160 Arithmetic courses in Bristol delivered On Demand

Boost Your Career with Apex Learning and Get Noticed By Recruiters in this Hiring Season! Get Hard Copy + PDF Certificates + Transcript + Student ID Card worth £160 as a Gift - Enrol Now With a single payment you will gain access to Data Science Course Bundle 2022 including 10 Career development courses, original hardcopy certificate, transcript and a student ID card which will allow you to get discounts on things like music, food, travel and clothes etc. The world is one big data bank, and data science is one of the most demanding professional sectors of the present era. The analytical and programming-oriented field of data science has limited resources for candidates to learn and develop skills, which is why you need our highly advanced [course_title] course.With step-by-step interactive video content, our training will equip you with extensive knowledge and expertise in data science, including machine learning. This bundle course offers an opportunity to foster your career opportunities with an expert-level understanding of data science and become skilful in this industry. Take this course anywhere and at any time. Don't let your lifestyle limit your learning or your potential. Data Science Course Bundle 2022 will provide you with the CPD certificate that you'll need to succeed. Gain experience online and interact with experts. This can prove to be the perfect way to get noticed by a prospective employer and stand out from the crowd. Data Science Course Bundle 2022 has been rated and reviewed highly by our learners and professionals alike. We have a passion for teaching, and it shows. All of our courses have interactive online modules that allow studying to take place where and when you want it to. The only thing you need to take Data Science Course Bundle 2022 is Wi-Fi and a screen. You'll never be late for class again. Experienced tutors and mentors will be there for you whenever you need them, and solve all your queries through email and chat boxes. Benefits you'll get choosing Apex Learning for this Course: One payment, but lifetime access to 11 CPD courses Certificates, student ID for the title course included in a one-time fee Full tutor support available from Monday to Friday Free up your time - don't waste time and money travelling for classes Accessible, informative modules taught by expert instructors Learn at your ease - anytime, from anywhere Study the course from your computer, tablet or mobile device CPD accredited course - improve the chance of gaining professional skills Gain valuable knowledge without leaving your home What other courses are included with this Course? Level 2 Microsoft Office Essentials Microsoft Teams Leadership & Management Diploma Working from Home Essentials Mental Health and Working from Home Online Meeting Management Effective Communication Skills Time Management Report Writing Emotional Intelligence and Human Behaviour Curriculum ***Data Science Course Bundle 2022*** Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview Set-up the Environment for the Course (lecture 1) Set-up the Environment for the Course (lecture 2) Two other options to setup environment Python Essentials Python data types Part 1 Python Data Types Part 2 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) Python Essentials Exercises Overview Python Essentials Exercises Solutions Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. NumPy arrays, built-in methods, array methods and attributes. Indexing, slicing, broadcasting & boolean masking Arithmetic Operations & Universal Functions Exercises Overview Exercises Solutions Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. Pandas Introduction Pandas Data Structures - Series Pandas Data Structures - DataFrame Handling Missing Data Data Wrangling - Combining, merging, joining Groupby Useful Methods and Operations Project 1 (Overview) Customer Purchases Data Project 1 (Solutions) Customer Purchases Data Project 2 (Overview) Chicago Payroll Data Project 2 (Solutions Part 1) Chicago Payroll Data Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach Matplotlib Essentials - Exercises Overview Matplotlib Essentials - Exercises Solutions Python for Data Visualization using Seaborn Introduction & Installation Distribution Plots Categorical Plots (Part 1) Categorical Plots (Part 2) Axis Grids Matrix Plots Regression Plots Controlling Figure Aesthetics Exercises Overview Exercise Solutions Python for Data Visualization using pandas Pandas Built-in Data Visualization Pandas Data Visualization Exercises Overview Panda Data Visualization Exercises Solutions Python for interactive & geographical plotting using Plotly and Cufflinks Interactive & Geographical Plotting (Part 1) Interactive & Geographical Plotting (Part 2) Interactive & Geographical Plotting Exercises (Overview) Interactive & Geographical Plotting Exercises (Solutions) Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types….. Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff Linear Regression Model - Hands-on (Part 1) Linear Regression Model Hands-on (Part 2) Good to know! How to save and load your trained Machine Learning Model! Linear Regression Model (Insurance Data Project Overview) Linear Regression Model (Insurance Data Project Solutions) Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. Logistic Regression Model - Hands-on (Part 1) Logistic Regression Model - Hands-on (Part 2) Logistic Regression Model - Hands-on (Part 3) Logistic Regression Model - Hands-on (Project Overview) Logistic Regression Model - Hands-on (Project Solutions) Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality …. K Nearest Neighbors - Hands-on K Nearest Neighbors (Project Overview) K Nearest Neighbors (Project Solutions) Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. Decision Tree and Random Forests - Hands-on (Part 1) Decision Tree and Random Forests (Project Overview) Decision Tree and Random Forests (Project Solutions) Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) Support Vector Machines - Hands-on (SVMs) Support Vector Machines (Project 1 Overview) Support Vector Machines (Project 1 Solutions) Support Vector Machines (Optional Project 2 - Overview) Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method ….. K Means Clustering - Hands-on K Means Clustering (Project Overview) K Means Clustering (Project Solutions) Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) Principal Component Analysis (PCA) - Hands-on Principal Component Analysis (PCA) - (Project Overview) Principal Component Analysis (PCA) - (Project Solutions) Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance Python for Recommender Systems - Hands-on (Part 1) Python for Recommender Systems - - Hands-on (Part 2) Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) NLP-Challenges, Data Sources, Data Processing ….. Feature Engineering and Text Preprocessing in Natural Language Processing NLP - Tokenization, Text Normalization, Vectorization, BoW…. BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … Pipeline feature to assemble several steps for cross-validation… How will I get my Certificate? After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (Previously it was £10 * 11 = £110) Hard Copy Certificate: Free (For The Title Course) If you want to get hardcopy certificates for other courses, generally you have to pay £20 for each. But this Fall, Apex Learning is offering a Flat 50% discount on hard copy certificates, and you can get each for just £10! P.S. The delivery charge inside the U.K. is £3.99 and the international students have to pay £9.99. CPD 20 CPD hours / points Accredited by CPD Quality Standards Who is this course for? There is no experience or previous qualifications required for enrolment on this Data Science Course Bundle 2022. It is available to all students, of all academic backgrounds. Requirements Our Data Science Course Bundle 2022 is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible on tablets and smartphones so you can access your course on wifi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having this CPD certificate will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Certificates Certificate of completion Digital certificate - Included

Overview of MySql Masterclass The digital world has changed how businesses work in the UK, and database management is now a key part of every successful company. MySQL is one of the most popular tools for this job, used by over 40% of websites around the world. In the UK, big names like BBC, Sky, and many tech startups in London’s Silicon Roundabout rely on MySQL. The MySql Masterclass is designed to help people learn the skills needed to build a strong career in this growing field. Database experts in the UK earn an average salary of £45,000 each year, making it a smart career choice. The MySql Masterclass has 41 helpful modules that start with the basics and move up to more advanced topics. Students will learn how to create databases, write MySQL commands, use joins, and manage stored procedures. The course also teaches how to make systems run faster and safer with performance and security tools. Other important topics include handling JSON data, using full-text search, and working with replication. These are all things that modern companies look for in a database specialist. This MySql Masterclass is made for beginners and gives them the knowledge they need to handle real business databases. It helps students build strong skills in design, optimisation, and administration. The