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Overview This comprehensive course on Computer Science: Graph Theory Algorithms will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Computer Science: Graph Theory Algorithms 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 Computer Science: Graph Theory Algorithms. It is available to all students, of all academic backgrounds. Requirements Our Computer Science: Graph Theory Algorithms 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 17 sections • 44 lectures • 08:37:00 total length •Promo: 00:03:00 •Introduction: 00:14:00 •Common Problem: 00:10:00 •Depth First Search: 00:11:00 •Breadth First Search: 00:08:00 •Breadth First Search Shortest Path on a Grid: 00:17:00 •Storage and Representation of Trees: 00:10:00 •Beginner Tree Algorithms: 00:10:00 •Rooting Tree: 00:05:00 •Center(s) of a Tree: 00:06:00 •Isomorphisms in Trees: 00:11:00 •Isomorphisms in Trees Source Code: 00:10:00 •Lowest Common Ancestor: 00:17:00 •Topological Sort: 00:14:00 •Shortest and Longest Paths on DAGs: 00:10:00 •Khan's Algorithm: 00:13:00 •Dijkstra's Shortest Path Algorithm: 00:25:00 •Dijkstra's Shortest Path Algorithm Source Code: 00:09:00 •Bellman-Ford Algorithm: 00:15:00 •Floyd-Warshall Algorithm: 00:16:00 •Floyd-Warshall Algorithm Source Code: 00:09:00 •Algorithm to Find Bridges and Articulation Points: 00:20:00 •Algorithm to Find Bridges and Articulation Points Source Code: 00:09:00 •Tarjan's Algorithm for Finding Strongly Connected Components: 00:17:00 •Tarjan's Algorithm for Finding Strongly Connected Components Source Code: 00:07:00 •Travelling Salesman Problem (TSP) with Dynamic Programming: 00:21:00 •Travelling Salesman Problem (TSP) with Dynamic Programming Source Code: 00:14:00 •Existence of Eulerian Paths and Circuit: 00:10:00 •Finding Eulerian Paths and Circuits: 00:16:00 •Eulerian Paths Source Code: 00:08:00 •Prim's Minimum Spanning Tree Algorithm (Lazy Version): 00:15:00 •Prim's Minimum Spanning Tree Algorithm ( Eager Version): 00:15:00 •Prim's Minimum Spanning Tree Algorithm Source Code ( Eager Version): 00:09:00 •Max Flow Ford-Fulkerson Method: 00:13:00 •Max Flow Ford-Fulkerson Method Source Code: 00:17:00 •Network Flow: Unweighted Bipartite Graph Matching: 00:11:00 •Network Flow: Mice and Owls: 00:08:00 •Network Flow: Elementary Math: 00:11:00 •Network Flow: Edmond-Karp Algorithm: 00:06:00 •Network Flow: Edmond-Karp Algorithm Source Code: 00:10:00 •Network Flow: Capacity Scaling: 00:10:00 •Network Flow: Capacity Scaling Source Code: 00:06:00 •Network Flow: Dinic's Algorithm: 00:12:00 •Network Flow: Dinic's Algorithm Source Code: 00:09:00

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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Duration 1 Days 6 CPD hours This course is intended for This course is designed for skilled users of Microsoft Windows and Office who do not have prior coding or programming experience and who are interested in creating custom business apps quickly and without writing application code. Overview In this course, you will use Microsoft Power Apps to build and deploy low-code business apps. You will: Determine how Microsoft Power Apps can meet your business needs. Plan and design apps. Build canvas apps. Build model-driven apps. Test and deploy apps. This course introduces building low-code/no-code apps with Microsoft© Power Apps©. Most out-of-the-box solutions do not meet exact business needs or integrate well with existing business apps. Power Apps eases users into app development with templates, automated app-building tools, and a streamlined programming language to enable any business user to create a custom app. Getting Started with Microsoft Power Apps Topic A: Introduction to Microsoft Power Platform Topic B: Introduction to Power Apps Topic C: Select App Types to Address Business Needs Planning and Designing Apps Topic A: Plan Apps Topic B: Design Apps Building Canvas Apps Topic A: Create an App from a Blank Canvas Topic B: Create an App from a Template Building Model-Driven Apps Topic A: Create Model-Driven Apps Topic B: Add Visualizations and Reports Testing and Deploying Apps Topic A: Make Apps Available to Other Users Topic B: Test Apps Topic C: Revise Apps

Develop a blockchain notary office on Ethereum and learn all about Solidity, Remix, Ganache, Geth, Metamask, and Truffle

You will not only learn a few lines of code in this course but will also understand the principles of programming. Learn the fundamentals of JavaScript with the latest JavaScript versions (ES6/ES7/ES8/ES9/ES10/ESNext) and you will be well on your way to being a Grandmaster programmer in any language.

