to this topic are relevant to students and faculty? And what roadblocks or challenges are instructors facing to bring more discussion of AI ethics to classrooms? The goal of this workshop is to foster a discussion on how to effectively integrate AI Ethics into Computer Science/Engineering programs at African Universities. This is an initial step to gather perspectives on the current situation at representative universities in different countries in Africa, and to initiate a discussion on how we can better support each other with lessons learned and share materials/curriculums to further develop AI ethics programs in higher education. After identifying the current state, the interests of students and faculty and the needs of departments in this workshop session, the goal is to continue the series with more in-depth workshops on specific topics.

Levels 1 - 5 Bonus Content The Complete Photoshop Masterclass Python for All Ages and Absolute Beginners Kids Coding 2.0 - Introduction to HTML, CSS and JavaScript Ethical Hacking from Scratch Hello Coding 2020: Anyone Can Learn to Code Amazon Web Services Certified Solutions Architect 2020 Code and Get Hired - Complete 2020 Learn to Code Guide The Complete AWS Machine Learning Course.

The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field. The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.

So you're new to coding, and you're not quite sure whether Python is the right programming language for you to learn? If that sounds familiar, you're in the right place. In this article, I will walk you through the most significant advantages of Python compared to other popular programming languages. You will learn why Python can be an excellent tool to add under your belt. We won't just focus on the lucrative career opportunities Python can offer. We'll also look at things that affect your learning experience as a beginner.

Conversational AI is revolutionizing how businesses operate in every industry with applications like virtual agents, chatbots and assistants. Creating an intelligent and intuitive app involves quickly adapting new state-of-the-art research and deploying it in production. You can learn about the latest advancements in this area at the GPU Technology Conference, taking place October 5-9. At GTC, researchers and developers from leading institutions across the globe will share new techniques and innovations in speech recognition, natural language processing and text-to-speech technologies. At GTC, the NVIDIA Deep Learning Institute is offering instructor-led, hands-on training on how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents.

Convolutional Neural Networks (CNNs) are considered as game-changers in the field of computer vision, particularly after AlexNet in 2012. And the good news is CNNs are not restricted to images only. They are everywhere now, ranging from audio processing to more advanced reinforcement learning (i.e., Resnets in AlphaZero). So, the understanding of CNNs becomes almost inevitable in all the fields of Data Science. Even most of the Recurrent Neural Networks rely on CNNs these days.

The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world! This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing). You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself. All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow.

Under the project, students will learn a customised version -- unique to the project -- of the popular programming language Scratch. They will also use MIT App Inventor that runs on Android devices and work on developing a robot kit in conjunction with Chromebooks. Students will study CT (computational thinking), the cornerstone computer science discipline that renders complex problems simple so that humans and computers can understand the possible solutions. This includes'pattern recognition', looking for similarities among data, and writing algorithms -- a step-by-step guide to solving a problem or task. Pupils will also learn skills such as'debugging' to identify and remove software or hardware errors and practises like'tinkering' to innovate and build on experimental models or prototypes.