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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

  During this training course, delegates will acquire the knowledge and skills to plan and carry out internal and external audits in compliance with ISO 19011 and ISO/IEC 17021-1 certification process. Based on practical exercises, you will learn audit techniques and become competent to manage an audit program, audit team, communication with customers, and understand reporting requirements. After acquiring the necessary expertise to perform this audit, you can sit for the exam and gain the "Certified ISO 13485 Lead Auditor' Certificate. By holding this Certificate, delegates can demonstrate that they have the competencies to audit organizations based on best practices. About This Course   Learning Outcomes   Review a Medical Devices Quality Management System based on ISO 13485 Acknowledge the correlation between ISO 13485 and other standards and regulatory frameworks Understand an auditor's role to: plan, lead and follow-up on a management system audit in accordance with ISO 19011 Learn how to lead an audit and audit team Learn how to interpret the requirements of ISO 13485 in the context of a MDQMS audit Acquire the competencies of an auditor to: plan an audit, lead an audit, draft reports, and follow-up on an audit in compliance with ISO 19011   Course Agenda   Day 1: Introduction to Medical Devices Quality Management Systems (MDQMS) and ISO 13485 Day 2: Audit principles, preparation and launching of an audit Day 3: On-site audit activities Day 4: Closing the audit and final exam.   Assessment     Delegates sit a combined exam, consisting of in-course quizzes and exercises, as well as a final essay style exam, consisting of 12 questions, on Day 4 of the course. The overall passing score is 70%, to be achieved within the 150 minute time allowance. Exam results are provided within 24 hours, with both a Certificate and a digital badge provided as proof of success.   Prerequisites   An understanding of ISO 13485 requirements and knowledge of audit principles. What's Included?   Certification fees are included on the exam price Training material containing over 500 pages of information and practical examples will be distributed An attestation of course completion worth 32 CPD (Continuing Professional Development) credits will be issued to the participants who have attended the training course. Who Should Attend?   Auditors seeking to perform Medical Devices Quality Management System (MDQMS) certification audits Managers or consultants seeking to understand the Medical Devices Quality Management System audit requirements Individuals responsible for maintaining conformance with Medical Devices Quality Management System requirements Accreditation Provided by   This course is Accredited by NACS and Administered by the IECB

This course has been specifically designed to assist professionals applying for Real Estate positions where a Real Estate Financial Modelling test is a requisite part of the interview process.

ISO 7101 Healthcare Quality Management System Lead Auditor Course provides participants with the knowledge and skills necessary to lead audits of quality management systems within healthcare organizations. This comprehensive training program covers the principles, processes, and practices of auditing healthcare quality management systems, focusing on key standards and regulations applicable to the healthcare industry.

Certified Associate in Project Management (CAPM)® Exam Prep This course gives you the knowledge you need to pass the exam and covers CAPM®-critical information on project management theory, principles, techniques, and methods Are you planning on taking the CAPM® examination? This course gives you the knowledge you need to pass the exam and covers CAPM®-critical information on project management theory, principles, techniques, and methods. You'll also have an opportunity for practical applications and time to review the kinds of questions you'll find in the CAPM® Exam. What you Will Learn Apply for the CAPM® Examination Develop a personal exam preparation plan Describe the structure, intent, and framework principles of the current edition of the PMBOK® Guide Explain the PMBOK® Guide Knowledge Areas, as well as their inter-relationships with the each other and the Process Groups Getting Started Program orientation The CAPM® certification process Certified Associate in Project Management (CAPM®) Examination Content Outline CAPM® eligibility requirements Code of Ethics and Professional Conduct Application options Foundation Concepts Skills and qualities of a project manager Project management terminology and definitions Relationship of project, program, portfolio, and operations management Project lifecycle approaches Project Integration Management Review Project Integration Management Knowledge Area Develop Project Charter Develop Project Management Plan Direct and Manage Project Work Manage Project Knowledge Monitoring and Controlling Perform Integrated Change Control Close Project or Phase Project Stakeholder Management Review Project Stakeholder Management Knowledge Area Identify Stakeholders Plan Stakeholder Engagement Manage Stakeholder Engagement Monitor Stakeholder Engagement Project Scope Management Review Project Scope Management Knowledge Area Plan Scope Management Collect Requirements Define Scope Create WBS Validate Scope Control Scope Project Schedule Management Review Project Schedule Management Knowledge Area Plan Schedule Management Define Activities Sequence Activities Estimate Activity Durations Develop Schedule Control Schedule Project Cost Management Review Project Cost Management Knowledge Area Plan Cost Management Estimate Costs Determine Budget Control Schedule Project Resource Management Review Project Resource Management Knowledge Area Plan Resource Management Estimate Activity Resources Acquire Resources Develop Team Manage Team Control Resources Project Quality Management Review Project Quality Management Knowledge Area Plan Quality Management Manage Quality Control Quality Project Risk Management Review Project Risk Management Knowledge Area Plan Risk Management Identify Risks Perform Qualitative Risk Analysis Perform Quantitative Risk Analysis Plan Risk Responses Implement Risk Responses Monitor Risks Project Communications Management Review Project Communications Management Knowledge Area Plan Communications Management Manage Communications Monitor Communications Project Procurement Management Review Project Procurement Management Knowledge Area Plan Procurement Management Conduct Procurements Control Procurements Summary and Next Steps Program Review Mock CAPM® Exam Getting Prepared for the CAPM® Exam After the CAPM® Exam

