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Grab the opportunities of the networking world with the Best CompTIA Network+ Certification. Learn Networking Fundamentals, Network Devices, Network Security.

The Disability Assessor course provides comprehensive training in assessing individuals with disabilities for benefit eligibility. It covers medical knowledge, assessment skills, and ethical considerations, preparing students for a rewarding career in this field.

Dive into the comprehensive Trade Lifecycle course, designed to navigate key challenges, strategies, and future trends in trade lifecycle management. From initiation to settlement, gain critical insights into each stage, enhancing your expertise in modern trade processes.

Discover the essential skills and knowledge needed for success in the legal profession with our comprehensive Legal Secretaries Introductory Training course. From legal writing to contract law, litigation skills to employment law, equip yourself with the expertise to thrive in law offices. Enroll now and embark on a rewarding career path in the legal field.

Overview This comprehensive course on Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query 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 Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query. It is available to all students, of all academic backgrounds. Requirements Our Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible on tablets and smartphones so you can access your course on wifi, 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 12 sections • 76 lectures • 06:08:00 total length •Course Outline and Introduction: 00:04:00 •Minimum Requirements for the Course: 00:01:00 •Prepayments Introduction: 00:01:00 •Month End Date Prepaid Expenses Amortization Calculation: 00:00:00 •Exact Prepaid Expenses Payment Date Calculation: 00:00:00 •Prepaid Expenses Accounting Definition: Prepayments: 00:03:00 •Prepaid Expense Example: How Accounting works for Prepayments: 00:03:00 •Advantages and Disadvantages of Prepaid Expenses: 00:03:00 •Introduction to PRO Excel Models and Formulas: 00:06:00 •Date Function: 00:05:00 •EOMONTH Function: 00:04:00 •DATEVALUE function: 00:03:00 •IF Function: 00:08:00 •IFS Function (Office 365 Only): 00:07:00 •VLOOKUP Function: 00:07:00 •MATCH Function: 00:05:00 •INDIRECT Function: 00:02:00 •NAMED Ranges: Name Manager: 00:03:00 •Advanced Version of VLOOKUP Function: 00:07:00 •Introduction to Model and Control Panel Tab (Important Sheet Tab): 00:08:00 •Formula Based Prepaid Expenses Model - Deep Dive (Part 1): 00:05:00 •Formula Based Prepaid Expenses Model - Deep Dive (Part 2): 00:06:00 •Formula Based Prepaid Expenses Model - Deep Dive (Part 3): 00:06:00 •IFS Function - Month End date Prepayment calculation: 00:04:00 •Prepaid Expenses - Closing Balance Summary Tab (Formula Based Summary): 00:09:00 •Protecting Formulas Cells and Fields in the Model: 00:04:00 •Exact Date Prepaid Amortisation calculation Intro: 00:03:00 •Formulas update and Model Changes for Exact Prepaid Exps Calculation: 00:03:00 •Formulas Update for Exact Date Prepaid Exps Amortisation (Part 1): 00:04:00 •Formulas Update for Exact Date Prepaid Exps Amortisation (Part 2): 00:03:00 •Formulas Update for Exact Date Prepaid Exps Amortisation (Part 3): 00:02:00 •Formulas Update for Exact Date Prepaid Exps Amortisation (Part 4): 00:07:00 •IFS Function - Exact Date Prepayments Amortisation: 00:04:00 •Data Validation Controls (Enhancing Data Input Controls with Protection): 00:10:00 •Bonus: Prepayment Model with Opening Balance Calculation (Part 1): 00:08:00 •Bonus: Prepayment Model with Opening Balance Calculation (Part 2): 00:09:00 •Additional Material: Resources: 00:00:00 •Power Query and Pivot Table Prepayment Summary Table Intro: 00:06:00 •What is Power Query and Some Awesome Resources for Power Query learning: 00:07:00 •Power Query and Pivot Table Summary - Deep Dive (Part 1): 00:05:00 •Power Query and Pivot Table Summary - Deep Dive (Part 2): 00:04:00 •Power Query and Pivot Table Summary - Deep Dive (Part 3): 00:05:00 •Power Query and Pivot Table Summary - Deep Dive (Part 4): 00:09:00 •Using Array Formulas to Add Formula Protection: 00:04:00 •Bonus: Allocate Prepaid Expenditure Cost Centre Wise - 1: 00:02:00 •Bonus: Allocate Prepaid Expenditure Cost Centre Wise - 2: 00:08:00 •Bonus: Prepayment Model with Opening Balance Calculation (PQ and PT Version): 00:13:00 •Changing Macros Security in Excel: 00:05:00 •Complete Walkthrough - Advanced VBA Prepaid Expenses Amortisation Model: 00:06:00 •Bonus : New Version - Excel VBA Model for Prepayment Expenditure: 00:08:00 •Dynamic Dashboard Overview: 00:07:00 •Importing Profit and Loss Statements Source Files and creating YTD P&L Sheets: 00:08:00 •Creating Dynamic Data Validation: 00:02:00 •Creating Named Ranges for Dynamic Table Arrays: 00:03:00 •Dynamic Date Column Headings for each Divisional PL Table: 00:02:00 •Dynamic Month and YTD Dashboard tables headings (PRO TIP): 00:03:00 •Dynamic VLOOKUP Formula - Preparing First section of the Dashboard: 00:04:00 •Creating Rolling Dashboard with Dynamic VLOOKUP Function: 00:08:00 •IMPORTANT : Error Checking for your reports/Dashboard (PRO TIP): 00:03:00 •Data Prep for Visualization: AREA Charts (Awesome trick using #NA Function): 00:05:00 •Visualization: AREA Charts for Month - Revenue, Gross Profit and Net Profit: 00:05:00 •Visualization DONUT Charts Revenue, Gross Profit and Net Profit (Part 1): 00:03:00 •Visualization DONUT Charts Revenue, Gross Profit and Net Profit (Part 2): 00:06:00 •Introduction - Formula-less Dashboard - Fully Dynamic and easily refreshed: 00:05:00 •Understanding the data files before building dashboard: 00:02:00 •Consolidating Reports with Power Query (Get & Transform) , How to install PQ: 00:08:00 •Dynamic File Path Trick in Power Query with Parameters (Amazing trick): 00:06:00 •Conditional Cumulative totals with SUMIFS Function: 00:04:00 •Bonus: Conditional Cumulative totals with Power Query Custom Formula (M Code): 00:06:00 •Dashboard Creation - Pivot Table showing Month and YTD KPIs division wise: 00:06:00 •Dashboard Creation Donuts Charts linked with Pivot Table (Replicate Charts fast): 00:08:00 •Dashboard Creation - Line Charts: 00:08:00 •Update Dashboard with Additional Divisional Data with Few Click (Magical): 00:03:00 •Thank you: 00:02:00 •Ultimate Prepaid Expenditure Model (Super Bonus): 00:02:00 •Resources - Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query: 00:00:00

