17811 AI courses

Become a Certified User Experience Professional Build a career in UX with the world’s only university credit-rated online UX course. Acquire the mindset, the skills and the confidence that make UX designers so valuable. A rewarding and meaningful career awaits. Why become a UX designer? Be in demand UX is a high-growth sector. The demand for UX designers far outstrips the supply of qualified professionals. Get well paid UX designers are highly paid. The average entry-level salary for a UX designer in the United Kingdom is £35,465 (PayScale, 2019) Love your job UX designers make an impact. They solve real-world problems using an exciting mix of research, design, technology and psychology. Study method Online, self-paced Duration 6 months Access to content 12 months Qualification Level 8 Diploma - User Experience Design Awarded by Glasgow Caledonian University Regulated by SQA Additional info Exam(s) / assessment(s) is included in price Tutor is available to students Description Duration: 6 months Get certified in six months following a flexible, structured learning path. Delivery: Online The course is delivered entirely online, including video modules, mentor-led webinars and support. Assessment: 2-hour exam Before getting certified, you'll be assessed during a two-hour exam taken online. Outcomes for you Think like a UX designer; adopt the mindset that sets them apart Showcase your knowledge with a portfolio of project work Speak with the confidence that comes from a true, deep understanding of UX Advance your career with job-ready skills Complete a university credit-rated course, valued by employers globally Become a Certified UX Professional Learn with us We’ve worked hard to design the best possible online learning experience for you. As well as an unmatched syllabus, our approach includes: Projects & portfolio Learn by doing with a series of real-world projects ideal for your UX portfolio. Mentors & webinars Be guided by our hand-picked, world-class mentors during monthly webinars. Structure & support Stay motivated with a structured programme supported by fellow students and our customer success team. What you’ll learn We’ve put together a university credit-rated curriculum that’s deep, rigorous and covers everything you need to know to become a certified UX professional and turbocharge your career. Module 01 - Introduction to UX design Module 02 - User research Module 03 - User goals Module 04 - Structure and navigation Module 05 - Interactions Module 06 - Design principles Module 07 - Design patterns Module 08 - Mobile Module 09 - Workflows Module 10 - Prototyping and handover Module 11 - AI and UX Module 12 - Creating your portfolio Module 13 - Career guidance Hub Tutorials for Figma Projects and portfolio - Projects that build into a portfolio Exam - 2-hour final exam Requirements Background You don’t need experience in design or technology to enrol in our courses, although having one or both is a definite bonus. Our students come from a diverse array of backgrounds, including project management, development, graphic design, product management, business analysis and so on. Commitment You do need to be motivated and committed. We set a high bar. Studying for one of our professional qualifications requires a certain amount of time, energy and focus. Our team will be there to support you along every step of the way but success will come as a result of your own diligence. Career path The average salary for entry level user experience designers is £28,000 The average salary for user experience designers with 1-5 years experience ranges from £29,000 to £50,000 The average salary for user experience managers/leads is £80,000 Information from LinkedIn Salary Reports, based on real jobs listings.

In this course, you will learn the fundamentals of data visualization in Python using the well-known Matplotlib and Seaborn data science libraries and perform exploratory data analysis (EDA) by visualizing a data set using a variety of charts.

