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Overview This comprehensive course on Ruby on Rails 5 - Become a Professional Developer will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Ruby on Rails 5 - Become a Professional Developer 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 Ruby on Rails 5 - Become a Professional Developer. It is available to all students, of all academic backgrounds. Requirements Our Ruby on Rails 5 - Become a Professional Developer 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 23 sections • 225 lectures • 16:47:00 total length •Course Introduction: 00:06:00 •The Application We're Going to Build: 00:09:00 •Section Introduction gg: 00:01:00 •Introduction to the Command Line Interface in Mac and Linux: 00:10:00 •Installing the X-Code Command Line Tools for Mac: 00:02:00 •Installing Git for Source Code Version Control: 00:03:00 •How to install Homebrew on a Mac: 00:04:00 •Installing GPG Security Systems on a Mac: 00:02:00 •Guide to RVM for Ruby Management: 00:09:00 •Configuring RubyGems and Installing Rails: 00:07:00 •Installing the Postgres Database on a Mac: 00:05:00 •Installing and Working with the Sublime Text Editor: 00:17:00 •Deep Dive into the Command Line - Part 1 of 2: 00:14:00 •Deep Dive into the Command Line - Part 2 of 2: 00:12:00 •PC Environment Setup: Installing Virtual Box: 00:03:00 •Guide to Installing Ubuntu Linux in VirtualBox: 00:10:00 •How to Reboot a Linux Virtual Machine from Terminal: 00:02:00 •Installing Desktop Applications and Ubuntu Navigation Tips and Tricks: 00:09:00 •How to Sign Up for a GitHub Account: 00:03:00 •How to install Git on Linux and Connect to GitHub via SSH: 00:20:00 •How to Securely Install Node and NPM on Linux via NVM: 00:09:00 •Installing the Heroku CLI and the Full Deployment Lifecycle: 00:14:00 •How to install and Work with Sublime Text in Linux: 00:08:00 •PC Setup: Guide for Ruby on Rails and Postgres on Linux - Part 1 of 2: 00:10:00 •PC Setup: Guide for Ruby on Rails and Postgres on Linux - Part 2 of 2: 00:15:00 •Fixing the Role Does Not Exist Error: 00:05:00 •Section Introduction ee: 00:01:00 •Signing Up for a Project Management Account: 00:03:00 •Managing the Project Management Dashboard: 00:08:00 •Importing Project Management Tasks into Pivotal Tracker: 00:01:00 •Creating a Rails Application: 00:11:00 •Generating a Blog for the Portfolio Application Using Rails Scaffolds: 00:11:00 •Analyzing the Rails Process: Index and Show Actions: 00:12:00 •Analyzing the Rails Process: New, Create, Update, Edit, and Destroy Actions: 00:14:00 •Analyzing the Rails Process: Routing with Resources: 00:07:00 •Rails File System Overview: 00:13:00 •Updating the Project Management Dashboard: 00:01:00 •Deep Dive: Analyzing the Application Generator - Part 1: 00:12:00 •Deep Dive: Analyzing the Application Generator - Part 2: 00:17:00 •Section Introduction bb: 00:02:00 •How to Sign Up for a Github Account: 00:03:00 •Generating SSH Keys to Securely Communicate with Github: 00:04:00 •Guide to GitHub: 00:12:00 •How to Update a .gitignore File to Securely Store Secret Credentials: 00:11:00 •Guide to the Markdown Syntax: 00:10:00 •Updating the Git Sprint in the Project Management Dashboard: 00:01:00 •Deep Dive: Analyzing Version Control and Git - Part 1: 00:18:00 •Deep Dive: Analyzing Version Control and Git - Part 2: 00:13:00 •Section Introduction aa: 00:01:00 •Guide to the Rails Controller Generator to Build Pages Part 1: 00:16:00 •Guide to the Rails Controller Generator to Build Pages Part 2: 00:05:00 •Guide