Python Programming for Beginners and Kids - Anyone Can Code

You’ve built a secure, tested, and scalable API. Now, it's time to transform it into a complete product. Part 3 focuses on the high-value features that modern users expect—real-time collaboration, secure payments. This is the course where your project leaves the local machine and becomes a revenue-generating asset available globally.The Modern Backend ToolkitThis final installment covers the three most challenging and high-demand aspects of modern development:Real-Time Capabilities: How do applications collaborate instantly, like a live chat or document editor?Solution: You will master WebSockets and use the NestJS Gateway to enable live, bi-directional communication across your application.Monetization Payments: How do you securely handle financial transactions?Solution: You will integrate Stripe and Paypal, the industry-standard payment processor, to handle subscriptions and checkout flows, turning your API into an e-commerce ready platform.What You Will Achieve—The Global LaunchBy the end of Part 3, you won't just have a portfolio project; you'll have a fully deployed, high-value, multi-service application:Live Collaboration: Implement real-time updates for your team projects, user status, and notifications using WebSockets.Video Call: You will learn how to do video-based collaboration using WebRTCSecure Payment Integration: Develop a full payment module using Stripe, PayPal to manage user subscriptions, checkout sessions, and webhook processing.This course closes the loop. It turns theory into practice, code into

This is a niche topic for students interested in data science and machine learning fields. The classical data imbalance problem is recognized as one of the major problems in the field of data mining and machine learning. Imbalanced learning focuses on how an intelligent system can learn when it is provided with unbalanced data.There is an unprecedented amount of data available. This has caused knowledge discovery to garner attention in recent years. However, many real-world datasets are imbalanced. Learning from unbalanced data poses major challenges and is recognized as needing significant attention. The problem with unbalanced data is the performance of learning algorithms in the presence of underrepresented data and severely skewed class distributions. Models trained on imbalanced datasets strongly favor the majority class and largely ignore the minority class. Several approaches introduced to date present both data-based and algorithmic solutions. The specific goals of this course are: Help the students understand the underline causes of unbalanced data problem. Go over the major state-of-the-art methods and techniques that you can use to deal with imbalanced learning. Explain the advantages and drawback of different approaches and methods .Discuss the major assessment metrics for imbalanced learning to help you correctly evaluate the effectiveness of your solution.

This course covers the fundamentals necessary for a state-of-the-art GAN. Anyone who experimented with GANs on their own knows that it's easy to throw together a GAN that spits out MNIST digits, but it's another level of difficulty entirely to produce photorealistic images at a resolution higher than a thumbnail.This course comprehensively bridges the gap between MNIST digits and high-definition faces. You'll create and train a GAN that can be used in real-world applications.And because training high-resolution networks of any kind is computationally expensively, you'll also learn how to distribute your training across multiple GPUs or TPUs. Then for training, we'll leverage Google's TPU hardware for free in Google Colab. This allows students to train generators up to 512x512 resolution with no hardware costs at all.The material for this course was pulled from the ProGAN, StyleGAN, and StyleGAN 2 papers which have produced ground-breaking and awe-inspiring results. We'll even use the same Flicker Faces HD dataset to replicate their results.Finally, what GAN course would be complete without having some fun with the generator? Students will learn not only how to generate an infinite quantity of unique images, but also how to filter them to the highest-quality images by using a perceptual path length filter. You'll even learn how to generate smooth interpolations between two generated images, which make for some really interesting visuals.

Note: though much of the course was recorded with an older version of Unity, the final project has been updated to use Unity 2019.Though there are minor differences in the UI, you should be able to complete the course using Unity 2019.We have created several advanced features to make this the most complete course on designing a Match-Three course in Unity:re-skinning your game using your own Spriteschecking for the deadlock condition (no more available moves)shuffling the Board pieces (instead of deleting them and re-filling the Board)building a one-, two-, or three-star score metercreating new level objectives (scored, timed, and collected) This course now weighs in at a 16+ hour marathon in game development. Save weeks of work on building your own match three game. Join the ever growing ranks of our class and see what you can build today!In this course, we will:start with a blank Unity project and flesh out a fully working game levellearn some fundamental techniques for creating your own tile-based match-three puzzle gamefollow several C# scripts line-by-line and learn how to organize our scripts logically and cleanlyWhat is covered:Setup our project settings and environmentCreate a Board class to store our tiles and game piecesCreate an auto-centered orthographic cameraMake a GamePiece class that can move with interpolationSetup the tiles to receive mouse inputSearch for matches in a two-dimensional arrayHandle recursive calls to clear and refill our Board Add sound effects to game eventsApply particle effects to add s

"Python for Data Science: Complete Masterclass" is a comprehensive online course designed to provide you with a deep understanding of Python and its applications in data science. This course is suitable for beginners as well as advanced learners who want to enhance their knowledge and skills in Python programming for data science.Throughout the course, you will learn about the fundamental concepts of Python programming language, such as variables, data types, loops, functions, and modules. You will also learn how to use libraries and frameworks, such as NumPy, Pandas, matplotlib, and Scikit-Learn to work with data.The course covers a range of topics related to data science, including data manipulation, data analysis, data visualization, and machine learning. You will learn how to clean, preprocess, and manipulate data using Python libraries like Pandas, and how to analyze and visualize data using tools like Matplotlib and Seaborn. You will also learn how to build machine learning models using Scikit-Learn, including regression, classification, clustering, and dimensionality reduction.By the end of the course, you will have a strong understanding of Python programming language and its applications in data science. You will have gained hands-on experience working with real-world datasets, and you will be able to use Python for data analysis, visualization, and machine-learning tasks.In addition to the topics mentioned above, the "Python for Data Science: Complete Masterclass" course also covers other important data science concepts, such as data preprocessing, exploratory data analysis, hypothesis testing, and data modeling.You will learn how to preprocess data, including handling missing values, encoding categorical variables, and scaling numerical data. You will also learn how to perform exploratory data analysis to gain insights into the data and identify patterns and trends.Furthermore, the course covers hypothesis testing and statistical inference, including t-tests

Hello,Getting here means you want to learn programming. You've come to the right place!Why programming?Because you will earn well, the employment rate in the field is almost 100% or maybe just for fun.We want to help you build a real experience in the field, not just learn a programming language.When you are just starting out in IT and want to get a job, the most important thing is to prove to your employer that you are capable of producing for him. In the end, it all comes down to money.If you bring added value to the employer, everything is fine, and our full beginner course can help you do just that.To take this course you need the following:-analytical thinking;-a laptop - in class you will work on your personal laptop;-a lot of willpower and patience - it will take a lot of work and it is good to be aware that in order to succeed in this field you must also invest time. You will come across many abstract concepts and you will need time to assimilate them. But don't worry - Everything is explained from A to Z.If you have any questions, feel free to contact us!