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The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. Learning Outcomes: Understand the fundamental concepts and types of machine learning, data science, and Python programming. Learn to prepare the system and environment for data analysis and machine learning tasks. Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. Explore feature selection methods and evaluation metrics for classification and regression algorithms. Compare and select the best machine learning model using pipelines and ensembles. Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00

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Curriculum: Course 01: Advanced Electrical Safety Module 01: Safety Precautions Module 02: Direct Current Module 03: Alternating Current Module 04: Voltage and Resistance Module 05: Capacitance and Capacitors Module 06: Working Safely with Electrical Equipment Module 07: Electrical Incidents Module 08: Risk Assessment Module 09: Reducing and Controlling Risks Module 10: Combined Inspection and Testing Course 02: First Aid at Work Introduction to Workplace First Aid Legal Framework for Workplace First Aid Incident Management at Work Primary Survey Secondary Survey Basic First-Aid Techniques Dealing With Fractures and Dislocations Secondary Illness Loss of Responsiveness and CPR Secondary Illness Breathing Problems Secondary Illnesses and Injuries Dealing with Minor Injuries at the Workplace Call for an Emergency Course 03: Distribution Station & Feeder Protection Module 01: Introduction Module 02: Per Phase Analysis Module 03: Sub-Station Protection Module 04: A Protection Coordination Problem Module 05: Surge Protective Equipment Module 06: Transformer Protection Course 04: Electrician Electrician - Updated Version Intro Basic Electricity The Nature of Electricity Conductors & Insulators Current and Electric Circuits Ohm's Law Electric Power Series Parallel Circuits Circuit Theorems Theorem Example Explained Electric Fields and Capacitance Magnetism and Inductance Transient Response Capacitors & Inductors Assignment Electrician - Old Version Basic Electrical Terms The Wonder of Electricity Energy Foundation Joules More on Electrical Generation and Transmission Electrical Principles and Application Section Common Electrical Units used in Formulas and Equations Static and Current Electricity Magnets and Electricity Electromagnets and Electromagnetism Understanding the Electric Charge Electric Current What is Electric Power? Batteries Produce Electricity Water and Electrical Principles are Very Similar Electrical Theory Faraday's Law Maxwell-Faraday Equation Essential Electrical Tools Assessing the risk Electrical Test Equipment Maintenance Combined Inspection and Test (PAT) Course 05: PAT Level 4 Module 01: Introduction to PAT Module 02: Relevant Statutory Provisions for PAT Module 03: Risk Assessment Module 04: Reducing and Controlling Risks Module 05: Electrical Units and Appliance Classification Module 06: Initial Visual Examination Module 07: Instruments for Testing and Equipment Inspection Module 08: Electrical Installation Testing Module 09: Safety Precautions in Electrical Testing Module 10: Reporting and Record-keeping Course 06: Level 3 Heating, Ventilation & Air Conditioning (HVAC) Technician Module 01: Introduction to Heating, Ventilation & Air Conditioning (HVAC) Module 02: Heating Fundamentals and Psychrometrics Module 03: HVAC Systems Module 04: Heating and Ventilation Module 05: Air Conditioning Systems for HVAC Technicians Module 06: Compressors, Water Chillers, and Fans Module 07: Piping for HVAC Technician Module 08: Duct System in HVAC Module 09: Refrigeration and Refrigerants Module 10: Special Applications Module 11: HVAC System Selection Module 12: Safety in the HVAC Industry Course 07: Electronic & Electrical Devices Maintenance Section 01: Introduction & Getting Started Section 02: Electronic and Electrical maintenance Repair Section 03: Tools Needed Section 04: Electronic Components Section 05: Testing Section 06: Troubleshooting Section 07: Introduction to Electronic Components: A Step by Step Guide Course 08: Digital Electric Circuits & Intelligent Electrical Devices