67 Azure Machine Learning courses delivered Online

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.

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.

This comprehensive course covers all Scrum principles and frameworks necessary to help participants understand how to guide a team and manage projects in a fast-paced agile environment. The course is meant for professionals who want to attain the certification of Scrum Master with deep insight into how AI can be utilized in increasing the effectiveness of agile practices. In addition to mastery of the core Scrum methodology, participants will be taken through state-of-the-art advancements in AI and machine learning in order to understand how these technologies can automate routine tasks, enhance decision-making, and continuous improvement. Real-world case studies and hands-on exercises will illustrate how to practically apply AI within Scrum to realize high efficiency and innovation for teams. Whether for enhancing one's career as a Scrum Master or the integration of AI into Agile practices, this course provides that ideal combination of conceptual theory and practical skills, assuring success in today's technology-driven world. Key Highlights: Certified Scrum Master training with AI applications Case studies in the real world about integrating AI in Scrum Hands-on projects to implement AI-driven tools and methodologies Workflow optimization techniques that ensure better collaboration of agile teams, with speeding up project delivery by the power of AI. Ideal for Scrum Masters, Agile Coaches, Product Owners, and tech pros looking to stay ahead.

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SQL Azure is Microsoft's cloud database service. Based on SQL Server database technology and built on Microsoft's Windows Azure cloud computing platform, SQL Azure enables organizations to store relational data in the cloud and quickly scale the size of their databases up or down as business needs change.  This Azure - SQL focuses primarily on Azure SQL Database as a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software. Learn how to deploy relational and non-relational databases in the cloud and explore the breadth of Azure's data services, from a single database instance to a massive scale data warehouse for working with Big data. You'll gain an understanding of how to configure firewall rules, manage log-ins and users, as well as roles and permissions, perform a database restore, and generally secure an Azure SQL database. Finally, you'll be introduced to Azure SQL Data Warehouse, a fully managed and scalable cloud service, that is compatible with other Azure offerings, such as Machine Learning and Data Factory, as well as existing SQL Server tools. What Will I Learn? Overview and provision Azure SQL Connect to Azure SQL DB and Migrate DB to Azure Work with SQL security and metrics Configure SQL DB auditing Copy and export a database Use DB Self-Service Restore and SQL DB Geo-Replication Who is the target audience? Students wanting an understanding of Azure SQL and to build their skills. Requirements Basic understanding of Azure and SQL concepts Introduction Introduction FREE 00:02:00 Azure SQL Azure SQL 00:02:00 Provisioning Azure SQL 00:06:00 Connecting to Azure SQL DB 00:08:00 Migrating to Azure DB 00:06:00 Understanding SQL Security 00:03:00 Understanding SQL DB Metrics and Auditing 00:05:00 Backing Up and Protecting your Database 00:05:00 Using SQL DB Geo-Replication 00:03:00 Conclusion Course Recap 00:02:00 Course Certification

