581 Mac courses in Cardiff delivered Live Online

Course Overview The Diet and Nutrition Specialist Course provides a comprehensive introduction to the principles of healthy eating, nutrient functions, and dietary management. This course is designed to equip learners with an in-depth understanding of food science, digestion, nutritional needs across different life stages, and the role of diet in preventing and managing health conditions. Throughout the course, students will explore a variety of essential topics, from macronutrients and micronutrients to food intolerances and women’s health concerns. Upon completion, learners will possess the knowledge to interpret food labels, recommend balanced diets, and understand the nutritional considerations for various groups. This course holds immense value for anyone wishing to enhance their expertise in dietetics or pursue a career focused on promoting health and wellbeing. Learners will leave with the ability to make informed nutritional assessments and offer evidence-based advice for healthy living. Course Description The Diet and Nutrition Specialist Course offers a structured learning journey through the fundamentals of food and nutrition, covering key topics essential for understanding human health. Learners will delve into the classification and function of macronutrients and micronutrients, explore the processes of digestion, absorption, and detoxification, and examine how nutrition impacts common health conditions such as heart disease, cancer, and diabetes. Further areas of focus include energy requirements, managing food allergies, maintaining a healthy weight, and specific nutritional needs during pregnancy and infancy. The course is delivered through engaging lessons designed to build both theoretical knowledge and critical thinking skills. By completing this course, participants will develop a robust foundation in nutrition science, enabling them to confidently address dietary concerns, support wellness initiatives, and contribute to healthier communities. This programme is ideal for those committed to advancing their knowledge of nutrition within a professional or personal context. Course Modules Module 01: Basics of Food and Nutrition Module 02: Macronutrients: Classification & Functions of Carbohydrates Module 03: Macronutrients: Classification & Functions of Lipids and Proteins Module 04: Micronutrients I & II Module 05: Digestion and Absorption Module 06: Detoxification Module 07: Healthy & Balanced Diet and Food Labelling Module 08: Average Requirements for Energy and Nutrients Module 09: Food Intolerances and Allergies Module 10: Health Conditions: Heart Disease, Cancer & Diabetes Module 11: Maintaining a Healthy Weight and Eating Problems & Treatments Module 12: Women’s Health Care & Common Concerns During Pregnancy Module 13: Nutrition for the Baby & Special Foods (See full curriculum) Who is this course for? Individuals seeking to gain a comprehensive understanding of diet and nutrition. Professionals aiming to enhance their career in health, wellness, or nutrition advisory roles. Beginners with an interest in nutrition, healthy eating, and human health. Health enthusiasts wanting to deepen their knowledge of dietary science. Career Path Nutrition Advisor Diet and Wellness Consultant Health Coach Community Health Worker Food and Nutrition Educator Public Health Assistant Healthcare Support Roles

Course Overview This Barista Training course provides a comprehensive introduction to the essential skills and knowledge required to excel in coffee making. Learners will gain a deep understanding of the coffee industry, from the basics of coffee beans to advanced espresso techniques. The course covers key areas such as machine operation, grinding, milk frothing, and latte art, equipping students with the confidence and competence to perform in a variety of coffee-related settings. Whether you're aiming to work in a café or enhance your skills for personal interest, this course offers valuable insights into the world of coffee. Course Description The Barista Training course is designed to provide learners with a solid foundation in coffee-making, starting with the origins of coffee beans and their journey to your cup. Topics include the operation of espresso machines and grinders, understanding the mechanics of espresso brewing, and perfecting the art of milk frothing and latte design. Learners will also explore menu creation and the essential skills required to manage the bar area. By the end of the course, students will have a clear understanding of the barista role and will be able to create a variety of espresso-based drinks, ensuring a top-quality coffee experience. This course offers both theoretical knowledge and practical expertise for anyone looking to build a career or deepen their knowledge of coffee culture. Course Modules Module 01: Introduction to Coffee Module 02: Espresso Machine Module 03: Espresso Grinder Module 04: Introduction to Espresso & Espresso Mechanics Module 05: Let's Wrap Up Module 06: Milk Frothing Module 07: Latte Art Module 08: Menu Module 09: Behind The Bar (See full curriculum) Who is this course for? Individuals seeking to pursue a career in the coffee industry. Professionals aiming to improve their barista skills. Beginners with an interest in learning about coffee making. Coffee enthusiasts looking to develop a deeper understanding of brewing techniques. Career Path Barista Coffee Shop Manager Café Owner Espresso Specialist Coffee Trainer