to the Rails Model Generator to Build out Data Specific Features: 00:17:00 •Guide to the Rails Model Generator for Building Out the Portfolio Feature: 00:10:00 •Deep Dive: Analyze Rails Generators and How to Customize a Generator - Part 1: 00:15:00 •Deep Dive: Analyze Rails Generators and How to Customize a Generator - Part 2: 00:11:00 •Section Introduction SS: 00:02:00 •Building out a Seeds File for Generating Dynamic Sample Data: 00:15:00 •Implement an Index Action from Scratch in Rails: 00:12:00 •Build the New and Create Functionality from Scratch in Rails: 00:20:00 •Implement the Ability to Edit Database Records from Scratch in Rails: 00:13:00 •How to Use the Link to Method in Rails for Creating Dynamic Links: 00:13:00 •How to Implement the Show Action from Scratch in Rails: 00:12:00 •Building the Ability to Delete Items in Rails: 00:15:00 •How to Implement Custom Routes for Pages in Rails: 00:09:00 •How to Override the Rails Resources Routes for the Show Action: 00:07:00 •How to Implement Permalinks in a Rails Application - Part 1: 00:17:00 •How to Implement Permalinks in a Rails Application - Part 2: 00:05:00 •How to Use Enums in Rails to Manage Data Stages: 00:13:00 •How to Implement a Custom Action in Rails via a Button Click - Part 1: 00:12:00 •How to Implement a Custom Action in Rails via a Button Click - Part 2: 00:10:00 •Updating the Project Management Dashboard and Performing a Pull Request: 00:05:00 •Deep Dive: Rails Routing and Data Flow - Part 1: 00:13:00 •Deep Dive: Rails Routing and Data Flow - Part 2: 00:13:00 •Deep Dive: Rails Routing and Data Flow - Part 3: 00:16:00 •Section Introduction RR: 00:01:00 •Implementing Data Validations in Rails 5: 00:12:00 •Guide to Data Relationships in Rails: 00:16:00 •How to Implement Custom Scopes in Rails 5: 00:16:00 •How to Set Default Values in Rails 5: 00:13:00 •How to Use Concerns in Rails 5: 00:13:00 •Building an Additional Parent/Child Relationship in Rails 5: 00:10:00 •Rails 5 Complex Forms: Configuring Nested Attributes in the Model: 00:17:00 •Rails 5 Complex Forms: Configuring Nested Attributes in the Form: 00:17:00 •Deep Dive: Working with SQL and ActiveRecord in Rails 5 - Part 1: 00:16:00 •Deep Dive: Working with SQL and ActiveRecord in Rails 5 - Part 2: 00:14:00 •Deep Dive: Working with SQL and ActiveRecord in Rails 5 - Part 3: 00:15:00 •Section Introduction QQ: 00:01:00 •How to Install Devise in Rails 5: 00:08:00 •Using Devise to Implement Registrations and Login Functionality: 00:18:00 •Building Custom Routes for Authentication Pages with Devise in Rails 5: 00:03:00 •Enable Users to Logout and Dynamically Render View Content in Rails 5: 00:07:00 •How to Add Custom Attributes to a Devise Based Authentication System: 00:15:00 •How to Use Controller Concerns in Rails 5 for Devise Custom Attributes: 00:15:00 •Integrate Virtual Attributes to Extract First and Last Name Data from a User: 00:17:00 •Deep Dive: Using BCrypt to Implement Encryption in Ruby: 00:19:00 •Section Introduction PP: 00:01:00 •Rails Data Flow Review and Working with Params: 00:14:00 •Using Rails Sessions to Share Data between Pages: 00:19:00 •Refactoring the Session Tracker into a Controller Concern: 00:07:00 •Working with Strong Params in a Rails 5 Controller: 00:13:00 •Deep Dive: Building a Guest User Feature in Rails - Part 1: 00:14:00 •Deep Dive: Building a Guest User Feature in Rails - Part 2: 00:18:00 •Section Introduction OO: 00:03:00 •How to Customize the Rails Master Layout File - Part 1: 00:15:00 •How to Customize the Rails Master Layout