521 Courses

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

Learn about the jQuery library from scratch and stretch your journey from a beginner-level to advanced-level professional with a step-by-step and comprehensive course. A basic understanding of the JavaScript Document Object Model and CSS is suggested as a prerequisite to this course.

Learning Objectives Introduction , Customizing Excel , Analyzing Data with Logical Functions , Working with Lookup Functions , Using Text Functions , Working with Date and Time Functions , Formula Auditing , What-If Analysis , Worksheet and Workbook Protection , Automating with Macros , Working with Form Controls , Ensuring Data Integrity , Collaborating in Excel , Importing and Exporting Data to a Text File , Conclusion   Pre-Requisites Excel 365 Introduction and Intermediate courses or equivalent experience.   Description This course will teach students advanced concepts and formulas in Microsoft Excel 365. Students will learn functions such as SUMIF, AVERAGEIF, and COUNTIF, advanced lookup functions, and complex logical and text functions. Additionally, students will experiment with auditing formulas and error checking, use the What-If Analysis tools, learn the options for worksheet and workbook protection, review advanced use of PivotTables and PowerPivot add-in, work with Macros, use form controls, and ensure data integrity in their worksheets and workbooks. Students will also learn about Excel's many collaboration features and import and export data to and from their workbooks. Course Introduction Introduction 00:01:00 Section 01 Lesson 01-Customizing the Ribbon 00:09:00 Lesson 02-Customizing the Quick Access Toolbar 00:06:00 Lesson 03-Customizing the General and Formula Options 00:04:00 Lesson 04-Customizing the AutoCorrect Options 00:03:00 Lesson 05-Customizing the Save Defaults 00:04:00 Lesson 06-Customizing Advanced Excel Options 00:03:00 Section 02 Lesson 01-Working with the Most Common Logical Functions 00:03:00 Lesson 02-Understanding IF Functions 00:06:00 Lesson 03-Evaluating Data with the AND Function 00:05:00 Lesson 04-Evaluating Data with the OR Function 00:03:00 Lesson 05-Creating a Nested IF Function 00:05:00 Lesson 06-Using the IFS Function 00:03:00 Lesson 07-Summarizing Data with SUMIF 00:03:00 Lesson 08-Summarizing Data with AVERAGEIF 00:02:00 Lesson 09-Summarizing Data with COUNTIF 00:02:00 Lesson 10-Summarizing Data with MAXIFS and MINIFS 00:03:00 Lesson 11-Using the IFERROR Function 00:03:00 Section 03 Lesson 01-What are Lookup Functions 00:03:00 Lesson 02-Using VLOOKUP 00:08:00 Lesson 03-Using HLOOKUP 00:03:00 Lesson 04-Using VLOOKUP with TRUE 00:04:00 Lesson 05-Using HLOOKUP with TRUE 00:02:00 Lesson 06-Using the Index Function 00:03:00 Lesson 07-Using the MATCH Function 00:03:00 Lesson 08-Combining INDEX and MATCH 00:04:00 Lesson 09-Comparing Two Lists with VLOOKUP 00:02:00 Lesson 10-Comparing Two Lists with VLOOKUP and ISNA 00:04:00 Lesson 11-Using the New XLookup Function-v2 00:07:00 Lesson 12-Using Dynamic Array functions-v2 00:06:00 Lesson 13-Other New Functions-v2 00:04:00 Section 04 Lesson 01-What are Text Functions 00:01:00 Lesson 02-Using CONCAT, CONCATENATE, AND TEXTJOIN 00:04:00 Lesson 03-Using Text to Columns 00:02:00 Lesson 04-Using LEFT, RIGHT, and MID Functions 00:03:00 Lesson 05-Using UPPER, LOWER, and PROPER Functions 00:02:00 Lesson 06-Using the Len Function 00:03:00 Lesson 07-Using the Trim Function 00:01:00 Lesson 08-Using the SUBSTITUTE Function 00:02:00 Section 05 Lesson 01-What are Date and Time Functions 00:02:00 Lesson 02-Using TODAY, NOW, and DAY Functions 00:03:00 Lesson 03-Using NETWORKDAYS and YEARFRAC Functions 00:03:00 Section 06 Lesson 01-Showing Formulas 00:03:00 Lesson 02-Tracing Precedents and Dependents 00:04:00 Lesson 03-Adding a Watch Window 00:04:00 Lesson 04-Error Checking 00:04:00 Section 07 Lesson 01-Using the Scenario Manager 00:07:00 Lesson 02-Using Goal Seek 00:03:00 Lesson 03-Analyzing with Data Tables 00:04:00 Section 08 Lesson 01-Understanding Protection 00:02:00 Lesson 02-Encrypting Files with Passwords 00:05:00 Lesson 03-Allowing Specific Worksheet Changes 00:02:00 Lesson 04-Adding Protection to Selected Cells 00:03:00 Lesson 05-Additional Protection Features 00:03:00 Section 09 Lesson 01-What are Macros 00:03:00 Lesson 02-Displaying the Developer Tab 00:03:00 Lesson 03-Creating a Basic Formatting Macro 00:05:00 Lesson 04-Assigning a Macro to a Button 00:03:00 Lesson 05-Creating Complex Macros 00:04:00 Lesson 06-Viewing and Editing the VBA Code 00:04:00 Lesson 07-Adding a Macro to the Quick Access Toolbar 00:03:00 Section 10 Lesson 01-What are Form Controls 00:02:00 Lesson 02-Adding a Spin Button and Check Boxes 00:04:00 Lesson 03-Adding a Combo Box 00:07:00 Section 11 Lesson 01-What is Data Validation 00:02:00 Lesson 02-Restricting Data Entry to the Whole Numbers 00:02:00 Lesson 03-Restricting Data Entry to a List 00:04:00 Lesson 04-Restricting Data Entry to a Date 00:02:00 Lesson 05-Restricting Data Entry to Specific Text Lengths 00:01:00 Lesson 06-Composing Input Messages 00:02:00 Lesson 07-Composing Error Alerts 00:03:00 Lesson 08-Finding Invalid Data 00:02:00 Lesson 09-Editing and Deleting Validation Rules 00:01:00 Section 12 Lesson 01-Working with Comments-v2 00:03:00 Lesson 02-Printing Comments and Errors 00:02:00 Lesson 03-Sharing a Workbook 00:04:00 Lesson 04-Co-Authoring in Excel 00:02:00 Lesson 05-Tracking Changes in a Workbook 00:03:00 Lesson 06-Working with Versions 00:03:00 Lesson 07-Sharing files Via Email-v2 00:03:00 Section 13 Lesson 01-Importing a Text File 00:04:00 Lesson 02-Exporting Data to a Text File 00:01:00 Course Recap Recap 00:02:00 Additional Materials Resource - Excel 365 Advanced 00:00:00 Final Exam Final Exam - Excel 365 Advanced 00:20:00