Module 01: Introduction Module 02: Numbering Systems Module 03: Binary Arithmetic Module 04: Logic Gates Module 05: Flip-Flops Module 06: Counters & Shift Registers Module 07: Adders Course 09: Smart Meter Installer Training Module 01: Introduction to Smart Meter Module 02: Designing of Smart Metering System Module 03: Install a Smart Meter Module 04: Meter Installation Guidelines Module 05: Smart Meter Cost and Benefit Analysis Module 06: Smart Metering Policy Guidelines Module 07: Smart Meters Act in the UK Course 10: Short Circuit Analysis for HV Three Phase Systems Module 01: Introduction to Short Circuit Analysis for HV Three Phase Systems course Module 02: Short Circuit & Fault Analysis Overview Module 03: Per Phase Analysis Module 04: Per Unit Analysis Module 05: Change of Base Module 06: Transformers & Per Unit Analysis Module 07: Symmetrical Components Module 08: Asymmetrical Three Phase Fault Analysis Module 09: System Modeling Course 11: Electric Power Metering for Single and Three Phase Systems Module 01: Basic Metering Introduction Module 02: Power & Energy Module 03: AC Power Module 04: Instrumentation Module 05: Single Phase Metering Module 06: Instrument Transformers Module 07: Three Phase Metering Module 08: Cross Wattmeter Verification Course 12: Electrical 3 Phase Power Transformers Fundamentals Module 01: Introduction Module 02: The Ideal Transformer Module 03: The Real Transformer Module 04: Instrument Transformers Module 05: Three Phase Power Transformers Module 06: Transformer Clock System Vector Nomenclature Module 07: Transformer Construction and Cooling Module 08: Transformer Protection Module 09: Transformer Relays Course 13: Electrical Power System and High Voltage Engineering Module 01: Introduction to Electrical Power System and High Voltage Module 02: Types of High Voltage Module 03: Generation of High Voltage AC at Power Frequency Module 04: Generation of High Voltage High Frequency AC Module 05: Generation of High Voltage Impulse Part 1 Module 06: Generation of High Voltage Impulse Part 2 Module 07: Generation of High Voltage DC Part 1 Module 08: Generation of High Voltage DC Part 2 Course 14: Electrical Engineering for Electrical Substations Unit 01: Basics of Electrical Substations Unit 02: Design of Earthing System Unit 03: Introduction to Design of Substation CPD 145 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this bundle. Requirements Our Electrical Safety course is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices. Career path Having this Electrical Safety expertise will increase the value of your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included You will get the PDF Certificate for the title course (Advanced Electrical Safety) absolutely Free! Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (Advanced Electrical Safety) absolutely Free! Other Hard Copy certificates are available for £10 each. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

Overview This comprehensive course on Typescript Level 4 will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Typescript Level 4 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? After successfully completing the course you will be able to order your certificate, these are included in the price. Who is This course for? There is no experience or previous qualifications required for enrolment on this Typescript Level 4. It is available to all students, of all academic backgrounds. Requirements Our Typescript Level 4 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 14 sections • 90 lectures • 04:07:00 total length •Module 01: What and why TypeScript: 00:02:00 •Module 02: TypeScript Playground: 00:04:00 •Module 03: Install TypeScript: 00:02:00 •Module 04: Install Visual Studio Code: 00:01:00 •Module 01: Introduction: 00:03:00 •Module 02: First Program Using Visual Studio Code: 00:04:00 •Module 03: Use JS in a HTML: 00:02:00 •Module 04: Strings: 00:02:00 •Module 05: Boolean: 00:01:00 •Module 06: Any Type: 00:01:00 •Module 07: Homogenous Arrays: 00:03:00 •Module 08: Heterogeneous Arrays: 00:01:00 •Module 09: Using alert confirm and prompt: 00:03:00 •Module 10: Comments: 00:02:00 •Module 11: Enum Type: 00:05:00 •Module 01: Arithmetic: 