File - Part 2: 00:09:00 •Guide to Using Multiple Layout Files in Rails 5: 00:14:00 •Introduction to Rendering Collections via Partials in Rails 5: 00:09:00 •Introduction to Partials in Rails 5: 00:08:00 •How to Send Data to Partials to Generate Custom Behavior in Rails 5: 00:15:00 •Guide to View Helpers in Rails 5: 00:17:00 •How to Use the Rails Content Tag Helper to Auto Generate HTML Code: 00:15:00 •Introduction to Rendering Collections via Partials in Rails 5: 00:09:00 •Guide to Manually Configuring Partials to Work with Collections in Rails: 00:07:00 •Guide to Helpful ActionView Helper Methods in Rails 5: 00:15:00 •Deep Dive: Tips and Tricks to Working with ActionView in Rails 5 - Part 1: 00:12:00 •Deep Dive: Tips and Tricks to Working with ActionView in Rails 5 - Part 2: 00:15:00 •Section Introduction NN: 00:01:00 •How to Utilize Puts Debugging in Rails: 00:08:00 •Guide to Byebug in Rails 5: 00:09:00 •Guide to Debugging with Pry in Rails 5: 00:19:00 •Proper Way to Implement Error Management in a Rails Application - Part 1: 00:15:00 •Proper Way to Implement Error Management in a Rails Application - Part 2: 00:06:00 •Section Introduction MM: 00:01:00 •How to Install Bootstrap 4 into a Rails 5 Application - Part 1: 00:10:00 •How to Install Bootstrap 4 into a Rails 5 Application - Part 2: 00:12:00 •Section Strategy for Building a Custom RubyGem: 00:11:00 •How to Build a RubyGem from Scratch - Part 1: 00:06:00 •How to Build a RubyGem from Scratch - Part 2: 00:15:00 •Section Introduction hh: 00:02:00 •Introduction to Authorization and How to Install the Petergate Gem: 00:08:00 •How to Add Roles to the User Model in Rails 5: 00:05:00 •Guide to Implementing Petergate in Rails 5 Application - Part 1: 00:15:00 •Guide to Implementing Petergate in Rails 5 Application - Part 2: 00:12:00 •Workplace Facilities: 00:35:00 •Portfolio Design Planning for Layout Specific Styles: 00:09:00 •Implementing the Initial Homepage Design in Rails 5 with Bootstrap 4: 00:09:00 •Building a Navigation Partial for the Master Application Layout with Bootstrap: 00:11:00 •Implement a Footer Along with Adding Method Arguments to a Rails View Helper: 00:14:00 •How to Embed a Google Map into a Rails Application: 00:06:00 •Guide to Updating the Styles for the Devise Edit Account Page in Rails: 00:16:00 •How to Override Default Bootstrap Styles for Text Alignment in the Card Class: 00:06:00 •Implementing Styles for the Devise Registration Page with Bootstrap 4: 00:14:00 •How to Design the Login and Password Pages with Bootstrap 4 Form Styles: 00:15:00 •How to Set Default Values for View Helper Method Arguments in Rails 5: 00:04:00 •Integrating a Different Design for a Separate Blog Layout in Rails 5: 00:04:00 •How to Use Bootstrap 4 to Add a Custom Nav Bar to a Blog Layout in Rails: 00:07:00 •Customizing the Design for the Blog Index Page with Bootstrap 4: 00:19:00 •Guide to Adding Styles to the Blog Show and Form Pages with Bootstrap 4: 00:12:00 •Implementing Partials, Pagination, and Font Awesome Icons - Part 1: 00:23:00 •Implementing Partials, Pagination, and Font Awesome Icons - Part 2: 00:23:00 •Initial Design for the Portfolio Index Page with Bootstrap 4: 00:17:00 •Integrating the Portfolio Animated Navigation Slider, Footer, and Index: 00:14:00 •Using the Bootstrap 4 Grid Layout to Style the Portfolio Show Page in Rails: 00:06:00 •Implementing Bootstrap 4 Forms for the Portfolio Form Pages: 00:13:00 •How to Build a View Helper to Dynamically Generate a Nav Bar - Part 1: 00:20:00 •How to Build a View Helper