In this blog, I am going to share my journey about Kaggle, That How I become Kaggle NotebooksExpert in 4 weeks. I will share tips and a proper road map to achieve this goal even I will tell you how many hours I spent on Kaggle. There are three other categories but I worked only on Notebooks till now. Kaggle Ranking is based on the Kaggle progression system. So basically if your notebook had 5 non-invoice votes you will earn a bronze medal.

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What does this course offer you? Python is easy to learn. The syntax is simple and the code is very readable. With Python, you can write programs in fewer lines of code than with most other programming languages. The popularity of Python is growing rapidly.

Artificial Intelligence technology is used to create recommendation engines through which you can engage better with your customers. These recommendations are made in accordance with their browsing history, preference, and interests. It helps in improving your relationship with your customers and their loyalty towards your brand. Virtual shopping assistants and chatbots help improve the user experience while shopping online. Natural Language Processing is used to make the conversation sound as human and personal as possible.

Created by Dr. Bright (PhD Data Science) 123 hours on-demand video course The Full-Stack Data Scientist BootCamp is the ONLY course on Udemy that covers A to Z of lessons that will make you a Data Scientist. Created by Dr. Bright, a Ph.D. in Data Science holder, former Microsoft Senior Data Scientist, and a Visiting Faculty at Worcester Institute, this course covers everything that you need to know to become a Full Stack Data Scientist. The instructors and advisors of the course spent over 13 months creating and vetting the course to make sure it meets the industry and academic standards. With 100 hours of quality course curriculum, this course is the same as we use for our 18 months MS in Data Science program on campus and even more exciting are the Projects in the course to make you more efficient and confident in building Data Science and Artificial Intelligence (AI) products. The motivation is to bring Quality Data Science education to every serious learner at affordable cost.

Introductory Python, to more advanced concepts like Object Oriented Programming, decorators, generators, and even specialized libraries like Numpy & Matplotlib Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle. Linear Regression, Logistic Regression and Neural Networks built from scratch. TensorFlow installation, Basics and training neural networks with TensorFlow 2. Convolutional Neural Networks, Modern ConvNets, training object recognition models with TensorFlow 2. Breast Cancer detection, people counting, object detection with yolo and image segmentation Generative Adversarial neural networks from scratch and image generation Recurrent Neural Networks, Modern RNNs, training sentiment analysis models with TensorFlow 2. Neural Machine Translation, Question Answering, Image Captioning, Sentiment Analysis, Speech recognition Deploying a Deep Learning Model with Google Cloud Function. Linear Regression, Logistic Regression and Neural Networks built from scratch. Convolutional Neural Networks, Modern ConvNets, training object recognition models with TensorFlow 2. In this course, we shall look at core Deep Learning concepts and apply our knowledge to solve real world problems in Computer Vision and Natural Language Processing using the Python Programming Language and TensorFlow 2. We shall explain core Machine Learning topics like Linear Regression, Logistic Regression, Multi-class classification and Neural Networks.

Despite most companies recognizing that AI is the next big innovation to hit their industries, one report finds that 63% of enterprises that have adopted AI are just scratching the surface of their capabilities. What are you going to do to fill the skills gap and find yourself a lucrative career in AI? The answer is simple: Grab The Machine Learning Mastery eBook Bundle while it's on sale for just $19.99. This bundle includes ten eBooks from Packt Publishing, one of the leading online libraries of purposeful ebooks designed to teach you new skills. These comprehensive volumes will guide you from the basic to advanced level of machine learning, covering important concepts, algorithms and applications to help you break into the field.

Get A Solid Understanding of C# & Basic Programming Concepts Learn Unity's API from Absolute Basics Learn Object Oriented Programming Concepts Learn C# Scripting With Practical Examples in Unity Use C# Skills for Building Mobile / Android Games Implement Basic AI Features in Unity Learn Version Controlling with Github, Bitbucket & SourceTree Build Real 2D & 3D Example Games With C# & Unity This Course will Teach You everything that you need to get started with C# scripting in Unity. You will learn step by step from scratch every feature of the C# language as well as how to implement it in Unity's API for building Games. All The Content works fine in Unity 2021 . I have taught C# Scripting to thousands of people on my Youtube Channel: Charger Games. I love teaching complex concepts in a simple way, so even if you have no previous coding experience, no need to worry, I'm gonna teach you everything step by step in the perfect order.

Traversing intersections is a challenging problem for autonomous vehicles, especially when the intersections do not have traffic control. Recently deep reinforcement learning has received massive attention due to its success in dealing with autonomous driving tasks. In this work, we address the problem of traversing unsignalized intersections using a novel curriculum for deep reinforcement learning. The proposed curriculum leads to: 1) A faster training process for the reinforcement learning agent, and 2) Better performance compared to an agent trained without curriculum. Our main contribution is two-fold: 1) Presenting a unique curriculum for training deep reinforcement learning agents, and 2) showing the application of the proposed curriculum for the unsignalized intersection traversal task. The framework expects processed observations of the surroundings from the perception system of the autonomous vehicle. We test our method in the CommonRoad motion planning simulator on T-intersections and four-way intersections.

Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous training activities could overload resource-constrained devices. In this work, we propose a smart multi-tenant FL system, MuFL, to effectively coordinate and execute simultaneous training activities. We first formalize the problem of multi-tenant FL, define multi-tenant FL scenarios, and introduce a vanilla multi-tenant FL system that trains activities sequentially to form baselines. Then, we propose two approaches to optimize multi-tenant FL: 1) activity consolidation merges training activities into one activity with a multi-task architecture; 2) after training it for rounds, activity splitting divides it into groups by employing affinities among activities such that activities within a group have better synergy. Extensive experiments demonstrate that MuFL outperforms other methods while consuming 40% less energy. We hope this work will inspire the community to further study and optimize multi-tenant FL.