Get started with creating JavaFX applications using TornadoFX - A lightweight JavaFX framework for Kotlin

The IOSH Working Safely® is a one-day course for people at any level, in any sector, that need a grounding in health and safety.

This course gets you started with the testing of web services and REST APIs using SoapUI features and tools. You will start with the basics and advance towards designing test frameworks for performing manual and automation testing on web services and APIs with the help of real-time projects.

This course will let you develop the skillsets needed to guide the delivery of value in a Lean enterprise—and learn about the activities, tools, and mechanics used to manage backlogs and programs. Attending the class prepares you to take the exam and become a certified SAFe® 6 Product Owner/Product Manager (POPM). SAFe Product Owner/Product Manager 6 training course is provided under Scaled Agile, Inc. Partner Program Agreement.

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

Explore everything that Vue.js has to offer from the ground up, all while building 4 projects to put your knowledge into practice. You will learn all of the essential Vue skills, along with the new Vue.js 3 features and syntax.

If you have already done with the basic functions of Microsoft Office Access 2016, then now you're ready to learn advanced Access features such as database management, advanced form design, packaging a database, encrypting a database, preparing a database for multi-user access, and more. Access is Microsoft's flagship database application that allows you to create and manage databases for all sorts of different purposes. This new version of Access incorporates a few new features and connectivity options in order to make databases more accessible to the everyday user. This Access 2016 Advanced is intended to help all users get up to speed on the different features of Access and to become familiar with its more advanced features. The course covers how to complete advanced form design tasks, add field and record validation, create and manage macros, conduct advanced database management, distribute and secure a database, and manage switchboards. What Will I Learn? Improve the Structure of a Database Maintain a Database Work with Access Switchboards Configure Access for Multiple Users Automate Processed with VBA Use Table Events Requirements Access Introduction and Intermediate courses or equivalent experience. Who is the target audience? Students who wish to learn the more advanced capabilities of Access. Introduction Introduction FREE 00:01:00 Improving the Structure of a Database Using the Table Analyzer Wizard 00:05:00 Append Querying to Normalize a Table 00:09:00 Creating a Junction Table 00:06:00 Maintaining a Database Backing Up a Database 00:02:00 Using the Compact and Repair Database Tool 00:03:00 Using the Object Dependencies Tool 00:02:00 Using the Database Documenter Too 00:02:00 Using the Performance Analyzer Tool 00:02:00 Working with Access Switchboards Understanding the Access Switchboard 00:03:00 Using The Switchboard Manager 00:09:00 Modifying a Switchboard 00:05:00 Setting the Startup Options 00:04:00 Configuring Access for Multiple Users Using the database Splitter 00:04:00 Configuring Trusted Locations 00:02:00 Password Protecting a Database 00:03:00 Password Protecting Modules 00:01:00 Converting a Database to an ACCDE file 00:02:00 Automating Processes with VBA Understanding VBA Basics 00:06:00 Exploring Variables and Control Flow Statements 00:10:00 Exporting a Table or Query with VBA 00:06:00 Converting a Macro to VBA 00:02:00 Using Table Events Understanding Table Events 00:02:00 Using the Before Change Event Command 00:04:00 Using the After Change Event Command 00:04:00 Conclusion Course Recap 00:01:00 Resources Resources - Access 2016 Advanced 00:00:00 Course Certification