UK’s tech industry is worth £150 billion a year, and the MySql Masterclass helps learners get ready for job opportunities in many areas like retail, health, and finance. Learning Outcomes By the end of the MySql Masterclass, learners will be able to: Build and manage MySQL databases from the ground up Use SELECT, INSERT, UPDATE, and DELETE commands with confidence Work with advanced joins, unions, and stored procedures Design and use indexes, views, and full-text search for better performance Set up MySQL replication, backups, and basic system administration Handle JSON data and manage time zone settings within databases Who is this course for? Aspiring Database Administrators who want full MySQL training to manage databases, user access, and backups in organisations needing strong data systems. Software Developers who want to improve their backend development by learning MySQL queries, stored procedures, and database performance skills. Data Analysts who need advanced SQL to pull useful data from complex tables using joins, group functions, and reporting tools. IT Professionals looking to build database knowledge, including how to configure, tune, and fix issues in MySQL systems. Career Changers with little or no tech background who want to start in database roles, learning from the basics to advanced MySQL tasks. Process of Evaluation After studying the MySql Masterclass Course, your skills and knowledge will be tested with an MCQ exam or assignment. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the MySql Masterclass Course, you can order your CPD Accredited Digital / PDF Certificate for £5.99. (Each) Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. (Each) Shipping Charges: Inside the UK: £3.99 International: £10.99 Requirements You don’t need any educational qualification or experience to enrol in the MySql Masterclass course. Career Path Completing this MySql Masterclass course could lead to rewarding jobs like: Database Administrator – £35K to £65K per year MySQL Developer – £30K to £55K per year Data Analyst – £25K to £45K per year Backend Developer – £35K to £60K per year Database Consultant – £40K to £70K per year Course Curriculum: MySql Masterclass Module 1: Introduction on MySQL 01:00:00 Module 2: Data Types 00:51:00 Module 3: SELECT Statements 00:59:00 Module 4: Backticks 00:15:00 Module 5: NULL 00:18:00 Module 6: Limit and Offset 00:13:00 Module 7: Creating databases 00:18:00 Module 8: Using Variables 00:25:00 Module 9: Comment MySQL 00:14:00 Module 10: INSERT Statements 00:29:00 Module 11: DELETE Statements 00:21:00 Module 12: UPDATE Statements 00:20:00 Module 13: ORDER BY Clause 00:08:00 Module 14: Group By 00:18:00 Module 15: Errors in MySQL 00:10:00 Module 16: Joins 00:37:00 Module 17: Joins continued 00:11:00 Module 18: UNION 00:18:00 Module 19: Arithmetic 00:20:00 Module 20: String operations 00:33:00 Module 21: Date and Time Operations 00:08:00 Module 22: Handling Time Zones 00:07:00 Module 23: Regular Expressions 00:19:00 Module 24: VIEWS 00:20:00 Module 25: Table Creation 00:23:00 Module 26: ALTER TABLE 00:23:00 Module 27: Drop Table 00:05:00 Module 28: MySQL LOCK TABLE 00:10:00 Module 29: Error codes 00:08:00 Module 30: Stored routines (procedures and functions) 00:29:00 Module 31: Indexes and Keys 00:24:00 Module 32: Full-Text search 00:18:00 Module 33: PREPARE Statements 00:09:00 Module 34: JSON 00:11:00 Module 35: Extract values from JSON type 00:05:00 Module 36: MySQL Admin 00:08:00 Module 37: TRIGGERS 00:12:00 Module 38: Configuration and tuning 00:07:00 Module 39: Events 00:08:00 Module 40: ENUM 00:09:00 Module 41: Collations, Transactions, Log files, Replication, Backup 00:41:00

Web applications shape the digital world we interact with daily—from the simplest online form to complex platforms driving entire businesses. This course is designed for those who are eager to specialise in building, maintaining, and optimising web-based systems. Whether you're familiar with HTML or curious about frameworks and database logic, this course steadily guides you through the layers of modern web application architecture. You’ll explore front-end and back-end development concepts, delve into programming essentials, and gain insights into how dynamic web environments function behind the scenes. If you're aiming to deepen your understanding and sharpen your development capabilities in a focused, structured format—this is your route. Delivered entirely online, it's tailored for learners who appreciate flexibility without compromising depth. 