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Get ready for an exceptional online learning experience with the Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security bundle! This carefully curated collection of 20 premium courses is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations. The Python & Data Science package is dynamic, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you're looking to break into a new field or deepen your existing knowledge, the Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Securitypackage has something for everyone. As part of the Python & Data Science package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Python & Data Science bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth! This Bundle Comprises the Following Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security CPD-accredited courses: Course 01: Python Programming: Beginner To Expert Course 02: Data Science & Machine Learning with Python Course 03: Coding with Python 3 Course 04: Introduction to Coding With HTML, CSS, & Javascript Course 05: Python for Spatial Analysis in ArcGIS Course 06: Python Programming Bible | Networking, GUI, Email, XML, CGI Course 07: Business Intelligence and Data Mining Course 08: SQL for Data Science, Data Analytics and Data Visualization Course 09: Python Data Science with Numpy, Pandas and Matplotlib Course 10: Cloud Computing / CompTIA Cloud+ (CV0-002) Course 11: Cyber Security Awareness Training Course 12: Learn Ethical Hacking From A-Z: Beginner To Expert Course 13: Easy to Advanced Data Structures Course 14: R Programming for Data Science Course 15: GDPR UK Training Course 16: Career Development Plan Fundamentals Course 17: CV Writing and Job Searching Course 18: Learn to Level Up Your Leadership Course 19: Networking Skills for Personal Success Course 20: Ace Your Presentations: Public Speaking Masterclass What will make you stand out? Upon completion of this online Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security bundle, you will gain the following: CPD QS Accredited Proficiency with this Python & Data Science bundle After successfully completing the Python & Data Science bundle, you will receive a FREE PDF Certificate from REED as evidence of your newly acquired abilities. Lifetime access to the whole collection of learning materials of this Python & Data Science bundle The online test with immediate results You can study and complete the Python & Data Science bundle at your own pace. Study for the Python & Data Science bundle using any internet-connected device, such as a computer, tablet, or mobile device. Each course in this Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This Python & Data Science bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development. Embrace the future of learning with the Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security , a rich anthology of 30 diverse courses. Our experts handpick each course in the Python & Data Science bundle to ensure a wide spectrum of learning opportunities. This Python & Data Science bundle will take you on a unique and enriching educational journey. The Python & Data Science bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Python & Data Science bundle offers you the flexibility and convenience to learn at your own pace. Make the Python & Data Science package your trusted companion in your lifelong learning journey. CPD 200 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security bundle is perfect for: Beginners interested in technology: Individuals with little to no background in tech who are looking to start their careers in various IT domains. Aspiring Data Scientists and Analysts: Those aiming to build foundational and advanced skills in data science and machine learning. Future Software Developers: Individuals interested in learning Python and SQL for software development or database management. IT Security Enthusiasts: People who are keen on entering the field of cyber security and want to understand how to protect data in a digital environment. Cloud Computing Aspirants: Those looking to gain skills in cloud technologies and understand how to manage and deploy applications on the cloud. Requirements You are warmly invited to register for this Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security bundle. Please be aware that no formal entry requirements or qualifications are necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment. Career path Upon Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security course completion, you can expect to: Python Developer Data Analyst Machine Learning Engineer Database Administrator Cloud Solutions Architect Cyber Security Specialist Data Science Consultant Systems Analyst Network Security Engineer Research Scientist (AI/ML) Certificates 20 CPD Quality Standard Pdf Certificates Digital certificate - Included

The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals 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. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals 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. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00

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Course Overview Make the most of the plotting and AI capabilities of the world's benchmark programming language by taking this course to create Spatial Data Visualisation and Machine Learning in Python Level 4. Using the intuitive syntax available to you, you will be amazed at the results you can achieve with the power of its libraries and mapping potential for all manner of complex projects. This comprehensive Python tutorial is an excellent way to learn the important and potentially ground-breaking aspects of machine learning. With the benefit of expert guidance and step-by-step training, IT technology, you will be taken from quick installations to complex coding. You will learn how to become proficient with coding capabilities that will put you at the forefront of advanced programming techniques and the aptitude to envisage AI projects that will impress and be used for practical and useful purposes.  This best selling Spatial Data Visualization and Machine Learning in Python Level 4 has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Spatial Data Visualization and Machine Learning in Python Level 4 is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Spatial Data Visualization and Machine Learning in Python Level 4 is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The Spatial Data Visualization and Machine Learning in Python Level 4 is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn. After successfully completing the Spatial Data Visualization and Machine Learning in Python Level 4, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Spatial Data Visualization and Machine Learning in Python Level 4 will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Spatial Data Visualization and Machine Learning in Python Level 4 to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

This is a complete crash course about KNIME for beginners. Here, we will learn how to do data cleaning and data preparation without any code, using KNIME. We will also cover data visualization using Tableau and Power BI Desktop. Then we will understand the predictive analytics capabilities of KNIME and finally, cover machine learning in KNIME.

