2452 Programming courses

Course Overview Learn how to make useful digital representations of complex patterns and statistics by taking this Computer Simulation of Realistic Mathematical Models Level 2 course. This intuitive course uses real-world examples to teach you how data can be transformed into online recreations, which can then be more easily understood and changed for research purposes. You'll be capable of turning a complex set of results into simplified visual models, guided by expert tuition and step-by-step instructions.  This Computer Simulation tutorial empowers sophisticated cross-platform computational packages and programming languages to be used in imaginative ways. This will allow the construction of two models on factual data and genuine results. Gaining the ability to perfect these projects will bring the talent to produce further models of this type. You will soon be able to use your capabilities in a wealth of presentations and other projects. This best selling Computer Simulation of Realistic Mathematical Models Level 2 has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Computer Simulation of Realistic Mathematical Models Level 2 is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Computer Simulation of Realistic Mathematical Models Level 2 is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The Computer Simulation of Realistic Mathematical Models Level 2 is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn. After successfully completing the Computer Simulation of Realistic Mathematical Models Level 2, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Computer Simulation of Realistic Mathematical Models Level 2 will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Computer Simulation of Realistic Mathematical Models Level 2 to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

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

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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

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.

Create a real-world backend for a Bootcamp directory app

Duration 2.75 Days 16.5 CPD hours This course is intended for Complete beginners who have never programmed before to experienced developers coming from another programming language. Overview You will learn how to leverage the power of Python to solve tasks. You will build games and programs that use Python libraries. You will be able to use Python for your own work problems or personal projects. You will create a portfolio of Python based projects you can share. Learn to use Python professionally, learning both Python 2 and Python 3! Create games with Python, like Tic Tac Toe and Blackjack! Learn advanced Python features, like the collections module and how to work with timestamps! Learn to use Object Oriented Programming with classes! Understand complex topics, like decorators. Understand how to use both the Jupyter Notebook and create .py files Get an understanding of how to create GUIs in the Jupyter Notebook system! Build a complete understanding of Python from the ground up! Our Introduction to Python course is designed to take complete beginners or experienced developers up to speed on Python?s capabilities, setting up students for success in using Python for their specific field of expertise. Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3. Learn how to use Python for real-world tasks, such as working with PDF Files, sending emails, reading Excel files, scraping websites for information, working with image files, and much more! This course will teach you Python in a practical manner and provides a full coding screencast and a corresponding code notebook to review the concepts and exercises conducted in class. Please note, this course is able to be offered in either 3 full day sessions or 5 partial day sessions. See the schedule below. This course includes 6-months access to the full course content in on-demand format to support post-class reference and review. Command Line Basics Python System Setup Jupyter Notebooks Python Data Types Key Data Structures Logic and Control Flow Functions Debugging Modules Object Oriented Programming File I/O Testing Decorators Generators Automation of Tasks Web Scraping Graphical User Interfaces Additional course details: Nexus Humans Introduction to Python 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 Introduction to Python 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.

Overview Uplift Your Career & Skill Up to Your Dream Job - Learning Simplified From Home! Kickstart your career & boost your employability by helping you discover your skills, talents and interests with our special IT for Recruiters Course. You'll create a pathway to your ideal job as this course is designed to uplift your career in the relevant industry. It provides professional training that employers are looking for in today's workplaces. The IT for Recruiters Course is one of the most prestigious training offered at StudyHub and is highly valued by employers for good reason. This IT for Recruiters Course has been designed by industry experts to provide our learners with the best learning experience possible to increase their understanding of their chosen field. This IT for Recruiters Course, like every one of Study Hub's courses, is meticulously developed and well researched. Every one of the topics is divided into elementary modules, allowing our students to grasp each lesson quickly. At StudyHub, we don't just offer courses; we also provide a valuable teaching process. When you buy a course from StudyHub, you get unlimited Lifetime access with 24/7 dedicated tutor support. Why buy this IT for Recruiters? 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 IT for Recruiters 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 IT for Recruiters 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 IT for Recruiters does not require you to have any prior qualifications or experience. You can just enrol and start learning.This IT for Recruiters 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 IT for Recruiters is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Introduction Introduction 00:02:00 IT Fundamentals What is hardware and software 00:04:00 Server 00:08:00 Project Life cycle 00:06:00 Software Development Life Cycle 00:08:00 IT Terms Basics Methodologies 00:09:00 CMS 00:04:00 API 00:04:00 CI/CD 00:04:00 Stacks 00:03:00 Native and Hybrid Native Apps 00:02:00 IT Positions CTO 00:13:00 IT Architect 00:08:00 Product Owner 00:12:00 Project Manager 00:13:00 Product Manager 00:06:00 PO vs PM vs Product Manager 00:07:00 PO vs Product Manager 00:04:00 Business Analyst 00:09:00 Business Intelligence Specialist 00:06:00 Data Engineer 00:05:00 Data Scientist 00:07:00 Data Engineer vs Data Scientist 00:07:00 Agile Coach 00:14:00 Scrum Master 00:08:00 Agile Coach vs Scrum Master 00:01:00 Frontend Developer 00:06:00 Backend Developer 00:06:00 Fullstack Developer 00:04:00 Frontend vs Backend vs Fullstack Developer 00:02:00 iOS Developer 00:03:00 Android Developer 00:04:00 UX Designer 00:09:00 UI Designer 00:08:00 UX vs UI Designer 00:04:00 QA Engineer_Tester 00:09:00 SysAdmin 00:07:00 DevOps 00:05:00 SEO 00:10:00 IT Technologies. Programming Languages and Frameworks Programming languages and frameworks. Intro 00:01:00 Programming languages and frameworks 00:08:00 Java 00:03:00 JavaScript 00:02:00 Python 00:01:00 C 00:02:00 C++ 00:03:00 C# 00:02:00 HTML 00:02:00 PHP 00:02:00 SWIFT 00:02:00 Objective-C 00:01:00 Ruby 00:02:00 SQL 00:02:00 Go (Golang) 00:01:00 Databases Database 00:05:00 Types of Databases 00:07:00 From Recruiter to Recruiter The day of an IT Recruiter 00:05:00 Key principles 00:03:00 Sourcing Tipps 00:03:00 Good Bye Video Good Bye Video 00:01:00