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) Course Curriculum: Section 01: CSS Introduction Introduction How to ask a great questions Introduction CSS Choosing Code Editor Installing Code Editor (Sublime Text) CSS Syntax Creating a first page with CSS Style Section 02: CSS Basic Inline CSS Internal CSS External CSS CSS Classes CSS IDs Colors Backgrounds Floating Positioning Margins Padding Borders Section 03: CSS Intermediate Styling Text Aligning Text Styling Links Font Family Font Styles Applying Google Fonts Box Model Icons Tables Navigation-Menu Dropdowns Section 04: CSS Advanced Advanced Selectors Forms Website Layout Rounded Corners Color Keywords Animations Pseudo Classes Gradients Shadows Calculations Creating Responsive Page Section 05: CSS Expert Button Styles Pagination Multiple Columns Image Reflection UI - UX Design Social Media Icons External CSS Style adding Section 06: PHP Introduction What is PHP Installing XAMPP for PHP, MySQL and Apache Installing Code Editor(Visual Studio Code) Creating PHP Project on XAMPP Hello World Program Section 07: PHP Basic Variables Echo and Print Data Types Numbers Boolean Arrays Multi-Dimensional Array Sorting Arrays Constants Section 08: PHP Strings Strings String Formatting String Methods Coding Exercise Solution for Coding Exercise Section 09: PHP Operators Arithmetic operators Assignment operators Comparison operators Increment - decrement operators Logical operators Ternary operator Section 10: PHP Decision making system If statement If-else statement If-elseif-else statement Switch-case statement Section 11: PHP Control flow statements Flow Chart While loop Do-while loop For loop For each loop Coding Exercise Solution for Coding Exercise Section 12: PHP Functions Creating a Function Function with Arguments Default Argument Function return values Call-by-value Call-by-reference Section 13: PHP Super globals $_POST Method $_GET Method Section 14: PHP Advanced Form Handling Date and Time Include Require Sessions File Reading File Upload Section 15: PHP Object oriented programming[OOPs] What is OOP Class and Objects Constructor Destructor Access Modifiers Inheritance Method overriding Abstract Class Interface Section 16: PHP - MySQL Application [CRUD] MySQL Basic PhpMyAdmin Creating Database and Table Database Connection PHP Form Create records PHP Form Reading records PHP Form Update Data PHP Form Delete records Section 17: PHP Real world code forms Registration Form MD5 Algorithm for Encrypting Sha Algorithm Login Form Section 18: PHP Validations On Submit Validation Input Numeric Validation Login Form Validation Form Server-side all Data Validation Form Server-side Validation Section 19: PHP Error handling Try-throw-catch Try-throw-catch-finally Section 20: MYSQL introduction Overview of Databases MySQL Installation MySQL Workbench Installation Connecting to MySQL using Console Section 21: MySQL basic Overview of Challenges SQL Statement Basic SELECT Statement SELECT DISTINCT Column AS Statement COUNT function Section 22: MySQL filtering data SELECT WHERE Clause - One SELECT WHERE Clause - Two ORDER BY LIMIT BETWEEN IN Operator LIKE and ILIKE Section 23: MySQL functions Overview of GROUP BY Aggregation function SUM() Aggregation MIN() and MAX() GROUP BY - One GROUP BY - Two HAVING Clause Section 24: MySQL joins Overview Assessment Overview of JOINS Introduction to JOINS AS Statement table INNER JOIN FULL Outer Join LEFT Outer JOIN RIGHT JOIN Union Section 25: MySQL advanced commands Advanced SQL Commands Timestamps EXTRACT from timestamp Mathematical Functions String Functions SUBQUERY Section 26: MySQL structure creation Database and Tables Data Types Primary key and Foreign key Create Table in SQL Script Section 27: MySQL data queries Insert Update Delete Section 28: MySQL structure queries Alter Table Drop Table Section 29: MySQL constraints NOT NULL Constraint UNIQUE Constraint Section 30: MySQL backup and restore Overview of Databases and Tables Backup database using phpMyAdmin Restoring a Database Course Assessment To simplify the procedure of evaluation and accreditation for learners, we provide an automated assessment system. Upon completion of an online module, you will immediately be given access to a specifically crafted MCQ test. The results will be evaluated instantly, and the score will be displayed for your perusal. For each test, the pass mark will be set to 60%. When all tests have been successfully passed, you will be able to order a certificate endorsed by the Quality Licence Scheme. 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). Who is this course for? This Web Applications for Specialisation on Development 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 Web Applications for Specialisation on Development course is open to all, with no formal entry requirements. Career path Upon successful completion of The Web Applications for Specialisation on Development Course, learners will be equipped with many indispensable skills and have the opportunity to grab.