Step into the future of machine learning with the Machine Learning Model Using AWS SageMaker Canvas Course, designed to make predictive modelling approachable, even if you haven’t written a single line of code. This course takes you through the streamlined world of AWS SageMaker Canvas—Amazon’s no-code solution for machine learning—allowing you to build, train, and deploy models using intuitive drag-and-drop functionality. Whether you're analysing business trends, forecasting sales, or making sense of complex data, this course provides a smart entry point into machine learning logic through a cloud-based platform trusted across industries. You’ll explore the fundamentals of data preparation, model creation, and evaluation—all while using a secure and scalable AWS environment. The course is tailored for analysts, business professionals, and data-curious individuals who want to make informed decisions with the support of AI-driven insights. With engaging lessons and smartly structured modules, this course delivers a smooth and intelligent introduction to machine learning concepts—without the noise. Dive into predictive analytics with confidence, and let AWS SageMaker Canvas guide your journey into data-driven forecasting. Expert Support Dedicated tutor support and 24/7 customer support are available to all students with this premium quality course. Key Benefits Learning materials of the Design course contain engaging voiceover and visual elements for your comfort. Get 24/7 access to all content for a full year. Each of our students gets full tutor support on weekdays (Monday to Friday) Key Features CPD Accredited Quality License Endorsed Certificate Available Upon Course Completion Course Curriculum: Introduction to Machine Learning What is Machine Learning? Introduction to AWS What is Amazon Web Services (AWS)? Signing into AWS Console Introduction to SageMaker What is SageMaker, and how it is used for Machine Learning? What is SageMaker Canvas? Setup SageMaker Domain and User Setup Setup Data in S3 Buckets for use in SageMaker SageMaker Canvas Interface Walkthrough Navigating in SageMaker Canvas Project 1: Banknote Authentication Adding Training Data Building and Using Model for Prediction Predict Single & Batch Dataset Validating Accuracy of Batch Predictions Project 2: Spam SMS Detection Adding Train & Test Data Building and Using Model for Prediction Predicting Data and Validating Accuracy Project 3: Customer Churn Prediction Adding Data Building Model Performing & Validating Predictions Project 4: Wine Quality Prediction Adding & Joining Datasets Building Model Predicting Test Data Assignment White Wine Quality Prediction Other Important Features in SageMaker Canvas Versioning Congratulations & Next Steps Getting Datasets for Practice Getting Help on SageMaker Canvas Congratulations & Thankyou Exam and Assessment MCQ based test 60% Marks to pass Instant Assessment and Feedback Certification CPD Accredited PDF and Hardcopy Certificate Level 2 QLS Endorsed Hardcopy Certificate for Award in Machine Learning Model Using AWS SageMaker Canvas at QLS Level 2 CPD 120 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Machine Learning Model Using AWS SageMaker Canvas course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge. Requirements This Machine Learning Model Using AWS SageMaker Canvas course is open to all, with no formal entry requirements. Career path Upon successful completion of the Machine Learning Model Using AWS SageMaker Canvas Course, learners will be equipped with many indispensable skills and have the opportunity to grab. Certificates Certificate of completion Digital certificate - £9 Certificate of completion Hard copy certificate - £69 QLS Endorsed Certificate Hardcopy of this certificate of achievement endorsed by the Quality Licence Scheme can be ordered and received straight to your home by post, by paying - Within the UK: £69 International: £69 + £10 (postal charge) = £79 CPD Accredited Certification from One Education Hardcopy Certificate (within the UK): £15 Hardcopy Certificate (international): £15 + £10 (postal charge) = £25

The Machine Learning Model Using AWS SageMaker Canvas Course is designed for those who want to build smart solutions without diving nose-first into endless lines of code. With AWS SageMaker Canvas, you can develop machine learning models through an intuitive, no-code interface—perfect for users who prefer their data science with a touch less drama and a bit more drag-and-drop. This course walks you through the finer points of model creation, training, evaluation, and prediction, all through a clear and structured approach. Ideal for professionals who need results without wrestling with complex syntax, this course offers a neat introduction to the powerful capabilities of SageMaker Canvas. You’ll explore everything from dataset preparation to visualising outputs, learning how to navigate the platform efficiently while keeping things organised and clean. If your goal is to work smarter with machine learning—without turning it into a hobby—this is your chance to do just that, guided with a touch of wit and a sharp focus on what matters. Course Curriculum: Introduction to Machine