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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

Register on the English Literature Diploma today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion. The English Literature Diploma is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The English Literature Diploma Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the English Literature Diploma, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content Elizabethan Drama and Shakespeare's Hamlet The History of Drama 00:10:00 Elizabethan Drama 00:12:00 William Shakespeare 00:06:00 Hamlet: A Synopsis of the Play 00:04:00 Hamlet: An Anlaysis of the Play 00:19:00 Hamlet's Soliloquies 00:05:00 Jacobean Literature and the Metaphysical Poets Jacobean Literature Poetry and Prose 00:12:00 Puritanism & The English Civil War 00:11:00 Metaphysical Poetry 00:08:00 George Herbert 00:05:00 Andrew Marvell 00:07:00 John Milton 00:08:00 The Rise of the Novel and Satire The Restoration & Glorious Revolution 00:06:00 The Novel 00:08:00 Early Novelists: Defoe, Richardson, and Fielding 00:11:00 English Satire: John Dryden & Alexander Pope 00:06:00 Jonathan Swift & Gulliver's Travels 00:10:00 Swift's A Modest Proposal 00:08:00 The Emergence of American Literature Colonialism & John Smith 00:07:00 Pilgrim Writers 00:10:00 The Revolutionary Period Writers 00:07:00 The Knickerbocker Era: Washington Irving 00:14:00 Frontier Fiction: James Fenimore Cooper 00:10:00 The New England Renaissance The Westward Expansion 00:04:00 Transcendentalism: Ralph Waldo Emerson 00:10:00 Transcendentalism: Henry David Thoreau 00:08:00 Anti-Transcendentalism: Nathaniel Hawthorne 00:05:00 Nathaniel Hawthorne and The Scarlet Letter 00:08:00 Herman Melville 00:05:00 Edgar Allan Poe 00:06:00 Poe's 'The Cask of Amontillado' 00:05:00 Poe's 'The Raven' 00:07:00 The American Civil War Era and the Gilded Age The American Civil War Era & Harriet Beecher Stowe 00:07:00 Walt Whitman 00:09:00 Whitman's 'Drum Taps' 00:06:00 Emily Dickinson 00:09:00 The Gilded Age 00:07:00 Local Color Writing: Mark Twain 00:08:00 Tom Sawyer & Huck Finn 00:11:00 A Connecticut Yankee in King Arthur's Court 00:07:00 Women Writers of the 19th Century Female Writers & The French Revolution 00:12:00 Mary Wollstonecraft 00:09:00 Helen Maria Williams 00:08:00 Unitarianism 00:04:00 Harriet Martineau 00:12:00 Anna Laetitia Barbauld 00:06:00 Lucy Aikin 00:05:00 British Romantic Poets and the Poetic Imagination The Napoleonic Wars 00:08:00 The Romantic Period 00:05:00 William Blake 00:08:00 William Wordsworth 00:08:00 Samuel Taylor Coleridge 00:08:00 Percy Bysshe Shelley 00:08:00 John Keats 00:09:00 Lord George Gordon Byron 00:07:00 Victorian Novels Gothic Novels 00:10:00 Jane Austin's Pride and Prejudice 00:14:00 Victorian Novels 00:09:00 Emily Brontë and Wuthering Heights 00:11:00 Charles Dickens and Great Expectations 00:15:00 The Imagist Movements after World War I Anti-Victorianism & Edwardian England 00:05:00 Imagism & Gertruid Stein 00:05:00 Post War Literature & Ezra Pound 00:10:00 T.S. Eliot 00:13:00 William Carlos Williams 00:05:00 Archibald MacLeish 00:05:00 Modernist Fiction Modernist Fiction 00:07:00 Henry James 00:07:00 James Joyce 00:07:00 Joyce's 'Araby' 00:19:00 Joyce's Ulysses 00:07:00 Virginia Woolf 00:06:00 Virgina Woolf's To the Lighthouse 00:12:00 The Lost Generation Ernest Hemingway and the Lost Generation 00:05:00 Hemingway's The Sun Also Rises 00:10:00 F. Scott Fitzgerald 00:04:00 Fitzgerald's The Great Gatsby 00:08:00 ee cummings 00:06:00 Robert Lowell 00:05:00 The Beat Generation & Allen Ginsberg 00:11:00 Resources Resources - English Literature Diploma 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Level 1 Certificate in Real Estate Principles and Practice Don't miss the chance to elevate your career with our Level 1 Certificate in Real Estate Principles and Practice. Seize the opportunity to master the market, outshine the competition, and become the tech-savvy real estate agent everyone