This ultimate course to kickstart your Python journey from scratch. This comprehensive course covers all the essential concepts of Python, providing explanations, examples, and practical implementations. Designed with beginners in mind, our goal is to help you learn and master Python by building a variety of projects.

Embark on this course in web development with HTML, CSS, JS, and React for a comprehensive training program designed to empower beginners and experienced designers alike with the essential skills needed to create captivating and dynamic websites. Explore the power of React.js, HTML5, CSS3, JavaScript, and build scalable components with React.

What you will learn from this course? Gain comprehensive knowledge about digital electric circuits and intelligent electrical devices Understand the core competencies and principles of digital electric circuits and intelligent electrical devices Explore the various areas of digital electric circuits and intelligent electrical devices Know how to apply the skills you acquired from this course in a real-life context Become a confident and expert electrician, electrical engineer or technicians Course Highlights Course Type: Self-paced online course Duration: 1 to 2 hours Tutor Support: Full tutor support is included Customer Support: 24/7 customer support is available Master In Digital Electric Circuits Course Master the skills you need to propel your career forward in digital electric circuits and intelligent electrical devices. This course will equip you with the essential knowledge and skillset that will make you a confident electrician, electrical engineer or technicians and take your career to the next level. This comprehensive master in digital electric course is designed to help you surpass your professional goals. The skills and knowledge that you will gain through studying this master in digital electric course will help you get one step closer to your professional aspirations and develop your skills for a rewarding career. This comprehensive course will teach you the theory of effective digital electric circuits and intelligent electrical devices practice and equip you with the essential skills, confidence and competence to assist you in the digital electric circuits and intelligent electrical devices industry. You'll gain a solid understanding of the core competencies required to drive a successful career in digital electric circuits and intelligent electrical devices. This course is designed by industry experts, so you'll gain knowledge and skills based on the latest expertise and best practices. This extensive course is designed for electrician, electrical engineer or technicians or for people who are aspiring to specialise in digital electric circuits and intelligent electrical devices. Enrol in this master in digital electric course today and take the next step towards your personal and professional goals. Earn industry-recognised credentials to demonstrate your new skills and add extra value to your CV that will help you outshine other candidates. Who is this Course for? This comprehensive master in digital electric course is ideal for anyone wishing to boost their career profile or advance their career in this field by gaining a thorough understanding of the subject. Anyone willing to gain extensive knowledge on this digital electric circuits and intelligent electrical devices can also take this course. Whether you are a complete beginner or an aspiring professional, this course will provide you with the necessary skills and professional competence, and open your doors to a wide number of professions within your chosen sector. Entry Requirements This master in digital electric course has no academic prerequisites and is open to students from all academic disciplines. You will, however, need a laptop, desktop, tablet, or smartphone, as well as a reliable internet connection. Assessment This master in digital electric course assesses learners through multiple-choice questions (MCQs). Upon successful completion of the modules, learners must answer MCQs to complete the assessment procedure. Through the MCQs, it is measured how much a learner could grasp from each section. In the assessment pass mark is 60%. Advance Your Career This master in digital electric course will provide you with a fresh opportunity to enter the relevant job market and choose your desired career path. Additionally, you