Artificial neural networks (ANNs) are the most powerful machine learning algorithms available today. They are capable of learning complex relationships in data, and they have been used to achieve state-of-the-art results in a wide variety of fields, including image recognition, natural language processing, and speech recognition. The Future of Machine Learning is Here! This Project on Deep Learning - Artificial Neural Network course will teach you how to build and train ANNs from scratch. You will learn about the different components of an ANN, such as the input layer, hidden layers, and output layer. You will also learn about the different activation functions that can be used in ANNs, and you will see how to optimise ANNs for different tasks. In addition to the theoretical concepts, you will also get experience with ANNs. You will work on a project where you will build an ANN to classify images. You will use the TensorFlow library to build your ANN, and you will see how to train your ANN on a dataset of images. By the end of this Project on Deep Learning - Artificial Neural Network course, you will have a deep understanding of ANNs and how to use them. You will be able to build your own ANNs to solve a variety of problems. You will also be able to use the TensorFlow library to build and train ANNs. So what are you waiting for? Enrol in this course today and start learning about the future of machine learning!   Learning Outcomes: Through this comprehensive course, you should be able to: Understand the fundamental concepts of deep learning and artificial neural networks. Install and configure an artificial neural network framework. Preprocess and structure data for optimal model performance. Encode data effectively for neural network training and predictions. Build and deploy artificial neural networks for real-world applications. Address data imbalance challenges and optimise model accuracy.   Who is this course for? This Project on Deep Learning - Artificial Neural Network course is ideal for: Data scientists and machine learning practitioners seeking to expand their knowledge. Software engineers interested in leveraging deep learning techniques. Students pursuing a career in artificial intelligence and machine learning. Professionals looking to enhance their skills in neural network development. Individuals with a passion for exploring advanced machine learning techniques.   Career Path  Our course will prepare you for a range of careers, including: Deep Learning Engineer: £40,000 - £100,000 per year. Machine Learning Researcher: £45,000 - £120,000 per year. Data Scientist: £50,000 - £110,000 per year. Artificial Intelligence Specialist: £55,000 - £130,000 per year. Software Engineer (specialising in AI): £45,000 - £100,000 per year. Research Scientist (Machine Learning): £50,000 - £120,000 per year. AI Consultant: £60,000 - £150,000 per year. Certification After studying the course materials of the Project on Deep Learning - Artificial Neural Network (ANNs) there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Prerequisites This Project on Deep Learning - Artificial Neural Network (ANNs) does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Project on Deep Learning - Artificial Neural Network (ANNs) was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.   Course Curriculum Section 01: Introduction Introduction of Project 00:03:00 Section 02: ANN Installation Setup Environment for ANN 00:11:00 ANN Installation 00:09:00 Section 03: Data Preprocessing Import Libraries and Data Preprocessing 00:11:00 Data Preprocessing 00:07:00 Data Preprocessing Continue 00:10:00 Section 04: Data Encoding Data Exploration 00:10:00 Encoding 00:07:00 Encoding Continue 00:06:00 Preparation of Dataset for Training 00:04:00 Section 05: Steps to Build ANN Steps to Build ANN Part 1 00:06:00 Steps to Build ANN Part 2 00:06:00 Steps to Build ANN Part 3 00:06:00 Steps to Build ANN Part 4 00:09:00 Section 06: Predictions and Imbalance-Learn Predictions 00:11:00 Predictions Continue 00:08:00 Resampling Data with Imbalance-Learn 00:09:00 Resampling Data with Imbalance-Learn Continue 00:08:00

Embark on a captivating journey into the world of artificial intelligence with our course, 'Machine Learning Basics.' This voyage begins with an immersive introduction, setting the stage for an exploration into the intricate and fascinating realm of machine learning. Envision yourself unlocking the mysteries of algorithms and data patterns, essential skills in today's technology-driven landscape. The course offers a comprehensive foray into the core principles of machine learning, starting from the very basics and gradually building to more complex concepts, making it an ideal path for beginners and enthusiasts alike. As you delve deeper, each section unravels a vital component of machine learning. Grasp the essentials of regression analysis, understand the role of predictors, and navigate through the functionalities of Minitab, a key tool in data analysis. Journey through the structured world of regression trees and binary logistic regression, and master the art of classification trees. The course also emphasizes the importance of data cleaning and constructing robust data models, culminating in the achievement of learning success. This course is not just an educational experience; it's a gateway to the future of data science and AI. Learning Outcomes Comprehend the basic principles and applications of machine learning. Develop proficiency in regression analysis and predictor identification. Gain practical skills in Minitab for data analysis. Understand and apply regression and classification trees. Acquire expertise in data cleaning and model creation. Why choose this Machine Learning Basics course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Machine Learning Basics course for? Novices eager to delve into machine learning. Data enthusiasts looking to enhance their analytical skills. Professionals in IT and related fields expanding their expertise. Academics and students in computer science and data studies. Career changers interested in the field of data science and AI. Career path Data Analyst - £30,000 to £55,000 Machine Learning Engineer - £40,000 to £80,000 AI Developer - £35,000 to £75,000 Business Intelligence Analyst - £32,000 to £60,000 Research Scientist (Machine Learning) - £45,000 to £85,000 Software Engineer (AI Specialization) - £38,000 to £70,000 Prerequisites This Machine Learning Basics does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Machine Learning Basics was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Section 01: Introduction Introduction to Supervised Machine Learning 00:06:00 Section 02: Regression Introduction to Regression 00:13:00 Evaluating Regression Models 00:11:00 Conditions for Using Regression Models in ML versus in Classical Statistics 00:21:00 Statistically Significant Predictors 00:09:00 Regression Models Including Categorical Predictors. Additive Effects 00:20:00 Regression Models Including Categorical Predictors. Interaction Effects 00:18:00 Section 03: Predictors Multicollinearity among Predictors and its Consequences 00:21:00 Prediction for New Observation. Confidence Interval and Prediction Interval 00:06:00 Model Building. What if the Regression Equation Contains 'Wrong' Predictors? 00:13:00 Section 04: Minitab Stepwise Regression and its Use for Finding the Optimal Model in Minitab 00:13:00 Regression with Minitab. Example. Auto-mpg: Part 1 00:17:00 Regression with Minitab. Example. Auto-mpg: Part 2 00:18:00 Section 05: Regression Trees The Basic idea of Regression Trees 00:18:00 Regression Trees with Minitab. Example. Bike Sharing: Part1 00:15:00 Regression Trees with Minitab. Example. Bike Sharing: Part 2 00:10:00 Section 06: Binary Logistics Regression Introduction to Binary Logistics Regression 00:23:00 Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC 00:20:00 Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1 00:16:00 Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2 00:18:00 Section 07: Classification Trees Introduction to Classification Trees 00:12:00 Node Splitting Methods 1. Splitting by Misclassification Rate 00:20:00 Node Splitting Methods 2. Splitting by Gini Impurity or Entropy 00:11:00 Predicted Class for a Node 00:06:00 The Goodness of the Model - 1. Model Misclassification Cost 00:11:00 The Goodness of the Model - 2 ROC. Gain. Lit Binary Classification 00:15:00 The Goodness of the Model - 3. ROC. Gain. Lit. Multinomial Classification 00:08:00 Predefined Prior Probabilities and Input Misclassification Costs 00:11:00 Building the Tree 00:08:00 Classification Trees with Minitab. Example. Maintenance of Machines: Part 1 00:17:00 Classification Trees with Miitab. Example. Maintenance of Machines: Part 2 00:10:00 Section 08: Data Cleaning Data Cleaning: Part 1 00:16:00 Data Cleaning: Part 2 00:17:00 Creating New Features 00:12:00 Section 09: Data Models Polynomial Regression Models for Quantitative Predictor Variables 00:20:00 Interactions Regression Models for Quantitative Predictor Variables 00:15:00 Qualitative and Quantitative Predictors: Interaction Models 00:28:00 Final Models for Duration and TotalCharge: Without Validation 00:18:00 Underfitting or Overfitting: The 'Just Right Model' 00:18:00 The 'Just Right' Model for Duration 00:16:00 The 'Just Right' Model for Duration: A More Detailed Error Analysis 00:12:00 The 'Just Right' Model for TotalCharge 00:14:00 The 'Just Right' Model for ToralCharge: A More Detailed Error Analysis 00:06:00 Section 10: Learning Success Regression Trees for Duration and TotalCharge 00:18:00 Predicting Learning Success: The Problem Statement 00:07:00 Predicting Learning Success: Binary Logistic Regression Models 00:17:00 Predicting Learning Success: Classification Tree Models 00:09:00