to Dynamically Generate a Nav Bar - Part 2: 00:19:00 •Deep Dive: Rails Asset Pipeline Part 1: 00:01:00 •Deep Dive: Rails Asset Pipeline Part 2: 00:19:00 •Section Introduction LL: 00:01:00 •How to Integrate a Position Attribute and Custom Scope in Rails: 00:12:00 •Installing the jQuery UI and html5sortable JavaScript Libraries in Rails 5: 00:09:00 •Initial Implementation of Drag and Drop Interface in Rails 5: 00:13:00 •Implementing Drag and Drop Functionality into a Ruby on Rails 5 App Part 1: 00:18:00 •Implementing Drag and Drop Functionality into a Ruby on Rails 5 App Part 2: 00:11:00 •Implementing Authorization Rules for Drag and Drop Feature with Petergate: 00:07:00 •Deep Dive: jQuery CoffeeScript in Rails: 00:19:00 •Section Introduction jj: 00:01:00 •Introduction to Using Images and the Rails Asset Pipeline: 00:12:00 •How to Set a Full Size Video Background in Rails with Bootstrap 4: 00:08:00 •Deep Dive: Installing and Configuring Carrierwave for Image Uploads in Rails 5: 00:20:00 •Deep Dive: Integrating Connection to AWS S3 for File Hosting with Carrierwave: 00:14:00 •Implementing File Upload Button Styles and Refactoring Placeholder - Part 1: 00:06:00 •Implementing File Upload Button Styles and Refactoring Placeholder - Part 2: 00:19:00 •Section Introduction ii: 00:01:00 •Difference Between form_form and form_tag in Rails - Part 1: 00:15:00 •Difference Between form_form and form_tag in Rails - Part 2: 00:16:00 •How to Integrate Cocoon in Rails 5 for jQuery Nested Forms: 00:20:00 •Guide to Integrating Alerts that Automatically Fade Away in Rails 5: 00:15:00 •Refactoring Alert Notifications to Use a Shared View Helper in Rails: 00:13:00 •Deep Dive: How to Build an HTML Form from Scratch the Works with Rails: 00:17:00 •Section Introduction ff: 00:03:00 •Integrating the Twitter Gem to Pull in Tweets in Rails 5: 00:20:00 •How to Render Tweets on a Rails View Page: 00:07:00 •Building a Parser View Helper Method to Make Twitter Links Clickable in Rails: 00:15:00 •Customizing the Styles for the Tech News Page with Bootstrap 4: 00:07:00 •Section Introduction cc: 00:01:00 •Running the Comment Generator and Installing the NoSQL Redis Database in Rails 5: 00:18:00 •Building the Comment Form and Required Controller Actions - Part 1: 00:14:00 •Building the Comment Form and Required Controller Actions - Part 2: 00:08:00 •Refactoring the Comment Form into a Partial and Implementing Data Validations: 00:05:00 •Building the jQuery Functions for Processing Comments - Part 1: 00:19:00 •Building the jQuery Functions for Processing Comments - Part 2: 00:05:00 •How to Create a Broadcast with ActionCable in Rails 5: 00:14:00 •Guide to Implementing Live Data Updates in a Rails 5 Application - Part 1: 00:15:00 •Guide to Implementing Live Data Updates in a Rails 5 Application - Part 2: 00:06:00 •Styling the Comment Section and Implementing Gravatar Based Thumbnails in Rails: 00:16:00 •Section Introduction dd: 00:01:00 •Planning Final Project Updates and Assignment: 00:11:00 •Building a Scope to Show the Most Recent Blog Posts and Hiding Draft Posts: 00:09:00 •Hiding the Show Page for Blogs in Draft Mode in Rails 5: 00:04:00 •How to Add a Drop Down Form Element in Rails 5 for Parent/Child Relationships: 00:09:00 •Using the Controller Generator to Build the Topic Features - Part 1: 00:10:00 •Using the Controller Generator to Build the Topic Features - Part 2: 00:11:00 •How to Build a Widget for the Blog to Render Topics in Rails 5: 00:17:00 •How to Implement Radio Buttons in a Rails Form to Update Enum