will be able to advance your career, increase your level of competition in your chosen field, and highlight these skills on your resume. Study Plex Subscription Study plex also provides a subscription option that allows you unlimited access to more than 700+ CPD courses for learning. You only need to spend £79 to take advantage of this fantastic offer, and you'll get an unlimited subscription for a full year. Additionally, you can cancel your membership from your account at any time by getting in touch with our friendly and devoted customer care team. Visit our subscriptions page for more details if you're interested. Why you should train with Study Plex? At Study Plex, you will have the chance to build social, technical and personal skills through a combination of extensive subjects tailored according to your interest. Along with receiving comprehensive knowledge and transferable skills, there are even more reasons o be involved with us, which include: Incredible Customer Support: We offer active customer service in the form of live chat, which you can access 24/7 Expert Tutor Support: You'll have access to our devoted and dedicated tutor support with all of our courses whenever you need it. Price Justified by Quality: We ensure that you will have the best experience possible for the price you are paying for the course. Money-back Guarantee: We provide a money-back guarantee if you are not satisfied with the course's quality. There is a 14-day time limit on this option (according to the terms and conditions). Instalment Facility: If your course costs more than £50, you can pay in three instalments using the instalment option. Satisfaction Guarantee: Our courses are designed to meet your demands and expectations by all means. Recognised Accreditation This course is accredited by continuing professional development (CPD). CPD UK is globally recognised by employers, professional organisations, and academic institutions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. What is CPD? Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements. Benefits of CPD Improve your employment prospects Boost your job satisfaction Promotes career advancement Enhances your CV Provides you with a competitive edge in the job market Demonstrate your dedication Showcases your professional capabilities What is IPHM? The IPHM is an Accreditation Board that provides Training Providers with international and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation is a guarantee of quality and skill. Benefits of IPHM It will help you establish a positive reputation in your chosen field You can join a network and community of successful therapists that are dedicated to providing excellent care to their client You can flaunt this accreditation in your CV It is a worldwide recognised accreditation What is Quality Licence Scheme? This course is endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. Benefits of Quality License Scheme Certificate is valuable Provides a competitive edge in your career It will make your CV stand out Course Curriculum Introduction Introduction 00:05:00 Numbering Systems 00:28:00 Binary Arithmetic 00:19:00 Logic Gates 00:30:00 Flip-Flops 00:23:00 Counters & Shift Registers 00:12:00 Adders 00:10:00 Assessment Assessment - Master In Digital Electric Circuits 00:10:00 Obtain Your Certificate Order Your Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

Overview This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with Python is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 2 sections • 90 lectures • 10:24:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:08:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:07:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

Overview This comprehensive course on Python Programming: Beginner To Expert will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python Programming: Beginner To Expert comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast-track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Python Programming: Beginner To Expert. It is available to all students, of all academic backgrounds. Requirements Our Python Programming: Beginner To Expert is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 18 sections • 121 lectures • 15:27:00 total length •Intro To Python Section Overview: 00:05:00 •What is Python Programming: 00:10:00 •Who is This Course For: 00:05:00 •Python Programming Marketplace: 00:06:00 •Python Job Opportunities: 00:05:00 •How To Land a Python Job Without a Degree: 00:08:00 •Python Programmer Job Roles: 00:09:00 •Python from A-Z Course Structure: 00:04:00 •Getting Familiar with Python Section Overview: 00:06:00 •Installing Python on Windows: 00:10:00 •Anaconda and Jupyter Notebooks Part 1: 00:08:00 •Anaconda and Jupyter Notebooks Part 2: 00:16:00 •Comments: 00:05:00 •Python Syntax: 00:02:00 •Line Structure: 00:03:00 •Line Structure Exercise: 00:07:00 •Joining Lines: 00:05:00 •Multiple Statements on a Single Line: 00:05:00 •Indentation: 00:08:00 •Basic Data Types Section Overview: 00:08:00 •String Overview: 00:10:00 •String Manipulation: 00:07:00 •String Indexing: 00:04:00 •String Slicing: 00:08:00 •Printing: 00:10:00 •Python Variables: 00:08:00 •Integers and Floats: 00:08:00 •Booleans: 00:05:00 •Mini Project 1 : Letter Counter: 00:20:00 •Python Operators Section Overview: 00:04:00 •Comparison Operators: 00:09:00 •Arithmetic Operators: 00:08:00 •Assignment Operators: 00:04:00 •Logical Operators: 00:13:00 •Identity Operators: 00:05:00 •Membership Operators: 00:02:00 •Bitwise Operators: 00:08:00 •Python Advanced Data Types Section Overview: 00:11:00 •Sets: 00:06:00 •List Overview: 00:05:00 •List Slicing and Indexing: 00:04:00 •Tuples: 00:02:00 •When to use each one?: 00:05:00 •Compound Data Types: 00:03:00 •Dictionaries: 00:11:00 •Control Flow Part 1 Section Overview: 00:15:00 •Intro to Control Flow: 00:01:00 •Basic Conditional Statements: 00:14:00 •More Conditional Statements: 00:05:00 •For Loops: 00:10:00 •While Loops: 00:12:00 •Control Flow Part 2 Section Overview: 00:02:00 •Break Statements: 00:08:00 •Continue Statements: 00:05:00 •Zip Function: 00:07:00 •Enumerate Function: 00:04:00 •List Comprehension: 00:04:00 •Python Functions Section Overview: 00:03:00 •Intro to Functions: 00:02:00 •Python help Function: 00:03:00 •Defining Functions: 00:09:00 •Variable Scope: 00:08:00 •Doc Strings: 00:04:00 •User Input and Error Handling Section Overview: 00:02:00 •Introduction to error handling: 00:03:00 •User Input: 00:04:00 •Syntax Errors: 00:04:00 •Exceptions: 00:11:00 •Handling Exceptions Part 1: 00:08:00 •Handling Exceptions Part 2: 00:08:00 •Python Advanced Functions Section Overview: 00:05:00 •Lambda Functions: 00:05:00 •Functions args and kwargs: 00:10:00 •Iterators: 00:08:00 •Generators and Yield: 00:12:00 •Map Function: 00:14:00 •Filter Function: 00:08:00 •Python Scripting and Libraries Section Overview: 00:05:00 •What is a script: 00:01:00 •What is an IDE: 00:17:00 •What is a text editor?: 00:12:00 •From Jupyter Notebook to VScode Part 1: 00:15:00 •From Jupyter Notebook to VScode Part 2: 00:05:00 •Importing Scripts: 00:03:00 •Standard Libraries: 00:04:00 •Third Party Libraries: 00:06:00 •NumPy Section Overview: 00:04:00 •Intro to NumPy: 00:04:00 •Why use NumPy?: 00:04:00 •NumPy Arrays: 00:10:00 •Reshaping, Accessing, and Modifying: 00:07:00 •Slicing and Copying: 00:06:00 •Inserting, Appending, and Deleting: 00:10:00 •Array Logical Indexing: 00:04:00 •Broadcasting: 00:08:00 •Intro to Pandas: 00:17:00 •Pandas Series: 00:17:00 •Pandas Series Manipulation: 00:17:00 •Pandas DataFrame: 00:17:00 •Pandas DataFrame Manipulation: 00:13:00 •Dealing with Missing Values: 00:10:00 •Functional vs OOP: 00:06:00 •OOP Key Definitions: 00:04:00 •Create your First Class: 00:12:00 •How to Create and Use Objects: 00:06:00 •How to Modify Attributes: 00:12:00 •Python Decorators: 00:27:00 •Property Decorator: 00:09:00 •Class Method Decorator: 00:07:00 •Static Methods: 00:10:00 •Inheritance from A to Z: 00:21:00 •Python Career Section Overview: 00:06:00 •Getting Started with Freelancing: 00:09:00 •Building A Brand: 00:12:00 •Personal Branding: 00:13:00 •Importance of Having Website/Blog: 00:04:00 •Do's And Don'ts Of Networking: 00:06:00 •Top Freelance Websites: 00:08:00 •Creating A Python Developer Resume: 00:06:00 •Resources - Python Programming: Beginner To Expert: 00:00:00 •Assignment - Python Programming: Beginner To Expert: 00:00:00