Description Get a comprehensive overview of Automation and Log Analytics in Azure in the MS Azure - Automation and Log Anayltics course. Microsoft Azure is a cloud computing platform that offers more than 600 Azure services. The course especially focuses on two major Azure services: Automation and Log Analytics. Azure Automation gives you the ability to automate frequent, time-consuming, and error-prone cloud management tasks. On the other hand, Log Analytics helps you to collect, correlate, and visualize structured and unstructured data. Using Log Analytics, you can monitor cloud and on-premises environments to maintain availability and performance. You will also monitor and systems to maintain availability and performance. Throughout the course, you will learn how to use these two services for making your office work easy. You will explore the strategies of creating an automation account, Runbooks, and creating and viewing OMS workspace. Finally, you will be familiarized with the Azure security Centre. Assessment: This course does not involve any MCQ test. Students need to answer assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Once the answers are submitted, the instructor will check and assess the work. Certification: After completing and passing the course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24. Who is this Course for? MS Azure - Automation and Log Anayltics is certified by CPD Qualifications Standards and CiQ. This makes it perfect for anyone trying to learn potential professional skills. As there is no experience and qualification required for this course, it is available for all students from any academic background. Requirements Our MS Azure - Automation and Log Anayltics is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation. Career Path After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. Introduction Introduction FREE 00:02:00 Azure Automation Azure Automation 00:03:00 Creating Azure Automation Accounts 00:03:00 Automation Assets 00:04:00 Creating Runbooks 00:07:00 Log Analytics Log Analytics Overview 00:03:00 Creating and Viewing OMS Workspaces 00:10:00 Azure Security Center Intro to Azure Security Center 00:04:00 Detection as a Service 00:02:00 ASC Investigations 00:06:00 Conclusion Course Recap 00:03:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Machine learning doesn’t need to be intimidating—especially when you’ve got R on your side. This course offers a clear, well-paced approach to learning machine learning using one of the most respected languages in data science. Whether you’re brushing up on your statistics or stepping into data modelling, the content is structured to help you think algorithmically and act analytically, without feeling overwhelmed by jargon or complexity. From regression techniques to classification methods and everything in-between, this course covers the core building blocks that give machine learning its predictive power. R is not just a programming language here—it’s your analytical toolkit. If terms like decision trees, clustering, and support vector machines sound like something out of a sci-fi novel, don’t worry—by the end, they’ll feel like familiar companions. Whether you’re analysing patterns or building predictive models, this course offers a confident route through the world of machine learning with an R-flavoured lens. Ask ChatGPT Learning Outcomes: Understand the basics of machine learning and its implementation using R. Develop the skills to build simple and multiple linear regression models. Learn how to use R to analyse datasets and develop predictive models. Understand the concept of dummy variables and the backward elimination approach. Learn how to make accurate predictions using machine learning algorithms and extract valuable insights from data. If you're looking to expand your knowledge in data analysis and machine learning, then the "Learn Machine Learning with R" course is perfect for you. This comprehensive course comprises two sections, each designed to help you gain an in-depth understanding of machine learning concepts, starting from the very basics. You'll learn about linear regression, the equation for the algorithm, and how to make simple linear regression models. Additionally, you'll dive into multiple linear regression, dummy variable concepts, and predictions over the year. With the help of this course, you'll be able to analyse datasets, develop predictive models, and extract valuable insights from them, using R. Learn Machine Learning with R Course Curriculum Section 01: Linear Regression and Logistic Regression Working on Linear Regression Equation Making the Regression of the Algorithm Basic Types of Algorithms predicting the Salary of the Employee Making of Simple Linear Regression Model Plotting Training Set and Work Section 02: Understanding Dataset Multiple Linear Regression Dummy Variable Concept Predictions Over Year Difference Between Reference Elimination Working of the Model Working on Another Dataset Backward Elimination Approach Making of the Model with Full and Null 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? Students or professionals looking to develop their data analysis and machine learning skills. Individuals interested in pursuing a career in data science or machine learning. Anyone interested in understanding how to extract insights from data. Programmers looking to learn machine learning implementation using R. Beginners interested in learning the basics of machine learning. Career path Data analyst: £30,000 to £50,000 Machine learning engineer: £45,000 to £85,000 Data scientist: £40,000 to £80,000 Business analyst: £30,000 to £55,000 Research analyst: £25,000 to £45,000 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.