Learning What is Machine Learning? Introduction to AWS What is Amazon Web Services (AWS)? Signing into AWS Console Introduction to SageMaker What is SageMaker, and how it is used for Machine Learning? What is SageMaker Canvas? Setup SageMaker Domain and User Setup Setup Data in S3 Buckets for use in SageMaker SageMaker Canvas Interface Walkthrough Navigating in SageMaker Canvas Project 1: Banknote Authentication Adding Training Data Building and Using Model for Prediction Predict Single & Batch Dataset Validating Accuracy of Batch Predictions Project 2: Spam SMS Detection Adding Train & Test Data Building and Using Model for Prediction Predicting Data and Validating Accuracy Project 3: Customer Churn Prediction Adding Data Building Model Performing & Validating Predictions Project 4: Wine Quality Prediction Adding & Joining Datasets Building Model Predicting Test Data Assignment White Wine Quality Prediction Other Important Features in SageMaker Canvas Versioning Congratulations & Next Steps Getting Datasets for Practice Getting Help on SageMaker Canvas Congratulations & Thankyou Exam and Assessment MCQ based test 60% Marks to pass Instant Assessment and Feedback Certification CPD Accredited PDF and Hardcopy Certificate Level 2 QLS Endorsed Hardcopy Certificate for Award in Machine Learning Model Using AWS SageMaker Canvas at QLS Level 2 CPD 120 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Machine Learning Model Using AWS SageMaker Canvas course is designed to enhance your expertise and boost your CV. Learn key skills and gain a certificate of achievement to prove your newly-acquired knowledge. Requirements This Machine Learning Model Using AWS SageMaker Canvas course is open to all, with no formal entry requirements. Career path Upon successful completion of the Machine Learning Model Using AWS SageMaker Canvas Course, learners will be equipped with many indispensable skills and have the opportunity to grab. Certificates Certificate of completion Digital certificate - £9 Certificate of completion Hard copy certificate - £69 QLS Endorsed Certificate Hardcopy of this certificate of achievement endorsed by the Quality Licence Scheme can be ordered and received straight to your home by post, by paying - Within the UK: £69 International: £69 + £10 (postal charge) = £79 CPD Accredited Certification from One Education Hardcopy Certificate (within the UK): £15 Hardcopy Certificate (international): £15 + £10 (postal charge) = £25

Overview In the era where information is abundant and decisions are driven by data, have you ever pondered, 'what is machine learning?' or 'what is data science?' Dive into the realm of 'Data Science & Machine Learning with R from A-Z,' a comprehensive guide to unravel these complexities. This course effortlessly blends the foundational aspects of data science with the intricate depths of machine learning algorithms, all through the versatile medium of R. As the digital economy booms, the demand for machine learning jobs continues to surge. Equip yourself with the proficiency to navigate this dynamic field and transition from being an inquisitive mind to a sought-after professional in the space of data science and machine learning with R. Learning Outcomes: Understand the foundational concepts of data science and machine learning. Familiarise oneself with the R environment and its functionalities. Master data types, structures, and advanced techniques in R. Acquire proficiency in data manipulation and visual representation using R. Generate comprehensive reports using R Markdown and design web applications with R Shiny. Gain a thorough understanding of machine learning methodologies and their applications. Gain insights into initiating a successful career in the data science sector. Why buy this Data Science & Machine Learning with R from A-Z course? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Data Science & Machine Learning with R from A-Z 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. Who is this Data Science & Machine Learning with R from A-Z course for? This course is ideal for Individuals keen on exploring the intricacies of machine learning and data science. Aspiring data analysts and scientists looking to specialise in Machine Learning with R. IT professionals aiming to diversify their skill set in the emerging data-driven market. Researchers seeking to harness the power of R for data representation and analysis. Academics and students aiming to bolster their understanding of modern data practices with R. Prerequisites This Data Science & Machine Learning with R from A-Z does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Data Science & Machine Learning with R from A-Z 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. Career path Data Scientist - Average salary range: £35,000 - £70,000 Per Annum Machine Learning Engineer - Average salary range: £50,000 - £80,000 Per Annum Data Analyst - Average salary range: £28,000 - £55,000 Per Annum R Developer - Average salary range: £30,000 - £60,000 Per Annum R Shiny Web Developer - Average salary range: £32,000 - £65,000 Per Annum Machine Learning Researcher - Average salary range: £40,000 - £75,000 Per Annum Course Curriculum Data