This Life Coaching course is an invaluable resource for anyone looking to improve their situation or turn their passion for helping others into a rewarding career. In today's world, job markets are fiercely competitive. If you lack solid organizational skills, communication skills, and strong listening skills, landing your dream job will be extremely difficult. This course covers all of these topics in-depth to help you improve your skills in those areas. This Life Coaching course will teach you about the duties and responsibilities of a professional life coach, common reasons for consulting with life coaches, how communication skills affect consultation quality, and an introduction to essential life coaching tools and techniques. So do not miss out on this once-in-a-lifetime opportunity and enrol now. When dealing with clients and bosses and during job interviews and presentations, intelligent body language is essential. This course will also teach you how to effectively manage your body language and facial expressions. Do not miss out on this great opportunity; enrol today. Learning Outcomes After completing the Life Coaching course, you will be able to: Describe the roles and goals of life coaching. Determine the most common reasons for consulting with a life coach. Describe everything about coaching. Develop your listening and communication skills and your understanding of how communication affects the quality of consultations. Use neuro-linguistic programming and put it into practice. Determine how body language plays a part in life coaching. Describe the most up-to-date life coaching strategies and processes. Investigate the possibility of owning a life coaching company. Improve your public speaking and negotiating abilities. Why Choose Life Coaching Course from Us? Self-paced course, access available from anywhere. Easy to understand, high-quality study materials of Life Coaching. This Course developed by industry experts. Life Coaching MCQ quiz after each module to assess your learning. Automated and instant assessment results. 24/7 support via live chat, phone call or email. ***Course Included*** Main Course: Life Coaching Course **Free Courses** Course 01: Anger Management Course 02: Level 5 Diploma in Business Analysis Course curriculum Module 1: Life Coaching Fundamentals Module 2: The Process of Life Coaching Module 3: Emerging Communication Skills Module 4: Introduction to Neuro-Linguistics Programming (NLP) Module 5: Mental Skills Development Module 6: Physical Skills Development Module 7: Body Language Module 8: Emerging Organizational Skills Module 9: Developing Creativity Module 10: Improving Presentation Skills Module 11: Developing Effective Negotiation Skills Module 12: Managing Your First Impression Assessment Method After completing each module of the Life Coaching Course, you will find automated MCQ quizzes. To unlock the next module, you need to complete the quiz task and get at least 60% marks. Certification After completing the MCQ/Assignment assessment for this Life Coaching course, you will be entitled to a Certificate of Completion from Training Tale. The certificate is in PDF format, which is completely free to download. A printed version is also available upon request. It will also be sent to you through a courier for £13.99. Who is this course for? This Life Coaching course is open to candidates with no prior expert experience from all backgrounds. This course is perfect for: Life Coaches Students Fresh Graduates Job Seekers People who are serious about loving and assisting others. Requirements There are no specific requirements for this Life Coaching course because it does not require any advanced knowledge or skills. Career path This Life Coaching course gives you a brand-new way to break into the related employment market. It enables you to get extensive experience and the necessary skill in a short time. It will also provide you with the greatest amount of confidence in your ability to grow and improve. By introducing new skills to your CV, you will be able to progress your career and become more successful. Certificates Certificate of completion Digital certificate - Included

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