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In a world where artificial intelligence is becoming increasingly important, learning the skills needed to build your own neural networks is more crucial than ever. Imagine having the ability to create your own neural network and train it to perform complex tasks. With our Project on Deep Learning - Artificial Neural Network course, you can do just that. In this course, you'll learn how to build your own artificial neural network from scratch, using Python. From installing the necessary packages to data preprocessing and encoding, you'll discover how to prepare your data for neural network training. You'll also gain a deeper understanding of the steps involved in building your own artificial neural network and learn how to make predictions using your model. So if you're ready to unlock the power of deep learning and take your skills to the next level, enrol in our Project on Deep Learning - Artificial Neural Network course today and start building your own neural networks. Learning Outcomes: Gain a solid understanding of artificial neural networks and their applications in deep learning. Learn how to install the necessary packages and preprocess data for neural network training. Discover how to encode data and build your own artificial neural network using Python. Understand the steps involved in making predictions using your neural network model. Learn how to deal with imbalanced data in your neural network training. The Project on Deep Learning - Artificial Neural Network course is designed to provide you with the skills and knowledge you need to build your own neural network and perform complex tasks using deep learning. You'll learn how to install the necessary packages, preprocess data, and encode data for neural network training. You'll also gain a deeper understanding of artificial neural networks and learn how to build your own model using Python. By the end of the course, you'll be able to make predictions using your neural network model and understand how to deal with imbalanced data in your training. ⊱⊱ Project on Deep Learning - Artificial Neural Network Course Curriculum Section 01: Introduction Section 02: ANN Installation Section 03: Data Preprocessing Section 04: Data Encoding Section 05: Steps to Build ANN Section 06: Predictions and Imbalance-Learn How is the course assessed? Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%. Exam & Retakes: It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable. Certification Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Data analysts who want to expand their skills in deep learning and artificial neural networks. Programmers who want to learn how to build their own neural network models for advanced tasks. Entrepreneurs who want to develop their own deep learning-based applications. Students who want to enhance their skills in deep learning and prepare for a career in the field. Anyone who wants to explore the world of artificial neural networks and deep learning projects. Career path Data Analyst: £24,000 - £45,000 Machine Learning Engineer: £28,000 - £65,000 Deep Learning Engineer: £30,000 - £75,000 Technical Lead: £40,000 - £90,000 Chief Technology Officer: £90,000 - £250,000 Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9. Certificate of completion Hard copy certificate - £15 Hard copy can be sent to you via post at the expense of £15.

ChatGPT, along with other AI tools, aims not to replace the human touch in management, but to enhance it. By addressing repetitive, daily tasks, these tools free up managers to concentrate on core responsibilities like strategic decision-making, team development, and innovation. As we move further into the digital age, integrating tools such as ChatGPT isn't a luxury; it's the future of proactive leadership. In this guide, we'll delve into 10 practical ways through which AI can elevate your efficiency and refine the quality of your work. Gain familiarity with prominent AI tools in the market Efficiently compose and respond to emails Generate concise summaries of complex reports and data. Obtain quick insights, data, and research across varied topics Streamline the writing of articles, training notes, and posts Craft interview tests, form relevant questions, and design checklists for the hiring process 1 Streamlining emails An inbox can be a goldmine of information but also a significant time drain for managers. Here's how to optimise it: Drafting responses: Give the AI a brief, and watch it craft a well-structured response. Sorting and prioritising: By employing user-defined rules and keywords, ChatGPT can flag important emails, ensuring no vital communication slips through the cracks. 2 Efficient report writing Reports, especially routine ones, can be time-intensive. Here's a smarter approach: Automate content: Supply key data points to the AI, and let it weave them into an insightful report. Proofreading: Lean on ChatGPT for grammar checks and consistency, ensuring each report remains crisp and error-free. 3 Rapid research From competitor insights to market trends, research is a pivotal part of management. Data synthesis: Feed raw data to the AI and receive succinct summaries in return. Question-answering: Pose specific questions about a dataset to ChatGPT and extract swift insights without diving deep into the entire content. 4 Reinventing recruitment Hiring can be a lengthy process. Here's how to make it more efficient: Resume screening: Equip the AI to spot keywords and qualifications, ensuring that only the most fitting candidates are shortlisted. Preliminary interviews: Leverage ChatGPT for the initial rounds of interviews by framing critical questions and evaluating the responses. 5 Enhancing training Especially for extensive teams, training can be a monumental task. Here's how ChatGPT can assist: Customised content: Inform the AI of your training goals, and it will draft tailored content suitable for various roles. PowerPoint design: Create visually appealing slide presentations on any topic in minimal time.