Do you want to learn software development but don't know where to start? Are you looking for a career change, but don't know how to get your foot in the door? If so, then our Software Development Training program is perfect for you! Our comprehensive Software Development Training offers more than just coding skills; we provide you with personalised mentoring, job opportunities, and unwavering support throughout your professional journey. At studyhub, we understand that becoming a successful software developer involves more than just learning programming languages. That's why our program goes beyond the basics and focuses on empowering you with the tools, knowledge, and confidence to excel in this ever-evolving field. We will help you until you find a job, so you won't have to worry about a thing. Just gain knowledge and learn skills, getting you a job is our duty. We are committed to helping you succeed in your career. 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Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course are Business Process Architects, Developers, Developer Consultants, System Administrators, System Architects, and Technology Consultants. Overview The participants will learn to configure scenariosUnderstand and configure the different options with which a back-end system can be connected to the SAP Process Integration (formerly known as SAP Exchange Infrastructure). In this course, students gain knowledge in configuring SAP Process Integration and learn how to implement different integration scenarios with SAP and non-SAP applications. SAP Process Integration Architecture Describing SAP Process Integration Architecture Explaining the Functionality and Utilization of SAP Process Integration The System Landscape Directory (SLD) Maintaining Products and Software Components in the System Landscape Directory (SLD) Implementing Technical Systems in the System Landscape Directory Maintaining Business Systems for Use in the Integration Directory Interface Objects in the Enterprise Service Repository (ESR) Listing Options for Navigating the Enterprise Services Repository (ESR) Analyzing Interface Objects in the Enterprise Services Repository (ESR) Maintaining Interface Objects Defining Message Processing Types and Quality of Services Object Mapping in the Enterprise Service Repository (ESR) Creating Message Mapping Objects Mapping Between Service Interfaces Analyzing Asynchronous Integration Scenarios Configuration of Integration Directory Communication Objects Using Configuration Views Creating Communication Channels in the Integration Directory (ID) Configuration of Integration Directory Objects for ABAP Processing Analyzing Objects Used in the Configuration of Integration Engine (IE) Communication Analyzing Routing Objects Used in Integration Engine (IE) Communication Analyzing Multiple Receiver Objects Used in Integration Engine (IE) Communication Configuration of Integration Directory Objects for Java Processing Maintaining Objects for Advanced Adapter Engine (AAE)-Only Processing Maintaining Objects for Advanced Adapter Engine Extended (AEX) Message Processing Creating and Deploying Integration Flows to the Advanced Adapter Engine Extended (AEX) Operations of SAP Process Integration Using the Runtime Workbench Using the Process Integration Monitoring (PIMon) Home Using the SAP NetWeaver Administrator Connectivity Options for SAP Process Integration Analyzing Connectivity Options between Back-End Systems with SAP Process Integration HTTP Connectivity Options Configuring the Simple Object Access Protocol (SOAP) Adapter Using Proxy Technology Checking the HTTP ABAP Adapter Using the HTTP_AAE (Java) Adapter File Adapter Configuration Configuring the Receiver File Adapter Configuring a Sender File Adapter Intermediate Document (IDoc) Connectivity Options Using the Intermediate Document (IDoc) Adapter with the Receiver Intermediate Document Adapter for the ABAP Stack Using the Sender Intermediate Document (IDoc) Adapter Using the Intermediate Document (IDoc) Adapter in the Advanced Adapter Engine (AAE) Configuring a Complete Intermediate Document (IDoc) Scenario Configuration of SAP Business Application Programming Interfaces (BAPIs) Connectivity Configuring the Receiver Remote Function Call (RFC) Adapter Configuring the Sender Remote Function Call (RFC) Scenario Enterprise Services and B2B Connectivity Analyzing