5926 Model courses

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

The aim of this course is to provide an understanding of the intersection of law and ethics, including major ethical perspectives, ethical decision-making models, and the relationship between law and ethics. Students will learn about the importance of setting an ethical tone at the top of an organization and the ethical decision tree for business leaders. The course will cover the short- and long-term returns and the promotion of ethical behavior and social responsibility in the marketplace. Students will also gain knowledge about the history and basic framework of antitrust laws, including horizontal and vertical restraints of trade, price discrimination, and concentrations of market power. The course will also explore antitrust enforcement, antitrust injury, limitations of antitrust enforcement, and antitrust considerations. After the successful completion of the course, you will be able to learn about the following, Law & Ethics: What Is Ethics? Major Ethical Perspectives. An Ethical Decision Model. Relationship b/w law and ethics. Ethical tone sets at the top. Ethical business leader decision tree. Tension b/w short- and long-term returns. Social Responsibility. Positive actions. Promoting ethical Behaviour. Moral of the marketplace. Antitrust: History and Basic Framework of Antitrust Laws. Horizontal Restraints of Trade. Vertical Restraints of Trade. Price Discrimination- The Robinson-Patman Act. Sherman Act, Section 2- Concentrations of Market Power. Acquisitions and Mergers under Section 7 of the Clayton Act. Unfair Methods of competition section 5 of the federal commission act. Antitrust enforcement. Antitrust injury. Limitations of antitrust enforcement. Antitrust considerations. This course aims to explore the intersection of law and ethics, with a particular focus on the major ethical perspectives and an ethical decision-making model. Students will examine the relationship between law and ethics and how ethical tone sets at the top of an organization. The course will cover topics such as the tension between short- and long-term returns, social responsibility, promoting ethical behaviour, and the morale of the marketplace. Additionally, students will learn about antitrust laws, including the history and basic framework of antitrust laws, horizontal and vertical restraints of trade, price discrimination, and concentrations of market power. The course will also cover acquisitions and mergers under Section 7 of the Clayton Act, unfair methods of competition under Section 5 of the Federal Commission Act, antitrust enforcement, antitrust injury, and the limitations of antitrust enforcement. The aim of this course is to provide an understanding of the intersection of law and ethics, including major ethical perspectives, ethical decision-making models, and the relationship between law and ethics. Students will learn about the importance of setting an ethical tone at the top of an organization and the ethical decision tree for business leaders. The course will cover the short- and long-term returns and the promotion of ethical behavior and social responsibility in the marketplace. Students will also gain knowledge about the history and basic framework of antitrust laws, including horizontal and vertical restraints of trade, price discrimination, and concentrations of market power. The course will also explore antitrust enforcement, antitrust injury, limitations of antitrust enforcement, and antitrust considerations. VIDEO - Course Structure and Assessment Guidelines Watch this video to gain further insight. Navigating the MSBM Study Portal Watch this video to gain further insight. Interacting with Lectures/Learning Components Watch this video to gain further insight. Concept and Implications of Ethics and Antitrust in Business Self-paced pre-recorded learning content on this topic. Concept and Implications of Ethics and Antitrust in Business Put your knowledge to the test with this quiz. Read each question carefully and choose the response that you feel is correct. All MSBM courses are accredited by the relevant partners and awarding bodies. Please refer to MSBM accreditation in about us for more details. There are no strict entry requirements for this course. Work experience will be added advantage to understanding the content of the course. The certificate is designed to enhance the learner's knowledge in the field. This certificate is for everyone eager to know more and get updated on current ideas in their respective field. We recommend this certificate for the following audience. Ethics and Compliance Officer Antitrust Attorney Corporate Social Responsibility Manager Business Ethics Consultant Antitrust Investigator Compliance Analyst Risk Management Specialist Business Conduct and Ethics Trainer Antitrust Litigation Specialist Corporate Responsibility Coordinator Average Completion Time 2 Weeks Accreditation 3 CPD Hours Level Advanced Start Time Anytime 100% Online Study online with ease. Unlimited Access 24/7 unlimited access with pre-recorded lectures. Low Fees Our fees are low and easy to pay online.