Course Overview Julia is one of the highest performing programming languages. The Tutorials - The Julia Programming Language course is designed to train you in this valuable programing language. In this course, you will get equipped with the skills to code in Julia and add available skill sets to your resume. The Tutorials - The Julia Programming Language course will introduce you to the basic principles of Julia programming language. In this course, you will learn the steps to install Julia. You will get introduced to Julia variables, integers, sign function and more. The course will provide you with lectures based on Cher types and strings. You will start to understand all the functions of this programming language. The course will give you an extensive understanding of Julia Dict and type. By the end of the course, you will pick up all the valuable information and skills to use this language. Learn the ins and outs of Julia programming language from the Tutorials - The Julia Programming Language course. This course will increase your abilities and boost your employability in the relevant industry. Learning Outcomes Understand the process of installing Julia Familiarize yourself with Julia variables and functions Enrich your understanding of Cher types and strings Learn the details of conditional and non-conditional blocks Grasp the skills essential for Juila Dict operations Who is this course for? This Tutorials - The Julia Programming Language course is suitable for programmers, data scientists, or individuals who want to learn a new programming language. 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. Certification After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24. PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry-leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognized accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path The Tutorials - The Julia Programming Language course is a useful qualification to possess and would be beneficial for any related profession or industry such as: Programmer Data Scientist Introduction Learning Julia 00:01:00 Installing Julia 00:06:00 Installing Juno 00:04:00 Begin Dancing with Julia Julia Variables 00:05:00 Julia Integers and Floats 00:05:00 Julia Convert and Comparisons 00:03:00 Rounding Operations 00:05:00 Division Function 00:04:00 Sign Function and Power 00:05:00 Complex and Rational Numbers 00:05:00 Julia Chars and Strings Julia Char type 00:03:00 String Literals 00:02:00 Extract Char and String 00:02:00 Concatenate and Interpolate 00:03:00 isEqual and Comparisons 00:04:00 Find and OccursIn 00:05:00 Repeat and Regex 00:04:00 Julia Functions Julia Function Object 00:04:00 Function Return Type 00:06:00 Functions as Objects and Arguments 00:04:00 Operators as Functions 00:02:00 Anonymous Function 00:04:00 Function Arguments Tuples 00:05:00 Unpacking Tuples 00:02:00 Varargs 00:03:00 Optional Arguments 00:03:00 Keyword Arguments 00:03:00 Conditional and Non-Conditional Blocks Do Block 00:04:00 Compound Expression 00:02:00 If Statements 00:05:00 If Statement Return Value 00:02:00 Short Circuit Evaluation 00:03:00 Loops and Exceptions For Loop 00:02:00 Control and Nest For Loops 00:03:00 Exceptions 00:03:00 Julia Try and Catch 00:03:00 While Loop 00:02:00 Variable Scope 00:05:00 Arrays in Julia Arrays 00:04:00 Pop and Push 00:03:00 Multidimensional Arrays 00:03:00 Copying Arrays 00:02:00 Julia Dicts Dicts 00:02:00 Dict Operations 00:02:00 More Dict Operations 00:04:00 More Cool Dict Operations 00:03:00 One More Cool Dict Operation 00:04:00 Broadcasting 00:04:00 Julia Types Julia Types 00:01:00 Verify and Specify Types 00:03:00 More Verification and Specification 00:05:00 Julia Methods 00:02:00 Composite Types 00:05:00 Mutable Structs 00:02:00 Constructor Functions 00:04:00 Modules and Packages Julia Modules 00:02:00 Using Packages 00:04:00 User Defined Modules 00:05:00 Working with Text Files Reading Text Files 00:04:00 Writing To Text Files 00:03:00 Writing Collections To Files 00:02:00 Julia Date and Time Date And Time 00:03:00 Date Queries 00:02:00 Date Arithmetic 00:03:00 Meta Programming in Julia Meta Programming 00:02:00 Quoted Expression 00:04:00 Macros 00:02:00 REST APIs and MySQL Using Genie 00:04:00 Payloads and POST Requests 00:05:00 Julia and MySQL 00:08:00 DataFrames and Plots DataFrames 00:05:00 Plotting with Plots 00:02:00 Where to go from here 00:01:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. 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 00:04:00 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 Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 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 00:06:00 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 - Data Science & Machine Learning with Python 00:00:00