needs. Enrol now - the property ladder won't climb itself Learning Outcomes: Gain a comprehensive understanding of the essential duties and responsibilities that underpin the role of a Real Estate Agents. Delineate the Spectrum of Real Estate Agent Specialisations. Learn to effectively organize and manage a Real Estate Agent's day-to-day activities using industry-standard tools and practices, ensuring a seamless flow of transactions and client interactions. Develop critical skills to appraise current listing strategies and employ innovative tactics that resonate with contemporary buyers and market demands. Construct and refine negotiation plans that are robust, client-centered, and designed to thrive in the fast-paced real estate environment. Leverage cutting-edge technology in real estate Operations; embrace and integrate the latest technological advancements in your practice to streamline processes More Benefits: LIFETIME access Device Compatibility Free Workplace Management Toolkit Level 1 Certificate in Real Estate Principles and Practice Course Syllabus Understanding Estate Agent: Master the roles, requirements, and competitive landscape of being a Real Estate Agent. Different Types of Estate Agents: Understand the roles of Rental Agents, Property Negotiators, and Property Managers in the Real Estate Agent profession. Getting Started on the Job: Learn organizational and professional strategies for success as a Real Estate Agent. Listing Property: Finding the Sellers: Acquire skills for listing appointments, handling offers, and closing deals as a Real Estate Agent. Valuation Process: Master the valuation, evaluation, and appraisal processes, and maintain client relationships as a Real Estate Agent. Real Estate Negotiations: Understand the negotiation process and the importance of effective communication for a Real Estate Agent.

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Animal Care with Dog First Aid and Dog Grooming Diploma Embark on a rewarding journey into the realm of Animal Care with our specialised diploma. This course equips you with the essential Animal Care skills and knowledge to provide top-notch care for animals, particularly dogs. Setting you on a fulfilling Animal Care career path, this diploma is your gateway to becoming a consummate professional in the field of Animal Care. Learning Outcomes: Master the key principles underpinning effective Animal Care. Gain practical insights into basic animal health, a cornerstone in quality Animal Care. Understand the fundamentals of animal psychology and how they interface with optimal Animal Care. Learn the critical rules governing Dog First Aid, a vital skill set in Animal Care. Discover the health and aesthetic benefits of Dog Grooming, another crucial aspect of Animal Care. Acquire practical skills for basic dog handling and safety, essential for responsible Animal Care. More Benefits: LIFETIME access Device Compatibility Free Workplace Management Toolkit Key Modules from Animal Care with Dog First Aid and Dog Grooming Diploma: Principles of Animal Care: Develop a strong foundational understanding of the ethics and methodologies involved in responsible Animal Care. Basic Animal Health in Animal Care: Delve into the vital components of animal health, focusing on preventative care and early diagnosis as key elements in Animal Care. Animal Psychology and Its Role in Animal Care: Explore the complexities of animal behavior and psychology, understanding its direct relevance to Animal Care. Rules of Dog First Aid in Animal Care: Equip yourself with life-saving Dog First Aid rules and procedures to handle emergencies effectively within the context of Animal Care. Benefits of Dog Grooming in Animal Care: Learn about the aesthetic and health benefits of Dog Grooming, along with professional techniques, to offer comprehensive Animal Care. Basic Dog Handling and Safety in Animal Care: Grasp essential dog handling techniques to ensure both human and animal safety as part of comprehensive Animal Care.