R isn’t just for statisticians hiding behind graphs — it’s a powerful tool for machine learning, and this course shows you exactly how to put it to work. Designed for curious minds with an interest in data prediction, modelling, and pattern recognition, this course guides you through the essentials of machine learning using R. You’ll explore key techniques like classification, regression, and clustering, all explained in plain English (with just the right amount of code). If you’ve ever wondered how machines “learn” from data — without trying to make them smarter than your laptop needs to be — this course breaks it down with clear logic and no unnecessary flair. Study entirely online, at your own pace, with structured content focused on helping you understand how algorithms behave, why they do what they do, and how to make them behave a little better. Whether you're dipping your toes or deepening your skills, it’s all here, minus the fluff and drama. Learning Outcomes: Understand the basics of machine learning and its implementation using R. Develop the skills to build simple and multiple linear regression models. Learn how to use R to analyse datasets and develop predictive models. Understand the concept of dummy variables and the backward elimination approach. Learn how to make accurate predictions using machine learning algorithms and extract valuable insights from data. If you're looking to expand your knowledge in data analysis and machine learning, then the "Learn Machine Learning with R" course is perfect for you. This comprehensive course comprises two sections, each designed to help you gain an in-depth understanding of machine learning concepts, starting from the very basics. You'll learn about linear regression, the equation for the algorithm, and how to make simple linear regression models. Additionally, you'll dive into multiple linear regression, dummy variable concepts, and predictions over the year. With the help of this course, you'll be able to analyse datasets, develop predictive models, and extract valuable insights from them, using R. ⊱⊱ Learn Machine Learning with R Course Curriculum Section 01: Linear Regression and Logistic Regression Working on Linear Regression Equation Making the Regression of the Algorithm Basic Types of Algorithms predicting the Salary of the Employee Making of Simple Linear Regression Model Plotting Training Set and Work Section 02: Understanding Dataset Multiple Linear Regression Dummy Variable Concept Predictions Over Year Difference Between Reference Elimination Working of the Model Working on Another Dataset Backward Elimination Approach Making of the Model with Full and Null 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 £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? Students or professionals looking to develop their data analysis and machine learning skills. Individuals interested in pursuing a career in data science or machine learning. Anyone interested in understanding how to extract insights from data. Programmers looking to learn machine learning implementation using R. Beginners interested in learning the basics of machine learning. Career path Data analyst: £30,000 to £50,000 Machine learning engineer: £45,000 to £85,000 Data scientist: £40,000 to £80,000 Business analyst: £30,000 to £55,000 Research analyst: £25,000 to £45,000 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.