929 Python courses delivered Online

A course by Sekhar Metla IT Industry Expert RequirementsNo programming experience needed. You will learn everything you need to knowNo software is required in advance of the course (all software used in the course is free)No pre-knowledge is required - you will learn from basic Audience Beginner JavaScript, Python and MSSQL developers curious about data science development Anyone who wants to generate new income streams Anyone who wants to build websites Anyone who wants to become financially independent Anyone who wants to start their own business or become freelance Anyone who wants to become a Full stack web developer Audience Beginner JavaScript, Python and MSSQL developers curious about data science development Anyone who wants to generate new income streams Anyone who wants to build websites Anyone who wants to become financially independent Anyone who wants to start their own business or become freelance Anyone who wants to become a Full stack web developer

This course will show you how to build Python-based web applications using Flask. You will cover the basics of the Flask framework and learn how to add functionality to your Flask applications using the popular extensions.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview This skills-focused combines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Building Recommendation Systems with Python (TTAI2360) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Building Recommendation Systems with Python (TTAI2360) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Computer Programming: Where Inspiration Meets Logic And Dreams Become Lines Of Code

Are you someone wishing to learn the skills to analyze data professionally using Power BI? Do you want to learn to create impressive reports? Then this is the right course for you! Learn the complete workflow in Power BI from A-to-Z and advanced analytics with DAX-calculated columns and measures, forecasting, and advanced analytics.

This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.

Welcome to this dual-phase course. In the first segment, we delve into neural networks and deep learning. In the second, ascend to mastering Generative Adversarial Networks (GANs). No programming experience required. Begin with the fundamentals and progress to an advanced level.

Learn core Apache Kafka features along with creating Java, Node.js and Python producers and consumers

Why Learn Sketchup and Stable Diffusion Rendering Course? Course Link SketchUp and Stable Diffusion Rendering Course. An AI image creation course designed to explore AI image creation techniques and master the use of advanced AI technology. You'll learn Ai 3D modeling, advanced rendering, and lighting techniques. Duration: 16 hrs. Method: 1-on-1 Online Over Zoom is also available. Schedule: Tailor your own schedule by pre-booking a convenient hour of your choice, available from Mon to Sat between 9 am and 7 pm. The Sketchup and Stable Diffusion Rendering Course equips students with comprehensive skills for visually stunning Ai (Artificial intelligence) 3D models and renderings. Master Sketchup's user-friendly interface, advanced rendering techniques, and stable diffusion rendering. Hands-on exercises and real-world projects enhance learning. Ideal for architecture, interior design, product development, and visualization careers. The Sketchup and Stable Diffusion Rendering Course equips students with comprehensive skills for visually stunning Ai (Artificial intelligence) 3D models and renderings. Master Sketchup's user-friendly interface, advanced rendering techniques, and stable diffusion rendering. Hands-on exercises and real-world projects enhance learning. Ideal for architecture, interior design, product development, and visualization careers. Sketchup and Stable Diffusion Rendering Course (16 hours) Module 1: Introduction to Sketchup (2 hours) Overview of Sketchup software and interface navigation Basic drawing tools and geometry creation techniques Module 2: Texturing and Materials (2 hours) Applying textures and customizing materials Exploring texture mapping and material libraries Module 3: Lighting and Shadows (2 hours) Understanding lighting principles and light placement Creating realistic shadows and reflections Module 4: Advanced Modeling Techniques (3 hours) Creating complex shapes and utilizing advanced tools Working with groups, components, and modifiers Module 5: Stable Diffusion Rendering (2 hours) Introduction to stable diffusion rendering Configuring rendering settings for optimal results Module 6: Scene Composition and Camera Setup (2 hours) Exploring composition principles and camera perspectives Managing scenes and creating walkthrough animations Module 7: Rendering Optimization (2 hours) Optimizing models for faster rendering Using render passes and post-processing techniques Module 8: Project Work and Portfolio Development (1 hour) Applying skills to complete a real-world project Showcasing work in a professional portfolio Optional: Installing Stable Diffusion and Python (Additional 10 hours) Module 1: Introduction to Stable Diffusion and Python Overview of Stable Diffusion and Python's significance Module 2: System Requirements Hardware and software prerequisites for installation Module 3: Installing Python Step-by-step installation process for different OS Module 4: Configuring Python Environment Setting up environment variables and package managers Module 5: Installing Stable Diffusion Downloading and installing the Stable Diffusion package Module 6: Setting Up Development Environment Configuring IDEs for Python and Stable Diffusion Module 7: Troubleshooting and Common Issues Identifying and resolving common installation errors Module 8: Best Practices and Recommendations Managing Python and Stable Diffusion installations Module 9: Practical Examples and Projects Hands-on exercises demonstrating usage of Stable Diffusion and Python Module 10: Advanced Topics (Optional) Exploring advanced features and techniques Stable Diffusion UI v2 | A simple 1-click way to install and use https://stable-diffusion-ui.github.io A simple 1-click way to install and use Stable Diffusion on your own computer. ... Get started by downloading the software and running the simple installer. Learning Outcomes: Upon completing the Sketchup and Stable Diffusion Rendering Course, with a focus on AI image rendering, participants will: Master AI Image Rendering: Gain expertise in using AI-powered rendering techniques to create realistic and high-quality visualizations. Utilize Sketchup for 3D Modeling: Navigate the software, proficiently use drawing tools, and create detailed 3D models. Optimize Renderings: Apply AI-based rendering to optimize model visuals, achieving faster rendering times and superior image quality. Implement AI-driven Lighting and Shadows: Utilize AI algorithms for lighting placement, shadows, and reflections, enhancing realism in renderings. Create Professional Portfolio: Showcase AI-rendered projects in a professional portfolio, highlighting advanced image rendering skills. Note: The course focuses on AI image rendering using Sketchup and Stable Diffusion techniques, empowering participants with cutting-edge skills for creating exceptional visual representations.

Overview Data and visual analytics are emerging fields concerned with analysing, modelling, and visualizing complex high-dimensional data. It can be analysed and visualised with many languages like Python, R Programming and more. This course will help to attain the skills and give in-depth knowledge to the participant's enhanced way of modelling, analysing and visualizing techniques.  The course will highlight practical challenges including composite real-world data and will also comprise several practical studies