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Step confidently into a rewarding UK career with our transformative "Python Bootcamp: JavaScript, HTML & CSS Coding" bundle. This bundle covers everything from honing your Python proficiency to crafting interactive web designs with HTML and CSS and developing dynamic applications using JavaScript. Uncover the secrets of Python's application in data science, machine learning, and neural networks. Establish a strong foundation in C# coding and exploring networking, GUI development, XML handling, and more. Delve into eight immersive CPD Accredited courses, each a standalone course: Course 01: The Complete Python 3 Course: Beginner to Advanced! Course 02: JavaScript for Everyone Course 03: HTML and CSS Coding: Beginner to Advanced Course 04: Basic C# Coding Course 05: Python Data Science with Numpy, Pandas and Matplotlib Course 06: Deep Learning & Neural Networks Python - Keras Course 07: Complete Python Machine Learning & Data Science Fundamentals Course 08: Python Programming Bible | Networking, GUI, Email, XML, CGI Our comprehensive Python Bootcamp: JavaScript, HTML & CSS Coding bundle ensure you're well-prepared for diverse tech challenges. Elevate your UK career prospects with hands-on learning and real-world applications. Embrace the "Python Bootcamp: JavaScript, HTML & CSS Coding" bundle for an all-encompassing skill set bound to make you a standout in the dynamic UK job landscape. Learning Outcomes Progress from beginner to advanced Python proficiency. Craft dynamic websites using HTML and CSS. Develop user-friendly web applications with JavaScript. Analyze data using Numpy, Pandas, and Matplotlib. Explore neural networks with Python's Keras. Gain practical machine learning expertise. Unleash your potential with our comprehensive Python Bootcamp: JavaScript, HTML & CSS Coding bundle that promises to transform your career trajectory. In the rapidly evolving tech landscape, Python proficiency stands as a cornerstone of success. From web development to data analysis and machine learning, Python is the language that opens doors to countless opportunities. Our Python Bootcamp: JavaScript, HTML & CSS Coding bundle ensures you learn and master Python, securing your place at the forefront of innovation. Harness the expertise of excellent instructors who unravel the complexities of Python in a relatable and engaging manner. Dive into dynamic web design, crafting seamless interfaces with HTML and CSS, and building interactive web applications using JavaScript.Take the plunge into deep learning, understanding the intricacies of neural networks through Python's Keras library. As you progress, embrace the fundamental principles of machine learning, propelling your career to new heights. The time to seize the moment is now. The "Python Bootcamp: JavaScript, HTML & CSS Coding" bundle equips you with indispensable skills, empowering you to stand out in a competitive job market. Whether a newcomer or a seasoned professional, this Python Bootcamp: JavaScript, HTML & CSS Coding bundle ensures you're ready to conquer challenges and grasp opportunities. Embrace the power of Python today, and let it shape a future of endless possibilities. CPD 80 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Beginners to advanced learners. HTML, CSS, and JavaScript skills. Master Python programming. Numpy, Pandas, and data science. Diverse tech skill acquisition. Deep learning and machine learning Requirements Without any formal requirements, you can delightfully enrol in this course. Career path Web Developer: £25,000 - £50,000 Python Programmer: £30,000 - £60,000 Data Analyst: £25,000 - £45,000 Front-End Developer: £25,000 - £50,000 UI/UX Designer: £30,000 - £50,000 Machine Learning Engineer: £40,000 - £70,000 Full-Stack Developer: £35,000 - £60,000 Certificates CPD Certificate Of Completion Digital certificate - Included 8 Digital Certificates Are Included With This Bundle CPD Quality Standard Hardcopy Certificate (FREE UK Delivery) Hard copy certificate - £9.99 Hardcopy Transcript - £9.99

Thinking about learning about Knowledge-Based Systems? The BCS Foundation Certificate in Artificial Intelligence teaches learners to recognise Knowledge-Based Systems (such as chat bots), gain an understanding of how they work, and consider how they can add value to an organisation. This award will also enable candidates to understand the concept of Uncertainty and Fuzzy Logic, and how Knowledge-Based Systems can be used to help organisations to make decisions and act where there is higher level of uncertainty. You will learn an understanding of Knowledge-Based Systems and their role within AI, the use of rules within a Knowledge-Based System, an understanding of the principles of case-based reasoning, an understanding of uncertainty and the use of fuzzy logic and an understanding of the role of the inference engine.