Machine Learning and other AI: Are You Ready? Machine Learning is the latest 'hot' title in computing and Artificial Intelligence. It sounds new but is influencing your life already. Machine Learning and AI will affect more and more of your life as they mature and more enabling technologies intersect with them. Machine Learning will change many disciplines and careers, overcoming scale issues, enabling better knowledge and insights, and augmenting many professions. Are you ready? This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.

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Unleash the power of Artificial Intelligence in your business leadership. Discover how AI reshapes business models, explore the potential of Generative AI, and build a robust, future-ready AI strategy with our expert-led course.

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

Are your emotions running wild, playing hide and seek with your peace of mind? Say hello to a brighter, balanced you with our secret weapon: Dialectical Behavior Therapy (DBT) for Emotional Wellness! This program is meticulously designed to guide you through a progressive exploration of skills that empower individuals to manage emotions, enhance interpersonal connections, and navigate life's challenges with resilience. At the heart of this course lies an exploration of mindfulness, cultivating a profound awareness of the present moment that lays the foundation for emotional balance. Learn to navigate the intricacies of emotion regulation, gaining tools to understand and manage feelings effectively. Dive into the art of interpersonal effectiveness, honing communication skills and building meaningful connections. Discover the strength within distress tolerance, equipping yourself with strategies to navigate adversity with grace. Tailored for both personal growth and professional application, this course extends its reach to specific populations, offering insights into customising DBT for diverse contexts. Whether you're seeking personal enrichment or expanding your professional repertoire, this course provides a holistic approach to emotional wellness. Unlock the secrets of DBT, and empower yourself to lead a life of emotional resilience and fulfilment. Join us where your path to emotional wellness starts. Key Features CPD Accredited FREE PDF + Hardcopy certificate Fully online, interactive course Self-paced learning and laptop, tablet and smartphone-friendly 24/7 Learning Assistance Discounts on bulk purchases Course Curriculum Module 1: Introduction to DBT Module 2: Mindfulness Skills Training Module 3: Emotion Regulation Skills Module 4: Interpersonal Effectiveness Skills Module 5: Distress Tolerance Skills Module 6: Application of DBT in Specific Populations Module 7: Implementation of DBT in Clinical Settings Module 8: Ethical and Professional Considerations in DBT Practice Learning Outcomes: Understand the foundations of Dialectical Behavior Therapy principles. Apply mindfulness skills to enhance emotional awareness and self-regulation. Cultivate effective strategies for regulating and managing emotions. Develop interpersonal skills to navigate complex relationships with confidence. Acquire distress tolerance techniques for coping with challenging situations. Demonstrate ethical considerations essential for a successful DBT practice. Accreditation This course is CPD Quality Standards (CPD QS) accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Certificate After completing this course, you will get a FREE Digital Certificate from Training Express. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Mental health professionals Therapists Counsellors Psychologists Social workers Healthcare professionals Students in psychology-related fields Individuals passionate about emotional well-being Career path Clinical Psychologist Counselling Psychologist Mental Health Therapist Social Worker Psychotherapist Psychiatric Nurse Certificates Digital certificate Digital certificate - Included Once you've successfully completed your course, you will immediately be sent a FREE digital certificate. Hard copy certificate Hard copy certificate - Included Also, you can have your FREE printed certificate delivered by post (shipping cost £3.99 in the UK). For all international addresses outside of the United Kingdom, the delivery fee for a hardcopy certificate will be only £10. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

