329 Courses in Cardiff delivered Online

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

Within the course, you have the chance to complete a questionnaire focused on determining your preferences, which will enable you to gain some insights into your Type. As you progress through the course, you are able to check how clear you are with each preference to give you a deeper understanding of your own type.

How to inspire others and build successful relationships. This course is designed to support professionals in business to develop communication skills through understanding and applying knowledge of personality and behaviour in the workplace. It is based upon the book "The Authority Guide to Behaviour in Business: How to Inspire Others and Build Successful Relationships" by Robin Hills (ISBN: 1912300087).

This online delivered course consists of over 4 1/2 hours of presentation, case studies and acupuncture point database including needling videos. A great way to refine your clinical reasoning and treatments for patients with chronic pain. Also can be used as a good refresher, as it recaps the mechanisms behind acupuncture succinctly. See the website for more information

This video course introduces you to the world of Rust programming. You'll learn about the Rust ecosystem, tools, primitive types, and control flow, and gain knowledge of how real-world applications are actually developed in Rust.

Description: In the 21st century, web design is one of the most influential aspects of your online presence. HTML 5, and CSS 3 are the primary foundations of the websites. And with the help of Bootstrap, you will be able to design and develop responsive websites. Master the advanced techniques of HTML 5, CSS 3, and Bootstrap for designing attractive and professional web pages by enrolling in the Diploma in Web Design (HTML5 CSS3 and Bootstrap) course. With our comprehensive course, get started on the journey to web designing. Explore the strategies of developing fascinating websites using HTML 5, and CSS 3 and make your websites responsive using Bootstrap. Additionally, you will look at some general rules to keep in mind while designing web pages. Upon completion, you will be able to design any types of websites using HTML 5, CSS 3, and Bootstrap. Who is the course for? Absolute beginners who want to learn the first building blocks (HTML & CSS) for creating websites Anyone with a website that wants to be able to customize it with HTML and CSS to make it look like you imagine Anyone looking for a quick refresher of how HTML, CSS, and Bootstrap works Entry Requirement: This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Certification: After completing the course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry leading experts; Study in a user-friendly, advanced online learning platform; The UK & internationally recognised accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path: After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. Welcome 1.1 Intro FREE 00:03:00 1.2 Install the Tools and Get Started 00:05:00 HTML 5 2.1 Intro to HTML 00:01:00 2.2 What is HTML 00:11:00 2.3 Start a New HTML File & Use Tags 00:12:00 2.4 Header Tags 00:05:00 2.5 Edit Text 00:09:00 2.6 Links 00:09:00 2.7 Images 00:10:00 2.8 Lists 00:04:00 2.9 Challenge 1 00:16:00 2.10 HTML Outro 00:01:00 CSS 3 3.1 CSS Intro 00:04:00 3.2 Add CSS Styles 00:16:00 3.3 Classes and IDs 00:07:00 3.4 Borders 00:06:00 3.5 Sizing 00:04:00 3.6 Padding and Margin 00:07:00 3.7 Text Styles 00:04:00 3.8 DIVs 00:08:00 3.9 Postioning 00:08:00 3.10 Hover 00:03:00 3.11 Easily Center Elements 00:03:00 3.12 Fonts 00:06:00 3.13 Challenge 00:23:00 3.14 CSS Outro 00:01:00 Bootstrap 4.1 Intro to Bootstrap 00:02:00 4.2 Install Bootstrap 00:10:00 4.3 Indenting and Containers 00:07:00 4.4 The Grid System 00:16:00 4.5 Images 00:07:00 4.6 Buttons 00:06:00 4.7 Challenge 3 00:11:00 4.8 Bootstrap Outro 00:01:00 Project 1 - Design a Landing Page 5.1 Landing Page Intro 00:01:00 5.2 Sketch Your Landing Page 00:05:00 5.3 The Top Section 00:16:00 5.4 Polish the Top Section 00:06:00 5.5 Adding Images 00:11:00 5.6 The Main Points Section 00:14:00 5.7 Collecting Emails With an Opt-In Form 00:11:00 5.8 Challenge 4 00:03:00 5.9 Landing Page Outro 00:02:00 Project 2 - Business Website 6.1 Business Site Intro 00:01:00 6.2 Sketch Up 00:03:00 6.3 Using Fancy Font Logo 00:07:00 6.4 Carousel Basics 00:10:00 6.5 Carousel Extras 00:09:00 6.6 Text on Images 00:15:00 6.7 Phone Number Icon 00:04:00 6.8 Google Maps 00:13:00 6.9 Font Awesome 00:09:00 6.10 Challenge 4 00:08:00 6.11 Business Site Outro 00:01:00 Project 3 - Portfolio 7.1 Intro 00:01:00 7.2 Portfolio Sketch 00:08:00 7.3 Jumbotron 00:10:00 7.4 Nav Bar 00:24:00 7.5 Panels 00:11:00 7.6 Challenge 5 00:13:00 7.7 Portfolio Outre 00:01:00 Hosting - Getting Your Website on the Internet 8.1 Hosting 00:01:00 8.2 Bluehost 00:06:00 8.3 Uploading 00:05:00 8.4 Tips 00:11:00 8.5 Hosting Outro 00:01:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

This course is perfect for the beginner but also delves into building a SAS Model and intermediate topics. Learn SAS Data Step, SQL Step, Macros, SAS Model Building, Predictive Analytics, SAS and ML. If you are using SAS Enterprise Guide and want to learn how to code/program instead of using the point-and-click interface, this course is ideal!