Actuarial Science is a specialised field that blends mathematics, statistics, and financial theory to assess risk and uncertainty in sectors such as insurance, pensions, and investments. This course offers a clear and structured exploration of the fundamental concepts behind actuarial practice, starting with essential topics like cash flow valuation and life tables, and progressing towards more advanced subjects such as stochastic models and premium calculation. Whether you are new to actuarial studies or seeking to strengthen your foundational knowledge, this course provides a solid theoretical framework essential for understanding the profession’s key methodologies. Through carefully designed modules, learners will delve into critical areas including life insurance, annuities, risk models, and the impact of taxation and inflation on financial products. The curriculum emphasises the mathematical and probabilistic underpinnings that actuaries use to evaluate financial risks, ensuring a thorough grasp of both deterministic and stochastic approaches. By the end of the course, participants will be well-prepared to appreciate the complexities of actuarial analysis and its application in various financial environments, making this an excellent step for those aiming to build a career in actuarial science or related disciplines. 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 01 : Introduction to Actuary Module 02 : Actuaries and Their Environment Module 03 : The Valuation of Cash Flows Module 04 : The Basic Deterministic Model Module 05 : The Life Table Module 06 : Life Annuities Module 07 : Life Insurance Module 08 : The Stochastic Life Contingencies Model Module 09 : The Stochastic Approach to Insurance and Annuities Module 10 : Taxation and Inflation Module 11 : Probabilistic Models, Uncertain Payment and Profit Testing Module 12 : Individual Risk Models Module 13 : Principles of Premium Calculation Module 14 : Multiple Decrement Theory Learning Outcomes: Analyse and predict financial risks with confidence. Master the valuation of cash flows and basic deterministic models. Understand the intricate world of life tables and annuities. Explore the complexities of life insurance and stochastic life contingencies. Navigate tax and inflation issues in the financial realm. Gain proficiency in probabilistic models and principles of premium calculation. 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. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Aspiring actuaries Financial analysts Risk management enthusiasts Insurance professionals Finance and economics students Anyone interested in mastering financial uncertainties Career path Actuary Financial Analyst Risk Manager Insurance Underwriter Pension Consultant Investment Analyst 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.

The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R

Employee Motivation Certification Employee motivation is pivotal to a company's success. Workers who enjoy their jobs are more productive and more willing to put in extra effort and take fewer sick days. A motivated workforce contributes to a positive company culture, which in turn attracts talented employees and reduces turnover. This course provides an introduction to the mechanics of employee motivation, the factors that encourage people to give their best at work, and how to increase worker motivation. You will learn two theories of motivation and how they can be translated into everyday management practice. You Will Learn: Why organisations must take the issue of employee motivation seriously and how motivation can be understood using the ERG and Goal Setting models. How these theories can help us understand what really motivates employees and how managers can put these insights into practice. How to show appreciation to your employees and why traditional recognition schemes may not be effective. How and why employee involvement boosts motivation and the merits of the Tell, Sell, Consult and Join model as a decision-making framework. How to promote an enjoyable, inspiring and inclusive work environment that encourages a healthy work-life balance. Benefits Of Taking This Course You will develop a general understanding of employee motivation, which is invaluable if you work in a management position. You will be able to help disengaged employees regain their motivation, thus improving company performance. You will be able to prevent absenteeism and high turnover by promoting a healthy workplace environment. You will be able to make practical recommendations regarding employee motivation at all levels of your organisation. You will learn how to collaborate with employees to improve workplace processes and policies in a way that enhances everyone's job satisfaction.

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Did you know? The construction industry in the UK is worth £110 billion and employs over 2.5 million people. Yet, many projects still experience delays and cost overruns. Why? One reason is the lack of skilled professionals trained in Building Information Modeling (BIM). Are you ready to be part of the future of construction? This BIM - Building Information Modeling Diploma equips you with the skills and knowledge to excel in this cutting-edge field. With this BIM - Building Information Modeling Diploma course you will get 20 CPD Accredited PDF Certificates, Hard Copy Certificate Advanced Diploma in Architectural Studies at QLS Level 7 and our exclusive student ID card absolutely free. Courses Are Included In This BIM - Building Information Modeling Diploma Bundle: Course 01: Building Information Modelling Course 02: Architectural Studies Course 03: LEED V4 - Building Design and Construction Course 04: Autodesk Revit: Modeling & Rendering Course 05: Revit Architecture Ultimate Interior Design Course Course 06: Revit Architecture Ultimate Exterior Design for Beginners Course 07: Solidworks Foundation Training Course 08: CAD Design Training Course 09: AutoCAD VBA Programming - Beginner Course Course 10: Learn AutoCAD Programming using VB.NET - Hands On! Course 11: AutoCAD Programming Using C#.NET Level 7 Course 12: Autocad Electrical Design Course 13: Construction Management Principles and Practices Course 14: Construction Project Management Course 15: Diploma in Construction Industry Scheme (CIS): QLS Endorsed Course 16: Construction, Design and Management(CDM) Awareness Training Course 17: Site Management Safety Training Scheme (SMSTS) at QLS Level 5 Course 18: WELL Building Standard Course 19: Building Surveying Practice Course 20: Lone Worket Safety Don't miss this chance to elevate your career and skillset. Enrol in BIM - Building Information Modeling Diploma today and take the first step towards achieving your goals and dreams. Learning Outcomes Master BIM software: Revit, AutoCAD, Solidworks. Design & model buildings: 3D modeling, project documentation. Collaborate effectively: BIM workflows, team communication. Manage construction projects: Scheduling, cost estimation, risk assessment. Understand sustainability principles: LEED certification, green building practices. Comply with UK regulations: CDM, SMSTS, electrical safety. Why buy this Bundle? Free QLS endorsed Certificate upon completion of BIM Diploma Get a free student ID card with BIM - Building Information Modeling Diploma Lifetime access to the BIM - Building Information Modeling Diploma course materials Get instant access to this BIM - Building Information Modeling Diploma course Learn BIM - Building Information Modeling Diploma from anywhere in the world 24/7 tutor support with the BIM - Building Information Modeling Diploma course. BIM Diploma is an entirely online, interactive lesson with voice-over audio. Start your learning journey straightaway! BIM - Building Information Modeling Diploma premium bundle consists of 20 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of BIM - Building Information Modeling Diploma is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience. Certification You have to complete the assignment given at the end of the BIM - Building Information Modeling Diploma course and score a minimum of 60% to pass each exam. After passing the Advanced Diploma in Architectural Studies at QLS Level 7 course exam You will be entitled to claim a QLS endorsed certificate completely free. CPD 380 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This BIM - Building Information Modeling Diploma course is ideal for: Students seeking mastery in BIM Professionals seeking to enhance BIM Individuals looking for a BIM Anyone passionate about BIM Requirements This BIM - Building Information Modeling Diploma doesn't require prior experience and is suitable for diverse learners. Career path This BIM - Building Information Modeling Diploma bundle will allow you to kickstart or take your career in the related sector to the next stage. BIM Technician BIM Coordinator BIM Modeler BIM Manager Project Manager Design Engineer Certificates Advanced Diploma in Architectural Studies at QLS Level 7 Hard copy certificate - Included CPD Accredited Digital certificate Digital certificate - Included