This Dialectal Behaviour Therapy (DBT) course is taught by a qualified psychotherapist, designed to provide learners with a broad understanding of this form of talking therapy. It covers the role of the DBT Therapist, career skills and the professional requirements to work in this field. DBT is a form of CBT, used to treat a range of psychological disorders, particularly Borderline Personality Disorder (BPD). In this course, you will learn about the uses, challenges and benefits of DBT, including its stages of treatment. You will also develop core life skills that every therapist/counsellor requires to succeed in this profession. This course cover theoretical knowledge, taking you through the different types of psychological disorders and their causes. Through step-by-step guidance and case studies, you will deepen your understanding of how to set up the therapeutic environment, conduct behavioural analysis, and much more. Become certified online and kickstart your career today. Learning Outcomes By the end of the course, learners will be able to: Demonstrate excellent knowledge of the difference between DBT and CBT Identify the different psychological disorders that DBT can be used to treat Understand the uses, challenges and effectiveness of DBT Express understanding of the stages of treatment of DBT Set up a learning environment for the client and conduct a behavioural analysis Apply a range of DBT strategies and life skills to treat different disorders Key Features of the Dialectical Behaviour Therapy Course Our trusted, high quality and affordable online courses are designed to train individuals to become experts in their field. Instant e-certificate and hard copy dispatch by next working day Fully online, interactive course with audio voiceover Developed by qualified safeguarding professionals Self-paced laptop/tablet/smartphone-friendly learning 24/7 Learning support and assistance Course Curriculum Module 01: Introduction In module one, we will get to know the instructor of the course and their professional background. We will also explore the key topics that will be discussed in this course. Module 02: What is Counselling & Psychotherapy In module two, we will gain a deeper understanding of the definition of counselling and psychotherapy. It covers a range of approaches and theories. Module 03: About Dialectical Behaviour Therapy (DBT) In module three, we will explore the history and philosophy of dialectal behavioural therapy and its benefits/challenges. Module 04: DBT Strategies & 'Life Skills' In module four, we will develop some core life skills required to work in the field of therapy. We will also learn fundamental DBT strategies for treating anxiety, depression, eating disorders, BPD and OCD. Module 05: Treatment by DBT In module five, we will explore the stages of treatment in DBT. It also includes step-by-step guidance on how to set up the therapeutic environment and how to conduct a behavioural analysis. Module 06: DBT for Psychological Disorders Module six takes us through the uses of DBT and why it is particularly effective for treating psychological disorders. We will explore the different types of psychological disorders in greater depth in this module. Module 07: Effectiveness of DBT In module seven, we will learn how effective DBT treatment is and how long it takes to work. We will also gain a deeper understanding of the types of people who benefit from DBT the most. Certification Once you've successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). Our certifications have no expiry dates, although we do recommend that you renew them every 12 months. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This comprehensive training course is ideal for individuals who are looking to kickstart their careers. Related professions in this field include the following: DBT Therapist CBT Therapist Counselling Psychologist Occupational Therapist Requirements Learners do not require any prior qualifications to enrol on this course. Certificates Digital certificate Digital certificate - Included Hard copy certificate Hard copy certificate - Included

Artificial Intelligence (AI) is the most disruptive technology since the internet came onto the scene. AI is transforming every aspect of how we manage projects from developing a business case, to planning the work, managing risk, and tracking performance. Because the technology and market are moving so fast, it can be difficult to know how to start using AI on projects. Generative AI for Project Management will engage you with diverse Generative AI tools to start, plan, and manage either your own project or a generic case study. We will embrace a tool agnostic approach to adopting, integrating, and scaling Generative AI without compromising data or trust. You will have hands-on practice utilizing AI tools to optimize your time and your outcomes. You will be accessing a variety of AI tools requiring you to register for a free account. A computer is required for all traditional classroom deliveries. None At the end of this program, you will be able to: Define essential terms and concepts related to artificial intelligence (AI) Illustrate how prompts facilitate interaction with Generative AI Recognize the capabilities of Large Language Models Craft prompts to develop project origination documents Create prompts to assist in planning a project Develop user stories with Generative AI Analyze project performance using Generative AI Identify the limitations of Generative AI Identify the risks associated with using Generative AI Articulate the need for governance and ethics when establishing an AI program in an organization Course Overview Getting Started Foundation Concepts Understanding essential terms and concepts related to AI Exploring various Generative AI Models Understanding Prompts Creating Prompts for Project Startup Prompts for starting a project Prompts for planning a project Best Practices for prompt engineering Creating Prompts for Managing Projects Creating agile user stories Measuring project performance Analyzing a schedule Using Generative AI Responsibly Limitations of AI Models Establishing an AI governance framework Future trends and next steps Summary and Next Steps

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.