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Scrum Product Owner Exam Prep: In-House Training This workshop prepares you for the Scrum.org PSPO™ I certification. A voucher for the exam and the access information you will need to take the exam will be provided to you via email after you have completed the course. NOTE: If you have participated in any of IIL's other Scrum workshops, you can bypass this program and focus on reading/studying the Scrum Guide and taking practice exams from Scrum.org The Product Owner is responsible for maximizing the value of the product and the work of the Development Team. The Product Owner must be knowledgeable, available, and empowered to make decisions quickly in order for an Agile project to be successful. The Product Owner's key accountability is the Product Backlog. Managing, maintaining, and evolving the Product Backlog involves: Establishing a clear vision that engages the Development Team and stakeholders Clearly expressing Product Backlog items Ordering the items in the Product Backlog to best achieve the vision and goals Ensuring that the Product Backlog is visible, transparent, and clear to all Working with the Development Team throughout the project to create a product that fits the customer's need The Professional Scrum Product Owner™ I (PSPO I) certificate is a Scrum.org credential that enables successful candidates to demonstrate a fundamental level of Scrum mastery. PSPO I credential holders demonstrate an intermediate understanding of the Scrum framework, and how to apply it to maximize the value delivered with a product. They will exhibit a dedication to continued professional development, and a high level of commitment to their field of practice. Scrum.org does not require that you take their own sponsored or any preparatory training. However, training can facilitate your preparation for this credential. And this course is based on IIL's Scrum Product Owner Workshop, which is aligned with The Scrum Guide™. It will provide you with the information you need to pass the exam and IIL will make the arrangements for your online exam. You will be provided with an exam code and instructions, so that you can take the exam at your convenience, any time you are ready after the course. Passwords have no expiration date, but they are valid for one attempt only. See additional exam details on the next page. What you will Learn You'll learn how to: Successfully prepare for the Scrum.org PSPO I exam Identify the characteristics of a successful Product Owner Create a powerful vision statement Apply techniques to understand your customers and the market Manage and engage stakeholders Write effective user stories with acceptance criteria Utilize techniques to visualize and prioritize the Product Backlog Participate in the 5 Scrum events as the Product Owner Understand the Product Owner's role in closing a Scrum project Getting Started Introductions Workshop orientation Exam prep preview Fundamentals Recap Agile Manifesto, values, and mindset Product Owner characteristics Good vs. great Product Owner Product Ownership Product ownership Project vision Understand your customers and market Personas Stakeholder management and engagement The Product Backlog User Stories and Acceptance Criteria Preparing User Stories for a Sprint The Product Backlog Visualizing the Product Backlog Product Backlog Prioritization Technical Debt Sprint Planning and Daily Standups Sprint Planning Planning Poker Team Engagement Daily Standups Sprint Review, Retrospectives, and Closing Sprint Reviews Key Agile Patterns Retrospectives Closing the Project Summary and Next Steps Review of course goals, objectives, and content Exam prep next steps

Scrum Product Owner Exam Prep This workshop prepares you for the Scrum.org PSPO™ I certification. A voucher for the exam and the access information you will need to take the exam will be provided to you via email after you have completed the course. NOTE: If you have participated in any of IIL's other Scrum workshops, you can bypass this program and focus on reading/studying the Scrum Guide and taking practice exams from Scrum.org The Product Owner is responsible for maximizing the value of the product and the work of the Development Team. The Product Owner must be knowledgeable, available, and empowered to make decisions quickly in order for an Agile project to be successful. The Product Owner's key accountability is the Product Backlog. Managing, maintaining, and evolving the Product Backlog involves: Establishing a clear vision that engages the Development Team and stakeholders Clearly expressing Product Backlog items Ordering the items in the Product Backlog to best achieve the vision and goals Ensuring that the Product Backlog is visible, transparent, and clear to all Working with the Development Team throughout the project to create a product that fits the customer's need The Professional Scrum Product Owner™ I (PSPO I) certificate is a Scrum.org credential that enables successful candidates to demonstrate a fundamental level of Scrum mastery. PSPO I credential holders demonstrate an intermediate understanding of the Scrum framework, and how to apply it to maximize the value delivered with a product. They will exhibit a dedication to continued professional development, and a high level of commitment to their field of practice. Scrum.org does not require that you take their own sponsored or any preparatory training. However, training can facilitate your preparation for this credential. And this course is based on IIL's Scrum Product Owner Workshop, which is aligned with The Scrum Guide™. It will provide you with the information you need to pass the exam and IIL will make the arrangements for your online exam. You will be provided with an exam code and instructions, so that you can take the exam at your convenience, any time you are ready after the course. Passwords have no expiration date, but they are valid for one attempt only. See additional exam details on the next page. What you will Learn You'll learn how to: Successfully prepare for the Scrum.org PSPO I exam Identify the characteristics of a successful Product Owner Create a powerful vision statement Apply techniques to understand your customers and the market Manage and engage stakeholders Write effective user stories with acceptance criteria Utilize techniques to visualize and prioritize the Product Backlog Participate in the 5 Scrum events as the Product Owner Understand the Product Owner's role in closing a Scrum project Getting Started Introductions Workshop orientation Exam prep preview Fundamentals Recap Agile Manifesto, values, and mindset Product Owner characteristics Good vs. great Product Owner Product Ownership Product ownership Project vision Understand your customers and market Personas Stakeholder management and engagement The Product Backlog User Stories and Acceptance Criteria Preparing User Stories for a Sprint The Product Backlog Visualizing the Product Backlog Product Backlog Prioritization Technical Debt Sprint Planning and Daily Standups Sprint Planning Planning Poker Team Engagement Daily Standups Sprint Review, Retrospectives, and Closing Sprint Reviews Key Agile Patterns Retrospectives Closing the Project Summary and Next Steps Review of course goals, objectives, and content Exam prep next steps