Buy Big. Save Big! Offer Ends Soon, Hurry Up!! Get FREE 1 QLS Endorsed Certificate Course with 10 Additional CPD Accredited Bundle Courses In A Single Payment. If you wish to gain a solid and compact knowledge of Particle Physics and boost yourself for your desired career, then take a step in the right direction with this industry-standard, comprehensive course designed by professionals. This bundle package includes 1 premium, The Quality Licence Scheme-endorsed course, 10 additional CPD accredited certificate courses, with expert assistance, and a FREE courses assessment included. Learn a range of in-demand technical skills to help you progress your career with 12 months of unlimited access to this bundle of courses. If you enrol in this package, you will not be charged any extra fees. This bundle package includes: Course 01: Diploma in Particle Physics at QLS Level 4 10 Additional CPD Accredited Premium Courses - Course 01: Radiography Course 02: Meteorology Course 03: Astronomy Fundamentals Course 04: Carbon Literacy Knowledge Course Course 05: Advanced Diploma in Renewable Energy Course 06: Renewable Energy: GRID Solar Energy Diploma Course 07: Energy Saving in Efficient Electric Motors Course 08: Diploma in Basic Chemistry Level 3 Course 09: Critical Thinking Training Course 10: Decision-Making in High-Stress Situations Success becomes a lot simpler with this bundle package, which allows you to monetise your skills. This bundle is appropriate for both part-time and full-time students, and it can be completed at your own pace. This premium online bundle course, named Particle Physics, supports your professional growth with Quality Licence Scheme endorsement, as well as CPD accreditation. 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Start your learning journey straight away with this Particle Physics bundle and take a step toward a brighter future! *** Course Curriculum: *** The bundle courses have the following curriculum: Module 01: Introduction to Particle Physics Basics of Elementary Particles Types of Interactions Classification of Elementary Particles Atomic Models Module 02: Elementary Particles Leptons Hadrons Quantum Numbers Module 03: The Nucleus Proton-Electron Hypothesis Proton-Neutron Hypothesis Terms Associated with the Nucleus Nuclear Models Nuclear Forces Module 04: Nuclear Physics Nuclear Reactions Types of Nuclear Reactions Radioactivity Radioactivity Terms Module 05: Particle Accelerators What Is a Particle Accelerator? Cockcroft and Walton Accelerator Van de Graaff Accelerator Tandem Accelerator Linear Accelerator (Linac) or Drift Tube Accelerator Magnetic Resonance Accelerators or Cyclotrons Betatron Synchrocyclotrons or Frequency Modulated Cyclotrons Azimuthally Varying Field (a.v.f.) Or Sector Focusing Cyclotrons Synchrotrons Module 06: Radiation detectors What Is a Radiation Detector? Gas-filled detectors Scintillation Detectors Semiconductor Radiation Detectors Module 07: The Standard Model Basics of the Standard Model The Interactions The Quark System Gauge Theories What is the Bundle Assessment Process? We offer an integrated assessment framework to make the process of evaluation and accreditation for learners easier. You have to complete the assignment questions given at the end of the course and score a minimum of 60% to pass each exam. Our expert trainers will assess your assignment and give you feedback after you submit the assignment. You will be entitled to claim a certificate endorsed by the Quality Licence Scheme after you have completed all of the exams. CPD 220 CPD hours / points Accredited by CPD Quality Standards Who is this course for? 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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.

About this event A life drawing class from a nude model, we try to alternate between male and female but this isn't always possible. Suitable for beginners and the more experienced. Basic materials available. Bring your own if you would like to work in a specific medium. 2 hour drawing time. View other classes @ FaceBook - @woodrawschool Twitter - @JainMcKay  Instagram - https://www.instagram.com/worcesterdrawingschool Refund Policy Contact the organiser to request a refund.