Did You Know? Over 1.5 billion people worldwide are learning English, with over a million jobs available monthly. As the demand for English teachers skyrockets globally, our 12-course Teaching English as a Foreign Language (TEFL/TESOL) bundle will make you an expert in the field. Designed for aspiring and seasoned educators alike, these TEFL/TESOL courses are more than just an educational journey; it's your path to becoming an influential English teacher across continents. Every course in our TEFL/TESOL bundle is CPD accredited and QLS endorsed, ensuring your qualification is recognised and respected worldwide. Imagine holding not one but 24 certificates (12 CPD and 12 QLS, both in PDF and Hardcopy) upon completion. These aren't just certificates; they are proof of your commitment and expertise in English language teaching. A Learning Experience Tailored for Success From mastering advanced English language skills to exploring the intricacies of English grammar, our bundle covers it all. Courses like "120 hours TEFL (TESOL) Masterclass", "Essential TEFL Training", and "Master English Speaking" are just the beginning. You'll learn about the practical aspects with "Lesson Planning for Teaching" and refine your pronunciation skills with the "British English Pronunciation Course". Whether you're starting with the "Basics of English for Beginners" or advancing with "GCSE English Preparation", each course is a stepping stone to excellence. Additionally, we provide full study assistance and career support to guide you towards your dream teaching role. Whether you're eyeing an international teaching career or aiming to excel locally, we're here to support every step of your professional journey. This isn't just education; it's an investment in your future. Enrol in our TEFL/TESOL Course Bundle today and embark on a journey that opens doors to a world of opportunities! Courses Included In this Teaching English as a Foreign Language (TEFL/TESOL): Course 01: Reach an Advanced Level in English as a Foreign Language Course 02: 120 hours TEFL (TESOL) Masterclass Course 03: Essential TEFL Training Course 04: 200 Common English Idioms and Phrases Course 05: Lesson Planning for Teaching Course 06: English: Spelling, Punctuation, and Grammar Course 07: GCSE English Preparation Course 08: Basics of English for Beginners Course 09: British English Pronunciation Course Course 10: Explore the Fundamentals of English Tenses Course 11: Level-3 English Grammar Course Course 12: Master English Speaking 1 What Will You Learn? Upon completing this TEFL/TESOL bundle, you will be able to: Gain advanced proficiency in English language teaching methodologies. Master classroom management and effective lesson planning techniques. Understand and apply over 200 common English idioms and phrases. Develop skills in teaching English grammar, spelling, and punctuation. Learn strategies for teaching English at different proficiency levels. Acquire the ability to teach English pronunciation with a focus on British English. Enhance capabilities in preparing students for English language examinations. Build a strong foundation in the fundamentals of English language teaching. Start a rewarding journey with our TEFL/TESOL course bundle, a comprehensive package of 12 courses designed for the aspiring English language teacher. Accredited by CPD and endorsed by QLS, this bundle sharpens your teaching abilities and equips you with a globally recognised certification. From advanced language techniques to practical classroom strategies, this bundle is your ticket to a successful career in teaching English as a foreign language. Dive into a world of opportunities and make a significant impact in the realm of English education. Enrol today! CPD 120 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This TEFL/TESOL bundle is perfect for: Aspiring educators looking to teach English as a foreign language. Current teachers seeking to enhance their English teaching skills. Individuals aiming to pursue a career in international education. Professionals seeking a change in career towards language education. Anyone with a passion for English language and teaching. Requirements Level 3 qualification or equivalent, e.g. A levels Career path After completing this bundle of courses, you should be able to pursue career paths such as: ESL Teacher: £22,000 - £40,000 per year. Private Tutor: £15 - £40 per hour. Language School Instructor: £25,000 - £45,000 per year. Corporate English Trainer: £30,000 - £50,000 per year. Online English Teacher: £15 - £35 per hour. Educational Content Developer: £30,000 - £60,000 per year. Certificates Certificate of completion Digital certificate - Included Certificate of completion Hard copy certificate - Included