00:03:00 •Module 02: Assignment: 00:04:00 •Module 03: Comparison: 00:04:00 •Module 04: Logical: 00:04:00 •Module 05: Ternary: 00:03:00 •Module 01: Introduction: 00:01:00 •Module 02: IF Else Ladder: 00:06:00 •Module 03: Switch: 00:04:00 •Module 04: Break and Case Flow: 00:03:00 •Module 05: While loop: 00:03:00 •Module 01: Introduction: 00:02:00 •Module 02: Object Literal: 00:03:00 •Module 03: For-In Loop: 00:02:00 •Module 04: Arrays: 00:04:00 •Module 05: De-Structuring Arrays: 00:02:00 •Module 06: De-Structuring Objects: 00:02:00 •Module 01: Introduction: 00:02:00 •Module 02: First Function: 00:03:00 •Module 03: Passing a parameter: 00:01:00 •Module 04: Passing Multiple Parameters: 00:02:00 •Module 05: Optional Parameters: 00:04:00 •Module 06: Default Values: 00:01:00 •Module 07: Function as parameter: 00:02:00 •Module 08: Returning a function: 00:03:00 •Module 09: Anonymous Functions: 00:02:00 •Module 10: Overloading: 00:05:00 •Module 11: REST PARAMS: 00:05:00 •Module 12: Using a Type on REST PARAM: 00:01:00 •Module 01: Introduction: 00:02:00 •Module 02: First arrow function: 00:03:00 •Module 03: Passing Parameters: 00:03:00 •Module 04: Array of Arrow Functions: 00:03:00 •Module 01: let: 00:03:00 •Module 02: const: 00:02:00 •Module 02: const functions: 00:02:00 •Module 04: declare: 00:01:00 •Module 01: Introduction: 00:02:00 •Module 02: Define an Object Interface: 00:03:00 •Module 03: Create and object: 00:03:00 •Module 04: Defining optional properties: 00:01:00 •Module 05: Interfaces are only compile time: 00:01:00 •Module 06: Function Interfaces: 00:04:00 •Module 07: Return Types in Functional interfaces: 00:02:00 •Module 08: Adding methods to Object Interfaces: 00:02:00 •Module 09: Array Interfaces: 00:03:00 •Module 10: String indexed Array Interfaces: 00:03:00 •Module 11: Extending interfaces: 00:06:00 •Module 01: Introduction: 00:01:00 •Module 02: Create a class: 00:03:00 •Module 03: Add a constructor: 00:04:00 •Module 04: Add Function properties: 00:02:00 •Module 05: Power of TypeScript: 00:01:00 •Module 06: Using for-in and instanceof: 00:04:00 •Module 07: Implementing an interface: 00:06:00 •Module 01: Introduction: 00:03:00 •Module 02: Extending a class: 00:05:00 •Module 03: Create Child Objects: 00:07:00 •Module 04: Inheriting Functionality: 00:04:00 •Module 05: Overriding: 00:03:00 •Module 01: Public and readonly: 00:02:00 •Module 02: Encapsulation: 00:01:00 •Module 03: Private properties: 00:04:00 •Module 04: Accessor methods: 00:02:00 •Module 05: Using Static Properties: 00:04:00 •Module 06: More about static: 00:01:00 •Module 07: Static Methods: 00:03:00 •Module 01: String to numeric: 00:04:00 •Module 02: Using the toString method: 00:03:00 •Module 03: Object Casting: 00:02:00 •Module 01: Introduction: 00:01:00 •Module 02: Using Function Modules: 00:04:00 •Module 03: Import Aliasing and Alternate Export Syntax: 00:02:00 •Module 04: Default Exports: 00:02:00 •Module 05: Class Modules: 00:01:00 •Module 06: Aliasing class modules: 00:02:00

About Course Advanced C++: Master the Language of Champions Learn the advanced C++ concepts and techniques you need to build high-performance software applications. In this comprehensive course, you will learn: Generic programming with templates Metaprogramming with constexpr and reflection Advanced object-oriented programming techniques Concurrency and parallelism Performance optimization This course is designed for experienced C++ programmers who want to take their skills to the next level. You will learn from an experienced C++ developer who will teach you the concepts and skills you need to succeed. By the end of this course, you will be able to: Write high-performance, efficient, and maintainable C++ code. Use advanced C++ features to solve complex problems. Design and implement complex software applications. Build a portfolio of real-world C++ applications Throughout the course, you will work on a variety of hands-on projects to build your skills and create a portfolio of real-world C++ applications. Enroll today and start your journey to becoming a C++ expert! Bonus: Get access to the instructor's personal collection of C++ resources. Why learn advanced C++? C++ is a powerful and versatile language that