Science and Machine Learning Course Intro Data Science and Machine Learning Introduction 00:03:00 What is Data Science 00:10:00 Machine Learning Overview 00:05:00 Who is This Course for 00:03:00 Data Science and Machine Learning Marketplace 00:05:00 Data Science and Machine Learning Job Opportunities 00:03:00 Getting Started with R Getting Started 00:11:00 Basics 00:06:00 Files 00:11:00 RStudio 00:07:00 Tidyverse 00:05:00 Resources 00:04:00 Data Types and Structures in R Unit Introduction 00:30:00 Basic Type 00:09:00 Vector Part One 00:20:00 Vectors Part Two 00:25:00 Vectors - Missing Values 00:16:00 Vectors - Coercion 00:14:00 Vectors - Naming 00:10:00 Vectors - Misc 00:06:00 Creating Matrics 00:31:00 List 00:32:00 Introduction to Data Frames 00:19:00 Creating Data Frames 00:20:00 Data Frames: Helper Functions 00:31:00 Data Frames Tibbles 00:39:00 Intermediate R Intermediate Introduction 00:47:00 Relational Operations 00:11:00 Conditional Statements 00:11:00 Loops 00:08:00 Functions 00:14:00 Packages 00:11:00 Factors 00:28:00 Dates and Times 00:30:00 Functional Programming 00:37:00 Data Import or Export 00:22:00 Database 00:27:00 Data Manipulation in R Data Manipulation in R Introduction 00:36:00 Tidy Data 00:11:00 The Pipe Operator 00:15:00 The Filter Verb 00:22:00 The Select Verb 00:46:00 The Mutate Verb 00:32:00 The Arrange Verb 00:10:00 The Summarize Verb 00:23:00 Data Pivoting 00:43:00 JSON Parsing 00:11:00 String Manipulation 00:33:00 Web Scraping 00:59:00 Data Visualization in R Data Visualization in R Section Intro 00:17:00 Getting Started 00:16:00 Aesthetics Mappings 00:25:00 Single Variable Plots 00:37:00 Two Variable Plots 00:21:00 Facets, Layering, and Coordinate Systems 00:18:00 Styling and Saving 00:12:00 Creating Reports with R Markdown Creating with R Markdown 00:29:00 Building Webapps with R Shiny Introduction to R Shiny 00:26:00 A Basic R Shiny App 00:31:00 Other Examples with R Shiny 00:34:00 Introduction to Machine Learning Machine Learning Part 1 00:22:00 Machine Learning Part 2 00:47:00 Starting A Career in Data Science Starting a Data Science Career Section Overview 00:03:00 Data Science Resume 00:04:00 Getting Started with Freelancing 00:05:00 Top Freelance Websites 00:05:00 Personal Branding 00:05:00 Importance of Website and Blo 00:04:00 Networking Do's and Don'ts 00:04:00 Assignment Assignment - Data Science & Machine Learning with R 00:00:00

Overview Uplift Your Career & Skill Up to Your Dream Job - Learning Simplified From Home! Kickstart your career & boost your employability by helping you discover your skills, talents, and interests with our special 2021 Data Science & Machine Learning with R from A-Z Course. You'll create a pathway to your ideal job as this course is designed to uplift your career in the relevant industry. It provides the professional training that employers are looking for in today's workplaces. The 2021 Data Science & Machine Learning with R from A-Z  Course is one of the most prestigious training offered at Skillwise and is highly valued by employers for good reason. This 2021 Data Science & Machine Learning with R from A-Z Course Course has been designed by industry experts to provide our learners with the best learning experience possible to increase their understanding of their chosen field. This 2021 Data Science & Machine Learning with R from A-Z Course, like every one of Skillwise's courses, is meticulously developed and well-researched. Every one of the topics is divided into elementary modules, allowing our students to grasp each lesson quickly. At Skillwise, we don't just offer courses; we also provide a valuable teaching process. When you buy a course from Skillwise, you get unlimited Lifetime access with 24/7 dedicated tutor support. Why buy this 2021 Data Science & Machine Learning with R from A-Z Course ? Lifetime access to the course forever Digital Certificate, Transcript, and student ID are all included in the price Absolutely no hidden fees Directly receive CPD Quality Standard-accredited qualifications after course completion Receive one-to-one assistance every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the 2021 Data Science & Machine Learning with R from A-Z Course 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 free. Original Hard Copy certificates need to be ordered at an additional cost of £8. Who is this course for? This 2021 Data Science & Machine Learning with R from A-Z  course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already work in relevant fields and want to polish their knowledge and skills. Prerequisites This 2021 Data Science & Machine Learning with R from A-Z Course does not require you to have any prior qualifications or experience. You can just enrol and start learning. This 2021 Data Science & Machine Learning with R from A-Z Course was made by professionals and it is compatible with all PCs, Macs, 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. Career path As this course comes with multiple courses included as a bonus, you will be able to pursue multiple occupations. This 2021 Data Science & Machine Learning with R from A-Z Course is a great way for you to gain multiple skills from the comfort of your home.