Enterprise Services Configuring Business-to-Business (B2B) Connectivity Cross-Component Business Process Management (ccBPM) Integration Processes Analyzing the Runtime of the Cross-Component Business Process Management (ccBPM) Identifying the Steps to Create and Use an Integration Process Additional course details: Nexus Humans BIT400 SAP Process Integration training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the BIT400 SAP Process Integration course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Highlights of the Course Course Type: Online Learning Duration: 19 Hours 57 Minutes Tutor Support: Tutor support is included Customer Support: 24/7 customer support is available Quality Training: The course is designed by an industry expert Recognised Credential: Recognised and Valuable Certification Completion Certificate: Free Course Completion Certificate Included Instalment: 3 Installment Plan on checkout What you will learn from this course? Gain comprehensive knowledge about python Understand the core competencies and principles of python Explore the various areas of python Know how to apply the skills you acquired from this course in a real-life context Become a confident and expert python developer Python Masterclass Course Master the skills you need to propel your career forward in python. This course will equip you with the essential knowledge and skillset that will make you a confident python developer and take your career to the next level. This comprehensive Python masterclass course is designed to help you surpass your professional goals. The skills and knowledge that you will gain through studying this Python masterclass course will help you get one step closer to your professional aspirations and develop your skills for a rewarding career. This comprehensive course will teach you the theory of effective python practice and equip you with the essential skills, confidence and competence to assist you in the python industry. You'll gain a solid understanding of the core competencies required to drive a successful career in python. This course is designed by industry experts, so you'll gain knowledge and skills based on the latest expertise and best practices. This extensive course is designed for python developer or for people who are aspiring to specialise in python. Enrol in this Python masterclass course today and take the next step towards your personal and professional goals. Earn industry-recognised credentials to demonstrate your new skills and add extra value to your CV that will help you outshine other candidates. Who is this Course for? This comprehensive Python masterclass course is ideal for anyone wishing to boost their career profile or advance their career in this field by gaining a thorough understanding of the subject. Anyone willing to gain extensive knowledge on this python can also take this course. Whether you are a complete beginner or an aspiring professional, this course will provide you with the necessary skills and professional competence, and open your doors to a wide number of professions within your chosen sector. Entry Requirements This Python masterclass course has no academic prerequisites and is open to students from all academic disciplines. You will, however, need a laptop, desktop, tablet, or smartphone, as well as a reliable internet connection. Assessment This Python masterclass course assesses learners through multiple-choice questions (MCQs). Upon successful completion of the modules, learners must answer MCQs to complete the assessment procedure. Through the MCQs, it is measured how much a learner could grasp from each section. In the assessment pass mark is 60%. Advance Your Career This Python masterclass course will provide you with a fresh opportunity to enter the relevant job market and choose your desired career path. Additionally, you will be able to advance your career, increase your level of competition in your chosen field, and highlight these skills on your resume. Recognised Accreditation This course is accredited by continuing professional development (CPD). CPD UK is globally recognised by employers, professional organisations, and academic institutions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. What is CPD? Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements. Benefits of CPD Improve your employment prospects Boost your job satisfaction Promotes career advancement Enhances your CV Provides you with a competitive edge in the job market Demonstrate your dedication Showcases your professional capabilities What is IPHM? The IPHM is an Accreditation Board that provides Training Providers with international and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation is a guarantee of quality and skill. Benefits of IPHM It will help you establish a positive reputation in your chosen field You can join a network and community of successful therapists that are dedicated to providing excellent care to their client You can flaunt this accreditation in your CV It is a worldwide recognised accreditation What is Quality Licence Scheme? This course is endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. Benefits of Quality License Scheme Certificate is valuable Provides a competitive edge in your career It will make your CV stand out Course Curriculum Python Basics What Is Python Installation And First Execution 00:09:00 Variables, Creating And Running External Script, Interactive Shell 00:14:00 Comments 00:03:00 Types Of Variables 00:09:00 Math Operators 00:10:00 Exercise: Adding VAT To Products 00:08:00 Semicolon and ENTER - Assigning Multiple Values To Variables At Once 00:04:00 Assignment Operators 00:02:00 Playing With Strings (Slicing, Adding And Having Fun) 00:10:00 Functions and Libraries Basics Importing Libraries, What Are Functions - Basics 00:11:00 ATTENTION - Important Lecture About Common Mistake Regarding Functions 00:05:00 Taking Data From User And Type Conversion (Casting) 00:12:00 Conditional statements Comparison (Relational) Operators 00:05:00 Instruction 'If' Why INDENTATION Is IMPORTANT In Python | DO NOT SKIP! 00:11:00 EXERCISE: Simple Calculator 00:11:00 Values Different Than 0 00:03:00 Logical Operators 00:10:00 Loops Loop While 00:06:00 EXERCISE: Adding Numbers Taken From The User 00:08:00 Loop For 00:06:00 Instruction Break And Continue 00:11:00 EXERCISE: Guess The Number 00:09:00 Lists What Are Lists Basic Operations On Lists 00:10:00 Checking If Element Is 'In' Or 'Not In' The List 00:03:00 Operating On Lists With Functions Bn 00:14:00 Advanced Types Tuples - What Does Immutable Mean 00:05:00 Dictionaries 00:10:00 Sets 00:07:00 Operations On Sets 00:08:00 Nested Types 00:13:00 Processing Nested Types Using Loops 00:03:00 Dictionary Inside Dictionary, Dictionary Inside List - When To Choose Which 00:09:00 Extracting (Lterating Through) Values From Nested Dictionaries 00:20:00 EXERCISE: Dynamic Dictionary With Definitions 00:12:00 Transformations List Comprehensions 00:10:00 Generator Expressions 00:11:00 Dictionary Comprehensions 00:14:00 Set Comprehension 00:03:00 EXERCISE: Finding Numbers That Are Divisible By 7, But Are Not Divisible By 5 00:10:00 Functions - Basics What Are Functions How To Create A Function Why Should We Use Functions 00:10:00 Multiple Parameters In Function (Passing More Arguments) 00:05:00 Returning Values From Function 00:12:00 Functions - Advanced Multi Module Application | How To Import Your Own Module 00:10:00 enum - What It Is And Why You Should Use It 00:13:00 EXERCISE: Sum Of All Numbers Up To The One Entered By User | IMPORTANT Lecture 00:14:00 Measuring PERFORMANCE Of Code | How well (Fast) Some Part Of Code Work | Time 00:15:00 Function As Argument Of Another Function | How To Measure Performance Of Func 00:07:00 Default Arguments 00:07:00 Named (keyword) And Unnamed (Positional) Arguments 00:09:00 EXERCISE - Checking If Value Is In Container 00:07:00 Variable Length Argument (Multiple Arguments Sent And Saved In Single Parameter) 00:12:00 Local vs Global Variables - Scope - Lifetime Of Variables 00:09:00 Mutable vs Immutable Objects 00:21:00 Shallow vs Deep Copy Of Object 00:14:00 Lambda | Anonymous functions - What Are They When Should You Use Them 00:12:00 Random Numbers Drawing Random Numbers - Creating A Program That Checks If You Hit The Monster 00:15:00 Random Events - Choice vs Choices Function 00:09:00 Shuffle - Shuffling Cards In 'War' Game 00:03:00 EXERCISE: Drawing Elements Without REPETITION - Lottery Game - 6 Numbers From 49 00:13:00 EXERCISE | GAME | Drawing Random Chests Colours With Random Rewards 00:25:00 EXERCISE | GAME | Drawing Approximate Value To A Certain Value 00:08:00 Working With Files in Python and Exceptions What Is A File How To Create It Why Do We Need To CLOSE It How To