Course Overview The "Gardening - Garden Design Level 5" course offers an in-depth exploration of the principles, processes, and components that define successful garden design. Learners will develop a comprehensive understanding of the key elements of garden layout, plant selection, and the design process itself. The course is designed to equip learners with the necessary skills and knowledge to create aesthetically pleasing and functional gardens, regardless of scale. By the end of the course, learners will have the expertise to approach a wide range of garden design challenges, from residential to commercial projects. This course will provide a solid foundation in garden design theory, while also fostering the ability to make informed decisions about materials, planting, and sustainable design practices. Course Description In this course, learners will explore a range of topics related to garden design, beginning with an introduction to the role of the garden designer and the key principles that underpin successful designs. The course covers essential topics, such as the design process, selecting suitable plants, and designing a rain garden for water management. Learners will also gain insight into the tools and machinery required for effective garden design, as well as the importance of maintenance and the associated costs. Throughout the course, learners will be encouraged to develop their design thinking, as they will gain the ability to plan, conceptualise, and implement garden designs suited to a variety of client needs. By the end of the course, learners will have acquired both theoretical knowledge and practical skills essential for a career in garden design. Course Modules Module 01: Introduction to Garden Design Module 02: The Role of the Garden Designer Module 03: The Basic Principles of Garden Design Module 04: Components of Garden Design Module 05: Garden Design Process Module 06: Designing a Rain Garden Module 07: Essential Tools & Machinery Module 08: Plant Selection and Material Guide Module 09: Garden Maintenance Module 10: Costing and Estimation (See full curriculum) Who is this course for? Individuals seeking to pursue a career in garden design. Professionals aiming to enhance their skills in landscaping and horticulture. Beginners with an interest in garden design and landscaping. Anyone looking to start a garden design business or enhance their current practices. Career Path Garden Designer Landscape Architect Horticulturalist Landscape Consultant Garden Maintenance Specialist Sustainability Consultant for Green Spaces

Course Overview: The "FinTech and Big Data Analytics" course provides an in-depth exploration of the dynamic intersection between financial technology (FinTech) and big data. Learners will gain essential knowledge about the innovative solutions disrupting the financial services industry, such as cryptocurrencies, insurtech, and regtech. The course offers insights into the tools, technologies, and trends shaping the future of finance, with a specific focus on how big data analytics is transforming business models and decision-making. By the end of the course, learners will have a comprehensive understanding of FinTech's growth and its applications, enabling them to make informed decisions in this rapidly evolving field. Course Description: This course delves deeper into the core concepts of financial technology and big data, exploring the impact of FinTech innovations on traditional financial systems. Topics covered include the rise of cryptocurrencies, regulatory technology (RegTech), the development of insurance technologies (InsurTech), and the use of big data in reshaping business strategies. Learners will explore the key technologies that drive FinTech, such as blockchain, artificial intelligence (AI), and machine learning, and learn how they enable data-driven decision-making in finance. The course prepares learners for the evolving future of FinTech, equipping them with the necessary skills to understand and navigate this transformative landscape. Course Modules: Module 01: Introduction to Financial Technology – FinTech Module 02: Exploring Cryptocurrencies Module 03: RegTech Module 04: Rise of InsurTechs Module 05: Big Data Basics: Understanding Big Data Module 06: The Future of FinTech (See full curriculum) Who is this course for? Individuals seeking to understand the financial technology landscape. Professionals aiming to advance their careers in the rapidly evolving FinTech sector. Beginners with an interest in emerging financial technologies and data analytics. Entrepreneurs looking to innovate within the financial services industry. Career Path: Financial Analyst FinTech Specialist Data Analyst in Financial Services Blockchain Developer RegTech Consultant InsurTech Specialist Big Data Analyst in Finance