Are you ready to be at the helm, steering the ship into a realm where data is the new gold? In the infinite world of data, where information spirals at breakneck speed, lies a universe rich in potential and discovery: the domain of Data Science and Visualisation. This 'Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3' course unravels the wonders of extracting meaningful insights using Python, the worldwide leading language of data experts. Harnessing the strength of Python, you'll delve deep into data analysis, experience the finesse of visualisation tools, and master the art of Machine Learning. The need to understand, interpret, and act on this data has become paramount, with vast amounts of data increasing the digital sphere. Envision a canvas where raw numbers are transformed into visually compelling stories, and machine learning models foretell future trends. This course provides a meticulous pathway for anyone eager to learn the data representation paradigms backed by Python's robust libraries. Dive into a curriculum rich with analytical explorations, visual artistry, and machine learning predictions. Learning Outcomes Understanding the foundations and functionalities of Python, focusing on its application in data science. Applying various Python libraries like NumPy and Pandas for effective data analysis. Demonstrating proficiency in creating detailed visual narratives using tools like matplotlib, Seaborn, and Plotly. Implementing Machine Learning algorithms in Python using scikit-learn, ranging from regression models to clustering techniques. Designing and executing a holistic data analysis and visualisation project, encapsulating all learned techniques. Exploring advanced topics, encompassing recommender systems and natural language processing with Python. Attaining the confidence to independently analyse complex data sets and translate them into actionable insights.   Video Playerhttps://studyhub.org.uk/wp-content/uploads/2021/03/Data-Science-and-Visualisation-with-Machine-Learning.mp400:0000:0000:00Use Up/Down Arrow keys to increase or decrease volume. Why buy this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 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. Who is this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 course for? Aspiring data scientists aiming to harness the power of Python. Researchers keen to enrich their analytical and visualisation skills. Analysts aiming to add machine learning to their toolkit. Developers striving to integrate data analytics into applications. Business professionals desiring data-driven decision-making capabilities. Career path Data Scientist: £55,000 - £85,000 Per Annum Machine Learning Engineer: £60,000 - £90,000 Per Annum Data Analyst: £30,000 - £50,000 Per Annum Data Visualisation Specialist: £45,000 - £70,000 Per Annum Natural Language Processing Specialist: £65,000 - £95,000 Per Annum Business Intelligence Developer: £40,000 - £65,000 Per Annum Prerequisites This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 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. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £85 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Visualisation with Machine Learning 00:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

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Entry Requirement: This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. CPD Certificate from Course Gate  At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £9 and the hard copy for £15. Also, you can order both PDF and hardcopy certificates for £22. Career path This course opens a new door for you to enter the relevant job market and also gives you the opportunity to acquire extensive knowledge along with required skills to become successful. You will be able to add our qualification to your CV/resume which will help you to stand out in the competitive job industry. Course Curriculum Microsoft Word 2016 Modify User Information 00:15:00 Share a Document 00:30:00 Work with Comments 00:15:00 Compare Document Changes 00:15:00 Review a Document 00:15:00 Merge Document Changes 00:15:00 Coauthor Documents 00:15:00 Add Captions 00:15:00 Add Cross-References 00:15:00 Add Bookmarks 00:15:00 Add Hyperlinks 00:15:00 Insert Footnotes and Endnotes 00:15:00 Add Citations 00:30:00 Insert a Bibliography 00:15:00 Insert Blank and Cover Pages 00:15:00 Insert an Index 00:30:00 Insert a Table of Contents 00:30:00 Insert an Ancillary Table 00:15:00 Manage Outlines 00:30:00 Create a Master Document 00:30:00 Suppress Information 00:30:00 Set Editing Restrictions 00:30:00 Add a Digital Signature to a Document 00:15:00 Restrict Document Access 00:15:00 Create Forms 00:30:00 Manipulate Forms 00:15:00 Form Data Conversion 00:15:00 Create a New Document Version 00:30:00 Compare Document Versions 00:15:00 Merge Document