This is a comprehensive and practical Apache Spark course. In this course, you will learn and master the art of framing data analysis problems as Spark problems through 20+ hands-on examples, and then scale them up to run on cloud computing services. Explore Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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.

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.

Unleashing the Power of Deep Learning: Mastering Neural Network with R Dive into the fascinating realm of artificial intelligence with our course, 'Deep Learning Neural Network with R.' Imagine a world where machines learn and make decisions, mimicking the intricacies of the human brain. This course is your gateway to unlocking the secrets of deep learning, focusing on neural networks implemented using the versatile R programming language. Immerse yourself in hands-on projects, from creating single-layer neural networks for agriculture analysis to mastering multi-layer neural networks for predicting deaths in wars. The journey begins with reviewing datasets and creating dataframes, leading you through running neural network code and generating insightful output plots. Join us in this captivating exploration, where coding meets creativity, and algorithms come to life. Learning Outcomes Master the fundamentals of single-layer neural networks, gaining the skills to analyze agricultural datasets effectively. Acquire proficiency in implementing multi-layer neural networks, specifically tailored for predicting outcomes in complex scenarios like deaths in wars. Develop hands-on experience in creating and manipulating dataframes for enhanced data analysis. Gain a deep understanding of neural network syntax, commands, and code execution in the R programming language. Hone your ability to generate meaningful output plots, transforming raw data into visually compelling insights. Why choose this Deep Learning Neural Network with R course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Deep Learning Neural Network with R course for? Aspiring data scientists and analysts eager to delve into the world of deep learning. R programming enthusiasts looking to enhance their skills with practical applications. Students and professionals in computer science, statistics, or related fields. Individuals seeking to understand the implementation of neural networks in real-world scenarios. Anyone fascinated by the intersection of coding, data analysis, and artificial intelligence. Career path Machine Learning Engineer: £40,000 - £70,000 Data Scientist: £35,000 - £60,000 Artificial Intelligence Researcher: £45,000 - £80,000 Research Scientist (Machine Learning): £50,000 - £90,000 Data Analyst (AI/ML): £30,000 - £55,000 Senior AI Developer: £60,000 - £100,000 Prerequisites This Deep Learning Neural Network with R does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Deep Learning Neural Network with R was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Section 01: Single Layer Neural Networks Project - Agriculture (Part - 1) Reviewing Dataset 00:14:00 Creating Dataframes 00:09:00 Generating Output 00:12:00 Section 02: Single Layer Neural Networks Project - Agriculture (Part - 2) Running Neural Network Code 00:11:00 Importing Dataset 00:09:00 Neural Network Plots for Hidden Layer 1 00:08:00 Section 03: Multi-Layer Neural Networks Project - Deaths in wars (Part - 1) Syntax and Commands for MLP 00:11:00 Running the Code 00:08:00 Testing for Dataframes 00:13:00 Predict Results 00:08:00 Section 04: Multi-Layer Neural Networks Project - Deaths in wars (Part - 2) Creating R Folder 00:14:00 Generating Output Plot 00:12:00 Testing and Predicting the Outputs 00:16:00

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brandnew version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative dversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

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 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.This skills-focused ccombines 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. 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 Applied AI: 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 Applied AI: 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.

Thinking about learning more about the data you are using in your job and how to present this? The BCS Foundation Award in Data Visualisation teaches how data is used to make decisions in an organisation and the importance of presenting accurate data in a way that enables decision making to happen.

This extensive course for beginners provides the basics of chatbots with machine learning, deep learning, AWS, and its applications, building it from scratch with hands-on practice for chatbot development. This course will help you learn basic to advanced mechanisms of developing chatbots using machine learning, deep learning, and AWS with Python.