can be used to build a wide variety of software applications. It is also one of the most in-demand languages in the software industry. Learning advanced C++ will give you a significant advantage in the job market and allow you to build more complex and sophisticated software applications. Here are some of the benefits of learning advanced C++: C++ is a high-performance language that can be used to build fast and efficient software applications. C++ is a versatile language that can be used to build a wide variety of software applications, including operating systems, embedded systems, games, and more. C++ is an in-demand language in the software industry, and there are many job opportunities for C++ programmers. Learning advanced C++ will give you a significant advantage in the job market and allow you to build more complex and sophisticated software applications. So what are you waiting for? Enroll in this course today and start your journey to becoming a C++ expert! What Will You Learn? Learn to program with one of the most powerful programming languages that exists today, C++ Master on every advanced C++ programming topics Learn to code C++ from scratch for application development Coding advanced problem statements using the C++ concepts Setting up a local C++ coding environment to create your own coding practices Develop skills on real-world class, object and methods programming techniques Learn how to implement C++ templates, template functions, and classes How to handle error, exception handling and catch real time app errors Apply overloading methods and deep inheritance to how code reusing for your development Polymorphism and abstract classes to implemeting secure code in multiple methods Learn to Apply overloading techniques in C++, Dynamic operators and conversions Course Content Getting Started with C++ Introduction Course Curriculum Getting Started on Windows, Mac or Linux How to Ask Great Questions FAQ's Get and Installing Visual Studio Creating Project C++ Hello World Program Compile and Run a CPP program C++ Object Oriented Programming (theory) Introduction What Are oops Data Structures What Are Access Modifiers C++ Classes Introduction Creating a Class Creating an Objects Class Methods Adding Parameters Constructors Constructor with Parameters The Destructor Get and Set Methods Access Modifiers Static Members C++ Objects and Methods Introduction Constant Objects and Functions Pointers to Class and Object Array of Objects C++ Operator Overloading Introduction Overloading the Equality Operator Overloading the Stream Insertion Operator Overloading the Stream Extraction Operator Overloading the Binary Arithmetic Operators Overloading the Assignment Operators Overloading the Unary Operators Overloading the Subscript Operator Inline Functions C++ Inheritance and Polymorphism Introduction Inheritance Protected Members Constructors and Inheritance Destructors and Inheritance Overriding Methods Polymorphism Abstract Classes Final Classes and Methods Deep Inheritance and Methods Multiple Inheritance C++ Error Handling and Exceptions Introduction What Are Exceptions Throwing an Exception Catching an Exception Catching Multiple Exceptions Create Custom Exceptions C++ Templates Introduction Creating a Function Template Function Template Arguments Overloading a Function Template Creating a Class Template Templates with Multiple Parameters A course by Sekhar Metla IT Industry Expert Xpert Learning RequirementsGood to have C++ basic, intermediate to start hereNo software is required in advance of the course (all software used in the course is free) Audience C++ Advanced level developers curious about programming Anyone interested in learning the Advanced concepts of C++ Anyone who wants to grasp the concept with real-world examples of coding Anyone who wants to become a proficient software developer Anyone who wants to become an independent programmer Audience C++ Advanced level developers curious about programming Anyone interested in learning the Advanced concepts of C++ Anyone who wants to grasp the concept with real-world examples of coding Anyone who wants to become a proficient software developer Anyone who wants to become an independent programmer