Overview Do you have the skills of a life coach? Have you ever considered a successful career in life coaching and CBT therapy?This Life Coaching and Cognitive Behavioural Therapy (CBT) Complete Bundle course will provide you with a solid understanding of life coaching. It'll cover many practical exercises, enabling you to master tools, techniques and the skills required to start your own coaching practice, if this is your motivation. Moreover, this course is a perfect starting point for anyone who is interested in a therapy career or for those who want to use CBT as a self help model.Don't be late. Enroll today and help to change people's lives with a brand new rewarding career in life coaching and CBT therapy.Please Note: Occupational Therapists and Clinical Psychologists must be registered with the Health and Care Professions Council (HCPC), which is the statutory regulator of the professions. Please note, that completion of the course does not entitle a person to join the HCPC Register or use the protected titles of Occupational Therapist or Clinical Psychologist. 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 Life Coaching and Cognitive Behavioural Therapy (CBT) Complete Bundle. It is available to all students, of all academic backgrounds. Requirements Our Life Coaching and Cognitive Behavioural Therapy (CBT) Complete Bundle is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible on tablets and smartphones so you can access your course on wifi, 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 26 sections • 164 lectures • 10:11:00 total length •What You Will Learn: 00:06:00 •Get To Know Your Instructor: 00:02:00 •How To Get Your CPD Certificates: 00:01:00 •What Is Life Coaching: 00:05:00 •The Benefits Of Becoming A Life Coach: 00:04:00 •The Difference Between Coaching, Therapy, Mentoring & Training: 00:03:00 •The Responsibilities Of A Life Coach: 00:03:00 •Coaching Fundamentals Overview: 00:02:00 •Listening Introduction: 00:02:00 •The 3 Kinds Of Listening As A Coach: 00:05:00 •Your Listening Template: 00:11:00 •Questioning Introduction: 00:01:00 •Questioning & Coaching: 00:03:00 •How Coaching Questions Are Different From Normal Questions: 00:02:00 •The Different Types Of Coaching Questions: 00:12:00 •Questions Summary: 00:03:00 •Building Trust Introduction: 00:01:00 •The Trust Checklist Exercise: 00:03:00 •How To Build Trust As A Coach: 00:08:00 •More Trust Building Strategies: 00:05:00 •Understanding Personalities Overview: 00:03:00 •Big Five Personality Model: 00:02:00 •Myers Briggs Personality Model: 00:04:00 •Your Personality Model: 00:09:00 •How To Bring Out Your Strengths With This Model: 00:10:00 •Coaching Models Introduction: 00:02:00 •GROW Model Explained: 00:05:00 •STEPPPA Model Explained: 00:08:00 •FUEL Model Explained: 00:03:00 •CLEAR Model Explained: 00:04:00 •Why We Will Use The GROW Model: 00:01:00 •Step 1 Introduction: 00:03:00 •Why Defining Your Values Is Important: 00:03:00 •How To Define Your Personal Values: 00:06:00 •What Does Your Ideal Life Look Like?: 00:04:00 •The 4 Biggest Goal Setting Mistakes To Avoid: 00:06:00 •Stretch Goals Explained: 00:03:00 •Why You Should Start With Long Term Goals: 00:05:00 •Setting Your First Long Term Goal: 00:06:00 •Turning Long Term Goals Into Short Term Goals: 00:05:00 •Next Steps: 00:01:00 •Step 2 Introduction: 00:01:00 •Status Quo Explained: 00:06:00 •Wheel Of Life Excercise Explained: 00:06:00 •Big Rocks Exercise Explained: 00:05:00 •Coaching Example Step 2: 00:05:00 •Step 3 Introduction: 00:01:00 •How To Brainstorm With Your Client(s): 00:05:00 •How To Evaluate Your Brainstorming Ideas: 00:05:00 •'Spheres Of Influence' Excerise Explained: 00:03:00 •'Not To Do List' Exercise Explained: 00:04:00 •Coaching Example Step 3: 00:07:00 •Step 4 Introduction: 00:01:00 •The 80/20 Rule Applied To Coaching: 00:04:00 •How To Design An Action Plan: 00:03:00 •Coaching Example Step 4: 00:05:00 •GROW Model Recap: 00:03:00 •Structuring Your Sessions Introduction: 00:01:00 •How To Define Your Coaching Ground Rules: 00:03:00 •How Much Should You Charge: 00:05:00 •The Pre Coaching Call: 00:03:00 •Your First Coaching Session: 00:03:00 •Progress Evaluation Sessions: 00:05:00 •My Recommended Coaching Timeline: 