Develop Big Data Pipelines with R, Sparklyr & Power BI Course Overview: This course offers a comprehensive exploration of building and managing big data pipelines using R, Sparklyr, and Power BI. Learners will gain valuable insight into the entire process, from setting up and installing the necessary tools to creating effective ETL pipelines, implementing machine learning techniques, and visualising data with Power BI. The course is designed to provide a strong foundation in data engineering, enabling learners to handle large datasets, optimise data workflows, and communicate insights clearly using visual tools. By the end of this course, learners will have the expertise to work with big data, manage ETL pipelines, and use Sparklyr and Power BI to drive data-driven decisions in various professional settings. Course Description: This course delves into the core concepts and techniques for managing big data using R, Sparklyr, and Power BI. It covers a range of topics including the setup and installation of necessary tools, building ETL pipelines with Sparklyr, applying machine learning models to big data, and using Power BI for creating powerful visualisations. Learners will explore how to extract, transform, and load large datasets, and will develop a strong understanding of big data architecture. They will also gain proficiency in visualising complex data and presenting findings effectively. The course is structured to enhance learners' problem-solving abilities and their competence in big data environments, equipping them with the skills needed to manage and interpret vast amounts of information. Develop Big Data Pipelines with R, Sparklyr & Power BI Curriculum: Module 01: Introduction Module 02: Setup and Installations Module 03: Building the Big Data ETL Pipeline with Sparklyr Module 04: Big Data Machine Learning with Sparklyr Module 05: Data Visualisation with Power BI (See full curriculum) Who is this course for? Individuals seeking to understand big data pipelines. Professionals aiming to expand their data engineering skills. Beginners with an interest in data analytics and big data tools. Anyone looking to enhance their ability to analyse and visualise data. Career Path: Data Engineer Data Analyst Data Scientist Business Intelligence Analyst Machine Learning Engineer Big Data Consultant

Build Your Own PC: A Beginner's Guide Course Overview "Build Your Own PC: A Beginner's Guide" is designed for individuals looking to gain an understanding of computer hardware and the process of assembling a fully functional PC. The course covers the essential components involved in building a computer, from selecting the right hardware to installing software and ensuring network connectivity. Learners will also be introduced to building gaming PCs and maintaining their machines for optimal performance. Upon completion, learners will have the skills to confidently build, troubleshoot, and maintain their own PC, opening up opportunities for personal and professional growth in the IT sector. Course Description This course provides an in-depth look into the world of computer building, starting with the basics of understanding computer components and their roles in a functional system. Learners will explore key hardware, including processors, motherboards, RAM, and storage devices, while also learning the significance of peripheral devices. The course includes modules on software installation, networking, and even building a gaming PC. With a focus on providing clear, structured guidance, learners will gain a strong foundation in assembling and maintaining PCs, preparing them for various roles in the technology sector. The course is ideal for beginners and those looking to improve their technical expertise. Build Your Own PC: A Beginner's Guide Curriculum Module 01: Introduction to Computer & Building PC Module 02: Overview of Hardware and Parts Module 03: Building the Computer Module 04: Input and Output Devices Module 05: Software Installation Module 06: Computer Networking Module 07: Building a Gaming PC Module 08: Maintenance of Computers (See full curriculum) Who is this course for? Individuals seeking to understand how computers work and how to build one. Professionals aiming to enhance their IT skills for career development. Beginners with an interest in technology and computer systems. Hobbyists interested in assembling their own custom-built PCs. Career Path IT Support Technician Systems Administrator PC Hardware Specialist Network Technician Gaming PC Builder and Technician

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

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts 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. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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.

Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS 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 The Machine Learning Pipeline on AWS 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.

Duration 1 Days 6 CPD hours This course is intended for This class is intended for the following: Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform. Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports. Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists. Overview This course teaches students the following skills:Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.Employ BigQuery and Cloud Datalab to carry out interactive data analysis.Train and use a neural network using TensorFlow.Employ ML APIs.Choose between different data processing products on the Google Cloud Platform. This course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. Introducing Google Cloud Platform Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products. Compute and Storage Fundamentals CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline. Data Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc. Scaling Data Analysis Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset. Machine Learning Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs. Data Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing. Summary Why GCP? Where to go from here Additional Resources Additional course details: Nexus Humans Google Cloud Platform Big Data and Machine Learning Fundamentals 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 Google Cloud Platform Big Data and Machine Learning Fundamentals 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.