Start your journey with the Linux command line with zero knowledge. This course will start from scratch with instructions to install a Linux OS on a virtual machine and advance to higher-level concepts with no prior knowledge required.

Description: In the 21st century, web design is one of the most influential aspects of your online presence. HTML 5, and CSS 3 are the primary foundations of the websites. And with the help of Bootstrap, you will be able to design and develop responsive websites. Master the advanced techniques of HTML 5, CSS 3, and Bootstrap for designing attractive and professional web pages by enrolling in the The Complete Web Design Course course. With our comprehensive course, get started on the journey to web designing. Explore the strategies of developing fascinating websites using HTML 5, and CSS 3 and make your websites responsive using Bootstrap. Additionally, you will look at some general rules to keep in mind while designing web pages. Upon completion, you will be able to design any types of websites using HTML 5, CSS 3, and Bootstrap. Who is the course for? Absolute beginners who want to learn the first building blocks (HTML & CSS) for creating websites Anyone with a website that wants to be able to customize it with HTML and CSS to make it look like you imagine Anyone looking for a quick refresher of how HTML, CSS, and Bootstrap works Entry Requirement: This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Assessment: At the end of the course, you will be required to sit an online multiple-choice test. Your test will be assessed automatically and immediately so that you will instantly know whether you have been successful. Before sitting for your final exam, you will have the opportunity to test your proficiency with a mock exam. Certification: After completing and passing the course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognised accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path: After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. Welcome 1.1 Intro FREE 00:03:00 1.2 Install the Tools and Get Started 00:05:00 HTML 5 2.1 Intro to HTML 00:01:00 2.2 What is HTML 00:11:00 2.3 Start a New HTML File & Use Tags 00:12:00 2.4 Header Tags 00:05:00 2.5 Edit Text 00:09:00 2.6 Links 00:09:00 2.7 Images 00:10:00 2.8 Lists 00:04:00 2.9 Challenge 1 00:16:00 2.10 HTML Outro 00:01:00 CSS 3 3.1 CSS Intro 00:04:00 3.2 Add CSS Styles 00:16:00 3.3 Classes and IDs 00:07:00 3.4 Borders 00:06:00 3.5 Sizing 00:04:00 3.6 Padding and Margin 00:07:00 3.7 Text Styles 00:04:00 3.8 DIVs 00:08:00 3.9 Postioning 00:08:00 3.10 Hover 00:03:00 3.11 Easily Center Elements 00:03:00 3.12 Fonts 00:06:00 3.13 Challenge 00:23:00 3.14 CSS Outro 00:01:00 Bootstrap 4.1 Intro to Bootstrap 00:02:00 4.2 Install Bootstrap 00:10:00 4.3 Indenting and Containers 00:07:00 4.4 The Grid System 00:16:00 4.5 Images 00:07:00 4.6 Buttons 00:06:00 4.7 Challenge 3 00:11:00 4.8 Bootstrap Outro 00:01:00 Project 1 - Design a Landing Page 5.1 Landing Page Intro 00:01:00 5.2 Sketch Your Landing Page 00:05:00 5.3 The Top Section 00:16:00 5.4 Polish the Top Section 00:06:00 5.5 Adding Images 00:11:00 5.6 The Main Points Section 00:14:00 5.7 Collecting Emails With an Opt-In Form 00:11:00 5.8 Challenge 4 00:03:00 5.9 Landing Page Outro 00:02:00 Project 2 - Business Website 6.1 Business Site Intro 00:01:00 6.2 Sketch Up 00:03:00 6.3 Using Fancy Font Logo 00:07:00 6.4 Carousel Basics 00:10:00 6.5 Carousel Extras 00:09:00 6.6 Text on Images 00:15:00 6.7 Phone Number Icon 00:04:00 6.8 Google Maps 00:13:00 6.9 Font Awesome 00:09:00 6.10 Challenge 4 00:08:00 6.11 Business Site Outro 00:01:00 Project 3 - Portfolio 7.1 Intro 00:01:00 7.2 Portfolio Sketch 00:08:00 7.3 Jumbotron 00:10:00 7.4 Nav Bar 00:24:00 7.5 Panels 00:11:00 7.6 Challenge 5 00:13:00 7.7 Portfolio Outre 00:01:00 Hosting - Getting Your Website on the Internet 8.1 Hosting 00:01:00 8.2 Bluehost 00:06:00 8.3 Uploading 00:05:00 8.4 Tips 00:11:00 8.5 Hosting Outro 00:01:00 Certificate and Transcript Order Your Certificates and Transcripts 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