Project Leadership Skills - Français: On-Demand To be effective within an organization, project managers must have a wide variety of skills and abilities.These include: creating and executing a vision;motivate others;influence without authority;networking;communicate up, down and laterally;the negociation;stakeholder management;and manage conflicts. This workshop aims to develop the soft skills essential to leading a team and creating lasting business change.Participants will gain insight into social science and brain science to motivate and empower others.They will learn and experiment with various influencing strategies and tactics. Participants will also discover their personal communication preferences, strengths and blind spots and learn how to best communicate with others they find 'difficult'.They will learn better how to manage the human side of change and learn strategies for managing each step.In the process.Practical negotiation and conflict management activities reinforce theoretical learning, rooting it in real life and making it actionable. What You Will Learn At the end of this program, you will be able to: Explain the importance of a vision in driving motivation and engagement Apply a scientific approach to better motivate those around you Strategically maximize your personal and positional power for better project results Determine influence and network development strategies necessary for personal development Know how to respond to communication challenges related to different personality styles Make the link between the expectations of stakeholders and the success criteria of a project Assess key stakeholders across different dimensions of complexity Apply the four rules of principled negotiation to real-life conflict situations Recognize key aspects of a physiological response to conflict Make the right choice of tools and techniques to "demine" an emotional situation Maximize different strategies and tactics to manage ambiguity at work Manage vision and purpose / Motivate others Communication and alignment with the vision Link the present to the future The Importance of Purpose The art and science of motivation Network development and influence Positive policy and project success Types of power in an organization power and influence Network Development Best Practices Communication The medium and the message Personality and communication styles Communication challenges Stakeholder management / Negotiation Identification of stakeholders Stakeholder analysis The basics of negotiation reasoned negotiation Manage the conflict Conflict dynamics The Anatomy of Conflict Conflict management approaches and tools Dealing with Ambiguity Summary and Next Steps Review of key concepts Create your personal action plan

Think of a presentation organized as parts of a body: head and eyes, body, legs, and feet. We will guide you to select and outline supporting materials for each main point. Discover how to prepare your introduction and summary to deliver your main points. Your opener and close are the most impactful parts of your presentation. Learning Objectives Explain how to create a presentation using four parts of the "presentation body", Prepare effective visuals, transitions, introductions, and summaries, Write compelling openers, Recommend a closing call to action Target Audience Managers, Team Leaders, Young Professionals, Sales Professionals, Customer Service Teams

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Project Leadership Skills: Virtual In-House Training To be effective within an organization, project managers need to have a wide variety of skills and abilities. Included among these are: creating and executing on a vision; motivating others; influencing without authority; networking; communicating up, down and laterally; negotiating; managing stakeholders; and managing conflict. This highly interactive workshop focuses on building the soft skills that are critical to leading a team and creating sustainable business change. Participants will gain insight into the social science as well as the brain science behind motivating and empowering others. They will learn and experiment with a variety of influencing strategies and tactics. Working in pairs as well as small groups, they will collaborate with others to brainstorm, share experiences, and apply concepts to everyday challenges. Participants will also discover their personal communication preferences, strengths, and blind spots and will gain insight into how best to communicate with others they find 'difficult.' They will gain insight into managing the people side of change, learning strategies for dealing with each step in the process. Hands-on negotiation and conflict management activities enhance the theoretical learning, grounding it in real life and making it actionable. Interweaving role play with experiential learning and group activities, this course will help participants refine a skill set that is invaluable to their organization, and one that transfers easily across their professional and personal lives. What You Will Learn At the end of this course, you will be able to: Explain the importance of vision in driving motivation and engagement Apply science-based research to better motivate those around you Strategically leverage both personal and positional power to achieve positive project results Determine influencing and networking strategies needed for personal growth Identify ways to problem solve communication challenges when others have different personality styles Connect stakeholder expectations to project success criteria Assess key stakeholders across various dimensions of complexity Apply the four rules of principled negotiation to a real-life conflict situation Recognize key aspects of a physiological response to conflict Utilize selected tools and techniques to 'defuse' an emotional situation Leverage various strategies and tactics to successfully deal with ambiguity at work Getting Started / Foundation Concepts Introductions Course structure, goals, and objectives Beginning a personal action plan Managing Vision and Purpose / Motivating Others Communicating and aligning around vision Tying the present to the future The importance of purpose The art and science of motivation Networking and Influencing Positive politics and project success Types of power within organizations Power and influence Networking best practices Communication The medium and the message Personality and communication styles Communication challenges Stakeholder Management and Negotiation Identifying stakeholders Analyzing stakeholders Negotiation basics Principled negotiation Conflict Management Dynamics of conflict The anatomy of conflict Conflict management approaches and tools Dealing with ambiguity Summary and Next Steps Key concepts review Creating your personal action plan

