AI LAW, ETHICS, PRIVACY & LEGALITIES - DR. PAVAN DUGGAL -CLU AN INTRODUCTION TO THE WONDERFUL WORLD OF DIFFERENT TOPICS UNDER ARTIFICIAL INTELLIGENCE LAW What you'll learn Description This course provides a holistic perspective of some of the important issues and topics that are gaining significance in the evolving Artificial Intelligence Law discipline. This course further tries to highlight the directions in which Artificial Intelligence Law as an emerging discipline is likely to evolve, with the passage of time. Who this course is for: Any student of any age group, who is interested in knowing about the complex legalities as also legal, policy and regulatory issues concerning Artificial Intelligence.
Machine Learning Interview Questions A Machine Learning interview calls for a rigorous interview process where the candidates are judged on various aspects such as technical and programming skills, knowledge of methods and clarity of basic concepts. If you aspire to apply for machine learning jobs, it is crucial to know what kind of interview questions generally recruiters and hiring managers may ask. Description Is this course for me? By taking this course, you will gain the tools you need to continue improving yourself in the field of app development. You will be able to apply what you learned to further experience in making your own apps able to perform more.
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.
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.
In this section, we will introduce the deep learning framework we'll be using through this course, which is PyTorch. We will show you how to install it, how it works and why it's special, and then we will code some PyTorch tensors and show you some operations on tensors, as well as show you Autograd in code!
You must have heard/read people telling you that if you want to be a rockstar Data Science or Machine Learning Engineer then have a good grip on mathematical concepts like Linear Algebra, Calculus, Probability, etc. Mathematics is necessary to build products or conduct academic research in machine learning. Maths and Programming are highly intertwined in machine learning workflows. Often, the code is built directly from mathematical intuition and shares the syntax of mathematical notation. In this article, I'm going to cover the two best courses from the top university in the world, that you can take to build a solid foundation in calculus. Single Variable Calculus is a first-year, first-semester course at MIT.
Time series analysis and forecasting is one of the key fields in statistical programming. Due to modern technology the amount of available data grows substantially from day to day. They also know that decisions based on data gained in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career!
NLP - Natural Language Processing with Python Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing Bestseller What you'll learn Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python. We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files. Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.