Values: 00:09:00 •Guide to Integrating Markdown and Syntax Highlighting in a Rails 5 Application: 00:13:00 •Integrating Breadcrumbs and Icons for the Blog Show Pages: 00:12:00 •Building a Partial for Managing Admin Actions for Blog Posts: 00:15:00 •How to Implement a Responsive Navigation Bar with Bootstrap 4: 00:11:00 •Updating the Source/Session Tracking System for the New Layouts: 00:14:00 •How to Integrate a Favicon in Rails 5: 00:08:00 •Implementing Responsive Image Components for the Portfolio Layout: 00:06:00 •Adding Card Styles to the About and Contact Pages: 00:09:00 •How to Implement Collapsable Content Components in a Rails 5 Application: 00:09:00 •How to Integrate Bootstrap Progress Bars in a Rails Application: 00:13:00 •How to Integrate Custom Fonts in a Rails 5 Application: 00:17:00 •Updating the Seeds File in Rails: 00:07:00 •Signing Up for and Creating a New Heroku Application: 00:04:00 •How to Deploy a Rails 5 Application to Heroku: 00:13:00 •Working with the Rails Console on Heroku: 00:11:00 •Installing Redis on Heroku for ActionCable Commenting Feature: 00:04:00 •Configuring Action Cable to Work on Heroku: 00:11:00 •How to Implement the Typed.js Library into a Rails 5 Application: 00:12:00 •How to Install and Configure Webpack, Yarn, and jQuery into a Rails 5.1+ App: 00:19:00 •How to Work with Encrypted Credentials in Rails 5.2+: 00:24:00

Overview This comprehensive course on Logistics of Crude Oil and Petroleum Products will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Logistics of Crude Oil and Petroleum Products comes with accredited certification, 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 Logistics of Crude Oil and Petroleum Products. It is available to all students, of all academic backgrounds. Requirements Our Logistics of Crude Oil and Petroleum Products 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 Having these various qualifications 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. Course Curriculum 8 sections • 26 lectures • 01:45:00 total length •Learning objectives: 00:04:00 •Introduction to the different methods of transport: 00:05:00 •The different types of tankers: 00:05:00 •The different types of charters: 00:02:00 •The cost of marine transport: 00:04:00 •The price of marine transport: 00:07:00 •Prices and costs of marine transport: 00:05:00 •Regulations : National flag requirement: 00:01:00 •Flags of convenience: 00:01:00 •Operating aspects: 00:04:00 •Controls on loading: 00:07:00 •Administrative formalities: 00:01:00 •The reception of tankers: 00:07:00 •The constraints: 00:05:00 •The cost of transport by pipeline: 00:03:00 •Comparing transport costs: 00:06:00 •Storage and delivery to the refinery: 00:03:00 •Introduction: 00:03:00 •Dispatch by pipeline: 00:07:00 •Dispatch by water: 00:07:00 •Dispatch by rail: 00:06:00 •Dispatch by road: 00:05:00 •Quality: 00:02:00 •Quantities: 00:02:00 •Administrative accounting methods: 00:01:00 •Customs formalities: 00:02:00

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

Level 5 QLS Endorsed Course | FREE 2 CPD Courses | CPD Accredited | Free PDF Certificates | Lifetime Access

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! 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? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 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: 00:04:00 •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: 00:06:00 •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

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