Machine literacy in data wisdom is a fleetly expanding discipline and now is the crucial element. This groundbreaking field equips computers and systems with the capacity to learn from data and ameliorate their performance over time without unequivocal programming. Statistical ways are employed to train algorithms to produce groups or prognostications and to find significant findings in data mining systems. immaculately, the conclusions made from these perceptivity impact crucial growth pointers in operations and companies. What's Machine Learning? . Machine learning classes in pune The machine literacy term was chased by Arthur Samuel in 1959. It's the discipline solely concentrated on studying and erecting tools and ways that can let machines learn. These styles use data to enhance the computer performance of a particular set of tasks. Machine literacy algorithms induce prognostications or possibilities and produce a model grounded on data samples, also called training data. There's a need for machine literacy as these algorithms are applied in a broad range of operations, for illustration, computer vision, dispatch filtering, speech recognition, husbandry, and drugs, where it's a challenge to produce traditional algorithms that can negotiate the needed tasks. orders in Machine Learning Being such a vast and complicated field, machine literacy is divided into three different orders machine literacy orders Supervised literacy – In this system, the algorithm is trained using data that has been labeled and in which the target variable or asked result is known. Once trained, the algorithm may make prognostications grounded on unidentified information by learning how to associate input variables with the willed affair. Unsupervised literacy – In this case, the algorithm is trained on unlabeled data, and its thing is to discover structures or patterns within the data without having a specific target variable in mind. Common unsupervised literacy tasks include dimensionality reduction and clustering. underpinning literacy – An algorithm is trained via relations with the terrain in this type of literacy. The algorithm learns how to operate in order to maximize a price signal or negotiate a particular ideal. Through prices or penalties, it receives feedback that helps it upgrade its decision-making process. Artificial Intelligence and Machine Learning Artificial intelligence( AI) is divided into several subfields, and machine literacy( ML) is one of them. In order to produce intelligent machines that can pretend mortal intelligence, a variety of methodologies, approaches, and technologies are used. This notion is known as artificial intelligence( AI). The development of ways and models that allow computers to acquire knowledge from data and make recommendations or judgments without unequivocal programming is the focus of machine literacy( ML). Some academics were interested in the idea of having machines learn from data in the early stages of AI as an academic field. They tried to approach the issue using colorful emblematic ways and neural networks. They were primarily perceptrons, along with other models that were ultimately discovered to be reimaginings of the generalized direct models of statistics. For this case, you aim to make a system secerning cows and tykes. With the AI approach, you'll use ways to make a system that can understand the images with the help of specific features and rules you define. Machine literacy models will bear training using a particular dataset of pre-defined images. You need to give numerous farmlands of cows and tykes with corresponding markers. Why is Machine Learning Important? Machine literacy is an abecedarian subfield of artificial intelligence that focuses on assaying and interpreting patterns and structures in data. It enables logic, literacy, and decision-making outside of mortal commerce. The significance of machine literacy is expanding due to the extensively more expansive and more varied data sets, the availability and affordability of computational power, and the availability of high-speed internet. It facilitates the creation of new products and provides companies with a picture of trends in consumer geste and commercial functional patterns. Machine literacy is a high element of the business operations of numerous top enterprises, like Facebook, Google, and Uber. Prophetic Analytics Machine learning course in pune Machine literacy makes prophetic analytics possible by using data to read unborn results. It's salutary in the fields of finance, healthcare, marketing, and logistics. Associations may prognosticate customer growth, spot possible troubles, streamline operations, and take visionary action to ameliorate results using prophetic models. Personalization and recommendation systems Machine literacy makes recommendation systems and substantiated gests possible, impacting every aspect of our diurnal lives. Platforms like Netflix, Amazon, and Spotify use machine literacy algorithms to comprehend stoner preferences and offer substantiated recommendations. Personalization boosts stoner pleasure and engagement while promoting business expansion. Image and speech recognition Algorithms for machine literacy are particularly good at jobs like speech and picture recognition. Deep literacy, a branch of ML, has converted computer vision and natural language processing. It makes it possible for machines to comprehend, dissect, and produce visual and audio input. This technology is helpful for driverless vehicles, surveillance, medical imaging, and availability tools, among other effects. Machine learning training in pune

Duration 3 Days 18 CPD hours This course is intended for This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll: Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights. Develop the ability to transform raw text into a structured format that machines can understand and analyze. Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects. Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends. Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction. Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback. The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots. Launch into the Universe of Natural Language Processing The journey begins: Unravel the layers of NLP Navigating through the history of NLP Merging paths: Text Analytics and NLP Decoding language: Word Sense Disambiguation and Sentence Boundary Detection First steps towards an NLP Project Unleashing the Power of Feature Extraction Dive into the vast ocean of Data Types Purification process: Cleaning Text Data Excavating knowledge: Extracting features from Texts Drawing connections: Finding Text Similarity through Feature Extraction Engineer Your Text Classifier The new era of Machine Learning and Supervised Learning Architecting a Text Classifier Constructing efficient workflows: Building Pipelines for NLP Projects Ensuring continuity: Saving and Loading Models Master the Art of Web Scraping and API Usage Stepping into the digital world: Introduction to Web Scraping and APIs The great heist: Collecting Data by Scraping Web Pages Navigating through the maze of Semi-Structured Data Unearth Hidden Themes with Topic Modeling Embark on the path of Topic Discovery Decoding algorithms: Understanding Topic-Modeling Algorithms Dialing the right numbers: Key Input Parameters for LSA Topic Modeling Tackling complexity with Hierarchical Dirichlet Process (HDP) Delving Deep into Vector Representations The Geometry of Language: Introduction to Vectors in NLP Text Manipulation: Generation and Summarization Playing the creator: Generating Text with Markov Chains Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank Peering into the future: Recent Developments in Text Generation and Summarization Solving real-world problems: Addressing Challenges in Extractive Summarization Riding the Wave of Sentiment Analysis Unveiling emotions: Introduction to Sentiment Analysis Tools Demystifying the Textblob library Preparing the canvas: Understanding Data for Sentiment Analysis Training your own emotion detectors: Building Sentiment Models Optional: Capstone Project Apply the skills learned throughout the course. Define the problem and gather the data. Conduct exploratory data analysis for text data. Carry out preprocessing and feature extraction. Select and train a model. ? Evaluate the model and interpret the results. Bonus Chapter: Generative AI and NLP Introduction to Generative AI and its role in NLP. Overview of Generative Pretrained Transformer (GPT) models. Using GPT models for text generation and completion. Applying GPT models for improving autocomplete features. Use cases of GPT in question answering systems and chatbots. Bonus Chapter: Advanced Applications of NLP with GPT Fine-tuning GPT models for specific NLP tasks. Using GPT for sentiment analysis and text classification. Role of GPT in Named Entity Recognition (NER). Application of GPT in developing advanced chatbots. Ethics and limitations of GPT and generative AI technologies.