Save Data 00:10:00 Exceptions, Try, Finally Block 00:04:00 Opening The File Using: With… As…: 00:03:00 Reading The Content Of File - Read vs Readlines, Splitting Lines, Encoding 00:07:00 Seek And Tell - Changing And Reading The Position Of Last Operation In File 00:05:00 Append - Adding Text At The End Of File 00:02:00 A+, R+, W+ - Two Function File Opening Modes 00:07:00 Keyword Except - Catching Exceptions - EXERCISE - Loading Names And Surnames 00:12:00 JSON What Is JSON Why Do We Use It What Problems Does JSON Solve 00:06:00 Creating And Saving Data From Python In JSON Format To File And String 00:10:00 Loading Data From JSON File To Python 00:04:00 Pretty Printer - Loading/Saving Pretty And Sorted JSON 00:08:00 pip and PyPi - using External Packages What Is Pip And PyPi Installing And Playing With External Package - Request 00:11:00 Loading Data From Server From JSON File - Preface To Next EXERCISE 00:10:00 Processing JSON Data | Extracting Top Values From List Of Dictionaries 00:18:00 SOLUTION 1: Retrieving A Few Users From Server At The Same Time 00:07:00 SOLUTION 2: Retrieving A Few Users From Server At The Same Time 00:05:00 SOLUTION 3: Retrieving A Few Users From Server At The Same Time 00:12:00 Defaultdict Supplying Missing Values Of Dictionary When Accessing Empty Element 00:06:00 API What Is Public API Where To Find Them. stackoverflow API 00:13:00 Getting JSON From Stackoverflow.com -> Getting Top Questions From Last Week 00:10:00 Opening Websites From Python Code - Webbrowser Module 00:05:00 Getting Records From Last Week - Datetime Module 00:10:00 EXERCISE: Cat Facts 00:06:00 EXERCISE: Random CAT Images By Breed 00:03:00 Authorization - Log In Using API Key - Getting Holiday Calendar 00:12:00 API - header authorization - POST % DELETE Authorization Through Header - Settings 00:06:00 EXERCISE: CAT System - Introduction 00:13:00 POST - Adding A Cat To Favourite List 00:07:00 DELETE - Removing A Cat From Favourite List 00:15:00 Visual Studio Code - Installation and Setting VSC - Downloading And Installing 00:03:00 Setting Up The Workspace In VSC And Installing Basic Extensions 00:08:00 Linter - What Is Linting - How To Use It, Settings In .Vscode 00:10:00 Code Runner | Custom Shortcuts | Shortcuts From Other Editors, Interactive Shell 00:11:00 Auto Save - Making Sure Your File Is Saved After Each Change 00:01:00 Snippets And Pass Keyword 00:06:00 Automatic Code Formatting 00:03:00 Debugging Mode - How To Professionally Debug Your Code 00:16:00 Ligatures - Custom Font Created Especially For Programmers - Fira Code 00:07:00 Visual Studio Code - Useful Extensions Icons Theme 00:02:00 Bracket Pair Colorizer - Improving Readability Of Your Code 00:02:00 Python Preview - Watching The Code Execution While Writing It 00:07:00 Python Quick Print 00:01:00 Code Spell Checker 00:03:00 What Is Object Oriented Programming 00:04:00 OOP - Object Oriented Programming What Are Objects And Classes How To Create A Simple Object And Class. 00:14:00 Creating Your First Method - What Is “Self” 00:15:00 __Init__ - Sending Arguments To Class For Initialization 00:08:00 EXERCISE: Create Rockets That Can Move Up 00:11:00 Document String - Docstring 00:06:00 __Str__ - Dunder Method Representing Text Of Object 00:05:00 EXERCISE: Organizing More Than One Class - Rocket Board 00:10:00 __Getitem__, __Setitem__ - Setting And Getting Values Using Dunder Methods 00:10:00 EXERCISE: Counting Distance Between Rockets 00:21:00 Is It Important To Understand Math That Was Used In The Last Lecture 00:06:00 Static Methods 00:12:00 Setting Type For Variables Using Annotations - Pyright Extension 00:12:00 __Len__ 00:05:00 Class / Static Variables - Creating USER With Unique Ids 00:11:00 EXERCISE: Bank Account - Withdraw/Deposit Money 00:07:00 How To Properly Handle Returned Values From Methods 00:16:00 Inheritance And Method Overriding - Super 00:19:00 EXERCISE: Inheritance On Practical Example - Result 00:08:00 EXERCISE: Playing With Cube, Square And Rectangle 00:09:00 Inheritance Vs Association - When To Use Which 00:16:00 Generator functions Generator Functions - Yield Keyword 00:13:00 EXERCISE: Generate Infinite Amount Of Numbers Multiplied By Themselves 00:06:00 Send Method - How To Send A Value Into A Generator 00:12:00 Assessment Assessment - Python Diploma 00:10:00 Obtain Your Certificate Order Your Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00