Machine learning isn’t just for self-driving cars and game-playing robots—it’s also helping identify whether someone’s wearing a mask or not. This course takes you through the full project lifecycle of building a Covid Mask Detector, using one of the most relevant applications of computer vision in recent years. Whether you're a data enthusiast or a coding hobbyist, you’ll enjoy diving into this machine learning challenge with a purpose that’s easy to relate to and timely. With clear, structured guidance, you'll explore how to prepare image data, train a neural network, and apply detection techniques—all from the comfort of your own screen. The content is delivered with clarity and a dash of wit, making the learning journey not just informative, but surprisingly enjoyable. You’ll walk away with confidence in building a full machine learning project, specifically tailored for image classification, and yes—taught in plain, human English (no jargon jungle here). Whether you're brushing up your Python skills or simply curious how AI spots face masks, this course offers an insightful experience in smart automation, delivered with a professional tone and just enough character to keep you grinning as you code. Learning Outcomes: Develop a Covid mask detector using machine learning. Master OpenCV, a popular computer vision library. Build models with TensorFlow. Design and build the app, upload files, and deploy it on AWS. Gain valuable experience in machine learning app development. The Hands-on Machine Learning Project - Covid Mask Detector course is designed to provide you with the skills and knowledge needed to develop a mask detector using deep learning. In this course, you'll learn how to master OpenCV, an open-source computer vision library used for image processing and face detection. You'll also learn how to build and train a deep learning model using Tensorflow, a popular machine learning framework. The course is perfect for aspiring data scientists, machine learning engineers, and developers who want to make a positive impact on society by contributing to public health and safety efforts. By the end of this course, you'll have a deep understanding of how to develop a mask detector app that can be used to detect whether individuals are wearing masks in public spaces. You'll be able to master OpenCV and use it to preprocess and detect faces in images. You'll also learn how to build and train a deep learning model using TensorFlow and how to deploy your mask detector app on AWS. This course provides a unique opportunity for individuals to gain real-world experience in developing cutting-edge technology that can make a positive impact on society. Hands on Machine Learning Project - Covid Mask Detector Course Curriculum Section 01: Introduction Introduction to Course Section 02: Mastering OpenCV Getting System Ready Read and Write Images Resize and Crop Working with Shapes Working with Text Section 03: Pre-Requisite for Face Detection Pre-Requisite for Face Detection Detect the Face Section 04: Deep Learning with Tensorflow Introduction to Deep Learning with Tensorflow Model Building Training the Mask Detector Saving the Best Model Basic Front End Design of App File Upload Interface for App App Prep App Build and Testing AWS Deployment AWS Deployment Continued 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? Aspiring data scientists. Machine learning engineers. Developers interested in machine learning app development. Anyone interested in developing technology to fight the pandemic. Professionals looking to upskill in the latest technology. Career path Data Scientist: £40,000 to £80,000 per year. Machine Learning Engineer: £55,000 to £90,000 per year. Artificial Intelligence Developer: £40,000 to £80,000 per year. Computer Vision Engineer: £40,000 to £80,000 per year. Deep Learning Engineer: £55,000 to £90,000 per year. Certificates Certificate of completion Digital certificate - £9 You can apply for a CPD Accredited PDF Certificate at the cost of £9.