00:03:00 •Client Coach Relationship Intro: 00:01:00 •What Clients Expect From Their Coaches: 00:04:00 •Why Clients Choose One Coach Over Another: 00:02:00 •Client Coach Boundaries: 00:03:00 •How To Retain Clients Long Term: 00:05:00 •Advanced Coaching Strategies Introduction: 00:01:00 •Reframing Explained: 00:07:00 •Managing Emotional States: 00:07:00 •Understanding Representational Systems: 00:08:00 •Personal SWOT Analysis: 00:06:00 •Johari Window - Developing Self Awareness: 00:05:00 •Giving Good Feedback As A Coach: 00:05:00 •Part 2 Introduction: 00:03:00 •What It Means To Be Healthy: 00:04:00 •What Does A Health Coach Do?: 00:03:00 •How Can You Combine Life Coaching & Health Coaching: 00:02:00 •Diet Introduction: 00:01:00 •Your Health & Diet: How They Are Linked: 00:04:00 •The Consequences Of Following A Bad Diet: 00:04:00 •How To Eat For Longevity: 00:02:00 •What Are Calories? Explained: 00:04:00 •The Relationship Between Bodyweight And Health: 00:05:00 •Calories & Bodyweight In A Healthy Diet: 00:02:00 •The Surprising Results Of The Twinkie Diet: 00:02:00 •The Relationship Between Weight Loss And Health: 00:03:00 •Figuring Out How Many Daily Calories You Need: 00:02:00 •Calculating Your Total Daily Calories: 00:03:00 •Healthy Weight Ranges: 00:02:00 •The Best Foods For A Healthier Life: 00:02:00 •Best Protein Foods: 00:01:00 •Best Carbohydrate Foods: 00:01:00 •Best Dietary Fat Foods: 00:01:00 •How Much Of Each (Protein, Carbs, Fat)?: 00:04:00 •What About The Remaining Calories: 00:01:00 •Client Assessment Introduction: 00:01:00 •Client Interview: 00:04:00 •How To Create A Diet Plan For Clients: 00:09:00 •Calculating BMI: 00:03:00 •Cardio Introduction: 00:01:00 •The Different Types Of Cardio: 00:02:00 •Creating A Cardio Workout For A Client: 00:04:00 •How To Monitor Your Heart Rate During A Workout: 00:02:00 •Cardio vs Weigh Training - Which Is Better For Weight Loss: 00:07:00 •Weight Training Introduction: 00:01:00 •Muscle Building Formula: 00:07:00 •The Science Behind Muscle Growth: 00:04:00 •Weight Training vs Bodyweight Training: 00:02:00 •How To Create A Beginner Workout Plan: 00:09:00 •5 Most Important Aspects Of Lifting Form: 00:03:00 •Progressive Overload: 00:03:00 •Hormones Introduction: 00:02:00 •The 3 Types Of Hormones: 00:03:00 •The Endocrine System Explained: 00:04:00 •Hormones & Heart Disease: 00:04:00 •Anabolic Steroids: Why They Are So Dangerous: 00:04:00 •Most Important Hormones Overview: 00:01:00 •Insulin: 00:04:00 •Glucagon: 00:02:00 •Adrenaline: 00:03:00 •Cortisol: 00:04:00 •Ghrelin: 00:02:00 •Leptin: 00:03:00 •Thyroid Hormones: 00:02:00 •Estrogen: 00:03:00 •Growth Hormone: 00:01:00 •Testosterone: 00:03:00 •How To Build Your Own Supplement Stack: 00:09:00 •Top 3 Beginner Supplements For Muscle Growth: 00:04:00 •The Best Supplements To Boost Immunity: 00:04:00 •The Best Supplements For Vegans & Vegetarians: 00:02:00 •The Best Supplements For Joint & Bone Health: 00:02:00 •The Best Supplements For Fat Loss: 00:04:00 •Apex Learning Motivation Introduction: 00:02:00 •How To Get Out Of A Workout Rut: 00:09:00 •What Is Nutrient Density: 00:04:00 •Is Brown Sugar Better Than White Sugar: 00:03:00 •What Should You Train On Your First Day At The Gym?: 00:07:00 •Are Microwaves Harmful?: 00:05:00 •Fresh vs Frozen Food: Which Is Healthier: 00:04:00 •Part 3 Introdudction: 00:02:00 •The Difference Between Stress & Anxiety: 00:03:00 •Short vs Long Term Stress: 00:02:00 •The Effects Of Stress & Anxiety On Your Body: 00:05:00 •Proven Stress Management Strategies & Techniques: 00:03:00 •The Benefits Of Calm Breathing: 00:03:00 •Breathing Exercise #1 - Three Part Breath: 00:02:00 •Breathing Exercise #2 - Bellows Breath: 00:02:00 •Progressive Muscle Relaxation: 00:03:00 •Complete PMR Routine: 00:07:00 •Sleeping For A Longer Life: 00:04:00 •Tips To Fall Asleep Faster: 00:03:00 •Supplements For Improved Sleep: 00:02:00 •What Is Emotional Self Care: 00:05:00 •How To Accept Yourself: 00:03:00 •Taming Your Inner Critic: 00:06:00 •Getting A Different Perspecitive: 00:04:00 •Acknowledging Your Qualities: 00:03:00 •Getting Rid Of Time Consuming Commitments: 00:06:00 •How To Say 'No' To Others: 00:05:00 •Resources - Life Coach Training - Guideline For The Startup: 00:00:00