Project Leadership Skills: In-House Training To be effective within an organization, project managers need to have a wide variety of skills and abilities. Included among these are: creating and executing on a vision; motivating others; influencing without authority; networking; communicating up, down and laterally; negotiating; managing stakeholders; and managing conflict. This highly interactive workshop focuses on building the soft skills that are critical to leading a team and creating sustainable business change. Participants will gain insight into the social science as well as the brain science behind motivating and empowering others. They will learn and experiment with a variety of influencing strategies and tactics. Working in pairs as well as small groups, they will collaborate with others to brainstorm, share experiences, and apply concepts to everyday challenges. Participants will also discover their personal communication preferences, strengths, and blind spots and will gain insight into how best to communicate with others they find 'difficult.' They will gain insight into managing the people side of change, learning strategies for dealing with each step in the process. Hands-on negotiation and conflict management activities enhance the theoretical learning, grounding it in real life and making it actionable. Interweaving role play with experiential learning and group activities, this course will help participants refine a skill set that is invaluable to their organization, and one that transfers easily across their professional and personal lives. What You Will Learn At the end of this course, you will be able to: Explain the importance of vision in driving motivation and engagement Apply science-based research to better motivate those around you Strategically leverage both personal and positional power to achieve positive project results Determine influencing and networking strategies needed for personal growth Identify ways to problem solve communication challenges when others have different personality styles Connect stakeholder expectations to project success criteria Assess key stakeholders across various dimensions of complexity Apply the four rules of principled negotiation to a real-life conflict situation Recognize key aspects of a physiological response to conflict Utilize selected tools and techniques to 'defuse' an emotional situation Leverage various strategies and tactics to successfully deal with ambiguity at work Getting Started / Foundation Concepts Introductions Course structure, goals, and objectives Beginning a personal action plan Managing Vision and Purpose / Motivating Others Communicating and aligning around vision Tying the present to the future The importance of purpose The art and science of motivation Networking and Influencing Positive politics and project success Types of power within organizations Power and influence Networking best practices Communication The medium and the message Personality and communication styles Communication challenges Stakeholder Management and Negotiation Identifying stakeholders Analyzing stakeholders Negotiation basics Principled negotiation Conflict Management Dynamics of conflict The anatomy of conflict Conflict management approaches and tools Dealing with ambiguity Summary and Next Steps Key concepts review Creating your personal action plan

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

Earned Value Management: On-Demand: On-Demand Earned Value Management (EVM) incorporates a set of proven practices appropriate for project or program management methodologies. These include integration of program scope, schedule, and cost objectives, establishment of a baseline plan for accomplishment of program objectives and use of earned value techniques for performance measurement during the execution of a program. Earned Value Management (EVM) incorporates a set of proven practices appropriate for project or program management methodologies. These include integration of program scope, schedule, and cost objectives, establishment of a baseline plan for accomplishment of program objectives and use of earned value techniques for performance measurement during the execution of a program. EVM provides a solid platform for risk identification, corrective actions, and management re-planning as may be required over the life of a project or program. The course emphasis is on the latest EVM principles and concepts in accordance with changes and guidelines for Earned Value Management in The Guide to the Project Management Body of Knowledge (PMBOK® Guide) and The Practice Standard for Earned Value Management published by the Project Management Institute. What you Will Learn You'll learn how to: Develop a project baseline, using an effective WBS Record actual project performance Calculate EVM measures Evaluate project performance based on EVM measures Respond to project variances Integrate EVM and risk management Determine how EVM will add value to your organization Develop an EVM implementation plan for your organization Getting Started Introductions Course structure Course goals and objectives Expectations Foundation Concepts Introduction to Earned Value Management (EVM) Benefits of EVM EVM Process Overview Applications of EVM Creating a Work Breakdown Structure Reviewing WBS concepts Reviewing WBS development process (decomposition) Using a WBS to support EVM Building a Project Baseline Defining a project baseline Developing a project baseline Using a project baseline Recording Actuals Recording actuals overview Collecting data for actual project performance Determining earned value - various methods EVM Performance Measures Using current status measures Using forecasting measures Analyzing EVM measures EVM and Risk Management Integrating EVM and Risk Management Using EVM measures in the risk register Exploring how EVM can facilitate reserves management Drawing down contingency reserves Responding to Variances Introduction to variances Process for responding to variances Response options Reporting Project Performance EVM reporting overview Meeting EVM reporting needs Addressing EVM reporting challenges Implementing an EVMS Defining EVMS requirements EVM for Agile projects Tailoring the EVMS Summary and Next Steps Review of content Review of objectives / expectations Personal action plan

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