Versions 00:15:00 Activities - Microsoft Word 2016 Advanced 00:00:00 Microsoft Excel 2016 Update Workbook Properties 00:15:00 Activity-Update Workbook Properties 00:05:00 Create and Edit a Macro 00:15:00 Activity-Create and Edit a Macro 00:05:00 Apply Conditional Formatting 00:15:00 Activity-Apply Conditional Formatting 00:05:00 Add Data Validation Criteria 00:15:00 Activity-Add Data Validation Criteriaty 00:05:00 Trace Cells 00:15:00 Activity-Trace Cells 00:05:00 Troubleshoot Invalid Data and Formula Errors 00:15:00 Activity-Troubleshoot Invalid Data and Formula Errors 00:05:00 Watch and Evaluate Formulas 00:15:00 Activity-Watch and Evaluate Formulas 00:05:00 Create a Data List Outline 00:15:00 Activity-Create a Data List Outline 00:05:00 Create Sparklines 00:15:00 Activity_Create Sparklines 00:05:00 Create Scenarios 00:15:00 Activity-Create Scenarios 00:05:00 Perform a What-If Analysis 00:15:00 Activity-Perform a What-If Analysis 00:05:00 Perform a Statistical Analysis with the Analysis ToolPak 00:15:00 Activity-Perform a Statistical Analysis with the Analysis ToolPak 00:05:00 Create Interactive Data with Power View 00:15:00 Activity-Create Interactive Data with Power View 00:05:00 Consolidate Data 00:15:00 Activity-Consolidate Data 00:05:00 Link Cells in Different Workbooks 00:15:00 Activity-Link Cells in Different Workbooks 00:05:00 Merge Workbooks 00:15:00 Activity-Merge Workbooks 00:05:00 Export Excel Data 00:15:00 Activity-Export Excel Data 00:05:00 Import a Delimited Text File 00:14:00 Activity- Import a Delimited Text File 00:05:00 Integrate Excel Data with the Web 00:15:00 Activity-Integrate Excel Data with the Web 00:05:00 Create a Web Query 00:15:00 Activity-Create a Web Query 00:05:00 Import and Export XML Data 00:15:00 Activity-Import and Export XML Data 00:05:00 Activities and Exercise Files - Microsoft Excel 2016 Advanced 00:00:00 Microsoft PowerPoint 2016 Customize the User Interface 01:30:00 Set PowerPoint 2016 Options 00:45:00 Create and Manage Sections 01:00:00 Modify Slide Masters and Slide Layouts 01:15:00 Add Headers and Footers 00:30:00 Modify the Notes Master and the Handout Master 00:45:00 Create SmartArt 00:45:00 Modify SmartArt 01:00:00 Add Audio to a Presentation 00:45:00 Add Video to a Presentation 01:00:00 Customize Animations and Transitions 01:00:00 Add Comments to a Presentation 00:45:00 Review a Presentation 00:45:00 Store and Share Presentations on the Web 01:30:00 Annotate a Presentation 00:45:00 Set Up a Slide Show 00:45:00 Create a Custom Slide Show 00:30:00 Add Hyperlinks and Action Buttons 00:45:00 Record a Presentation 00:15:00 Secure a Presentation 01:30:00 Present a Slide Show Online 01:00:00 Create a Video or a CD 00:45:00 Activities - Microsoft PowerPoint 2016 Advanced 00:00:00 Microsoft Outlook 2016 Insert Advanced Characters and Objects 01:30:00 Modify Message Settings, Properties, and Options 01:30:00 Use Automatic Replies 01:30:00 Sort Messages 00:45:00 Filter Messages 01:00:00 Organize Messages 02:00:00 Search Messages 01:30:00 Manage Junk Mail 01:00:00 Manage Your Mailbox 02:00:00 Manage Advanced Calendar Options 00:45:00 Manage Additional Calendars 01:00:00 Manage Meeting Responses 00:45:00 Assign and Manage Tasks 01:00:00 Edit an Electronic Business Card 01:00:00 Manage Advanced Contact Options 01:30:00 Forward Contacts 00:30:00 Export Contacts 01:00:00 Delegate Access to Mail Folders 01:00:00 Share Your Calendar 00:30:00 Share Your Contacts 00:15:00 Back Up Outlook Items 00:45:00 Change Data File Settings 00:45:00 Configure E-mail Message Security Settings 00:30:00 Activities - Microsoft Outlook 2016 Advanced 00:00:00 Microsoft Access 2016 Add Controls to Forms 01:00:00 Set Form Controls 01:00:00 Create Subforms 00:30:00 Organize Information with Tab Pages 00:30:00 Enhance Navigation with Forms 00:30:00 Format a Form 01:00:00 Apply Conditional Formatting 00:30:00 Field Validation 00:30:00 Form and Record Validation 00:30:00 Create a Macro 01:00:00 Restrict Records Using a Condition 00:30:00 Validate Data Using a Macro 00:30:00 Automate Data Entry Using a Macro 00:30:00 Convert a Macro to VBA 00:30:00 Link Tables to External Data Sources 00:30:00 Manage a Database 00:30:00 Determine Object Dependency 00:30:00 Document a Database 00:30:00 Analyze the Performance of a Database 00:30:00 Split a Database for Multiple User Access 00:30:00 Implement Security 00:30:00 Set Passwords 00:30:00 Convert an Access Database to an ACCDE File 00:30:00 Package a Database with a Digital Signature 00:30:00 Create a Database Switchboard 01:00:00 Modify a Database Switchboard 00:30:00 Set Startup Options 00:30:00 Mock Exam Mock Exam - Microsoft Office 2016 Advanced 00:20:00 Final Exam Final Exam - Microsoft Office 2016 Advanced 00:20:00 Certificate and Transcript Order Your Certificates or Transcripts 00:00:00

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