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

Overview This comprehensive course on Python for Data Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python for Data Analysis 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 Python for Data Analysis. It is available to all students, of all academic backgrounds. Requirements Our Python for Data Analysis 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 19 sections • 99 lectures • 00:08:00 total length •Welcome & Course Overview: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:37:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method ..: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00 •Resources- Python for Data Analysis: 00:00:00

Overview In this age of technology, data science and machine learning skills have become highly demanding skill sets. In the UK a skilled data scientist can earn around £62,000 per year. If you are aspiring for a career in the IT industry, secure these skills before you start your journey. The Complete Machine Learning & Data Science Bootcamp 2023 course can help you out. This course will introduce you to the essentials of Python. From the highly informative modules, you will learn about NumPy, Pandas and matplotlib. The course will help you grasp the skills required for using python for data analysis and visualisation. After that, you will receive step-by-step guidance on Python for machine learning. The course will then focus on the concepts of Natural Language Processing.  Upon successful completion of the course, you will receive a certificate of achievement. This certificate will help you elevate your resume. So enrol today! 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? Anyone with an interest in learning about data science can enrol in this course. It will help aspiring professionals develop the basic skills to build a promising career. Professionals already working in this can take the course to improve their skill sets. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush.   Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst Course Curriculum 18 sections • 98 lectures • 23:48:00 total length •Welcome & Course Overview6: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:17:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method.: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00

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