This training is for lawyers who may come into contact with vulnerable people through their work. In particular, it is aimed at those who hold legal aid contracts and must meet the requirements of the Specialist Quality Mark (SQM) standard in respect of safeguarding. Everyone has the right to be kept safe from harm, abuse or neglect. We all have obligations to ensure that vulnerable people are protected and that we have appropriate procedures in place to ensure that abuse is identified and reported.  We will help you to understand the following; What is safeguarding? The different types of abuse Example signs of abuse, harm and neglect What action should be taken if abuse is suspected The SQM requirements What a Safeguarding Policy should include Target Audience The online course is suitable for staff of all levels, from support staff to senior partners and is aimed at firms maintaining or wishing to obtain the Specialist Quality Mark. Resources Comprehensive and up to date course notes will be provided to all delegates which may be useful for ongoing reference or cascade training. Please note a recording of the course will not be made available. Speaker Amie Higgins, Consultant, DG Legal Amie is a Senior Solicitor, qualified under the Law Society’s Immigration & Asylum Accreditation Scheme (IAAS) as an Advanced Caseworker and Supervisor. Amie specialises in all aspects of Immigration, Asylum and Human Rights work, with a specialism in human trafficking/modern slavery cases and working with vulnerable clients. Amie is an experienced trainer, delivering a range of training on both legal and practice management topics.

Business Networking 1 Day Training in Airdrie

Gain a thorough grasp of time series analysis and its effects, as well as practical tips on how to apply machine learning methods and build RNNs. Learn to train RNNs efficiently while taking crucial concepts such as overfitting and underfitting into account. The course offers a useful, hands-on manner for learning Python methods and principles.

In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.

Learners will develop knowledge from the FD&FA foundation module and/or industry experience and apply this to the commissioning, handover, and acceptance of FD&FA systems.

Why Learn Sketchup Basic to Advance Training Course? Course info SketchUp mastery with our hands-on course go from basics to advanced 3D modeling, advanced rendering, and expert lighting techniques. Immerse yourself in transformative learning tailored for SketchUp excellence. 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. Course Benefits: Comprehensive Skills Development: Master SketchUp from basics to advanced, gaining a comprehensive skill set. Efficient 3D Modeling: Learn to navigate and create intricate designs effortlessly with AI-driven 3D modeling techniques. Stunning Visual Realism: Explore advanced rendering methods to bring your designs to life with stunning visual realism. Professional Lighting Mastery: Harness the power of lighting to add depth and dimension, achieving a polished and professional finish. Flexible Learning: Tailor your own schedule with the convenience of pre-booking hours between 9 am and 7 pm, Monday to Saturday. Personalized 1-on-1 Training: Receive personalized attention and guidance with our 1-on-1 training approach. Course Title: "Sketchup Basic to Advance Training Course" Module 1: SketchUp Fundamentals (4 hours) Explore the SketchUp Interface Learn Basic Drawing Tools and Techniques Create and Edit Components Utilize Groups and Layers for Organization Introduction to Scenes and Animation Module 2: Advanced SketchUp Techniques (4 hours) Precision Modeling with Measurements Dynamic Components and Interactivity Advanced 3D Modeling Techniques Importing and Managing 3D Assets Enhance Functionality with SketchUp Extensions Module 3: V-Ray Rendering Basics (4 hours) Navigate the V-Ray Interface Set up V-Ray Materials Master Basic Lighting Techniques Understand Camera Settings and Composition Optimize Rendering Settings and Output Module 4: Advanced V-Ray Rendering (4 hours) Global Illumination and Image-based Lighting Realistic Material Creation Post-Processing using V-Ray Frame Buffer Efficient Scene Management with V-Ray Proxy Rendering Animation Sequences Course Project (2 hours) Integrate SketchUp and V-Ray for a Final Project Receive Guidance on Creating Photorealistic Renderings Participate in a Review and Feedback Session Upon completing the SketchUp Basic to Advance Training Course, participants will: Master SketchUp Basics: Navigate the interface, use drawing tools, and manage components effectively. Advance 3D Modeling: Apply precision modeling, dynamic components, and advanced techniques. Explore Extensions: Enhance functionality with SketchUp extensions. V-Ray Rendering Introduction: Learn V-Ray basics, including materials, lighting, and rendering settings. Advanced V-Ray Techniques: Implement global illumination, realistic materials, post-processing, and animation. Course Project Proficiency: Integrate SketchUp and V-Ray for a final project with photorealistic renderings.