AgilePM® Practitioner This course offers preparation for the Practitioner-level examination to gain the APMG-International™ / Agile Project Management Practitioner Certification. Agile Project Management (AgilePM) is the result of collaboration between APMG-International and The DSDM Consortium. DSDM (Dynamic Systems Development Method) is the longest-established Agile method, launched in 1995, and is the only Agile method to focus on the management of Agile projects. The method has evolved over the years and the DSDM Agile Project Framework is the latest version of which AgilePM is a subset. DSDM has always operated predominantly in the corporate environment and has consistently demonstrated its ability to successfully work with and complement existing corporate processes. APMG-International is a global Examination Institute accredited by The APM Group Ltd. It is one of the Examination Institutes accredited by AXELOS. APMG-International has regional offices located in Australia, China, Denmark, Germany, the Netherlands, Malaysia, the United States, and the United Kingdom. Their portfolio of qualifications includes the Best Practice qualifications of ITIL®, PRINCE2®, MSP®, M_o_R®, and P3O®. AgilePM is one of their specialist management qualifications, which also include Change Management and Service Catalogue. The course covers all the Practitioner elements of the AgilePM Handbook v2 with: Clear explanations of the method and practical examples provided by your course tutor Sample exam paper for the Practitioner-level exams to enrich your knowledge and understanding A case study to allow you to practice the application of the method to an agile project The Traditional Classroom option includes the Practitioner exam to provide you with the right opportunity to verify your new skill set by way of a professional qualification The Virtual Classroom option includes a Practitioner exam voucher to allow you to choose the date and time of your online exam to verify your new skill set by way of a professional qualification What You Will Learn You will learn how to: Identify and apply the concepts, tools, and techniques described in Section 2 (Digging Deeper) of the APMG-International's Agile Project Management Handbook (v2.0) to agile projects Tailor and customize AgilePM to suit the needs of different projects Use AgilePM in conjunction with other project management methods such as PRINCE2® Prepare yourself for the Practitioner exam in AgilePM Roles and Responsibilities - The PM View The roles Key project manager relationships Agile Project Management - Through the Lifecycle The DSDM process and the project lifecycle Project management focus phase by phase The Effective Use of Products The products Deliver on Time - Combining MoSCoW & Timeboxing Ensuring effective prioritisation Bringing MoSCoW and timeboxing together People, Teams, and Interactions Effective communication Collaboration Requirements and User Stories What is a requirement? User stories Estimating - How and When Coping with uncertainty Estimating through the lifecycle Project Planning through the Lifecycle Planning in a DSDM project Planning activities phase by phase Quality - Never Compromise Quality What do we mean by quality? Solution and process quality Risk Management Project risk How DSDM helps mitigate project risk Tailoring the Approach The project approach questionnaire Summary and Next Steps

AgilePM® Practitioner: In-House Training This course offers preparation for the Practitioner-level examination to gain the APMG-International™ / Agile Project Management Practitioner Certification. Agile Project Management (AgilePM) is the result of collaboration between APMG-International and The DSDM Consortium. DSDM (Dynamic Systems Development Method) is the longest-established Agile method, launched in 1995, and is the only Agile method to focus on the management of Agile projects. The method has evolved over the years and the DSDM Agile Project Framework is the latest version of which AgilePM is a subset. DSDM has always operated predominantly in the corporate environment and has consistently demonstrated its ability to successfully work with and complement existing corporate processes. APMG-International is a global Examination Institute accredited by The APM Group Ltd. It is one of the Examination Institutes accredited by AXELOS. APMG-International has regional offices located in Australia, China, Denmark, Germany, the Netherlands, Malaysia, the United States, and the United Kingdom. Their portfolio of qualifications includes the Best Practice qualifications of ITIL®, PRINCE2®, MSP®, M_o_R®, and P3O®. AgilePM is one of their specialist management qualifications, which also include Change Management and Service Catalogue. The course covers all the Practitioner elements of the AgilePM Handbook v2 with: Clear explanations of the method and practical examples provided by your course tutor Sample exam paper for the Practitioner-level exams to enrich your knowledge and understanding A case study to allow you to practice the application of the method to an agile project The Traditional Classroom option includes the Practitioner exam to provide you with the right opportunity to verify your new skill set by way of a professional qualification The Virtual Classroom option includes a Practitioner exam voucher to allow you to choose the date and time of your online exam to verify your new skill set by way of a professional qualification What You Will Learn You will learn how to: Identify and apply the concepts, tools, and techniques described in Section 2 (Digging Deeper) of the APMG-International's Agile Project Management Handbook (v2.0) to agile projects Tailor and customize AgilePM to suit the needs of different projects Use AgilePM in conjunction with other project management methods such as PRINCE2® Prepare yourself for the Practitioner exam in AgilePM Roles and Responsibilities - The PM View The roles Key project manager relationships Agile Project Management - Through the Lifecycle The DSDM process and the project lifecycle Project management focus phase by phase The Effective Use of Products The products Deliver on Time - Combining MoSCoW & Timeboxing Ensuring effective prioritisation Bringing MoSCoW and timeboxing together People, Teams, and Interactions Effective communication Collaboration Requirements and User Stories What is a requirement? User stories Estimating - How and When Coping with uncertainty Estimating through the lifecycle Project Planning through the Lifecycle Planning in a DSDM project Planning activities phase by phase Quality - Never Compromise Quality What do we mean by quality? Solution and process quality Risk Management Project risk How DSDM helps mitigate project risk Tailoring the Approach The project approach questionnaire Summary and Next Steps

Overview This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with Python is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 2 sections • 90 lectures • 10:24:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:08:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:07:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

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