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The course covers data science and machine learning with R.

Learn the art of landscape sketching and give wings to your creativity! Get started with this Landscape Sketching Course and kickstart a rewarding landscape artist career.  Through this Landscape Sketching Course, you'll get equipped with the skills to create stunning landscape sketches. The course covers clear instructions from drawing basic shapes to adding details, definitions and finishing touches. Going through the lessons, you'll look into the essential elements of landscape design in more facts. On top of that, you'll learn about the line language and how to use it in your sketch. Furthermore, you'll get information on the various linear perspectives and schematics and know how to measure your drawings for maximum accuracy. Enrol now! Learning Objectives Learn to create excellent landscape sketches Familiarise yourself with the vital landscape elements Know about line language and how to use it in your sketches Learn about one-point, two-point and three-point perspective drawings Learn how to add human figures and cars to your landscapes Who is this Course for? This Landscape Sketching Course is ideal for aspiring professionals who wish to gain the relevant skills and knowledge to fast track their careers. It is for those who have little or no knowledge of landscape sketching or those who are new to the field and want to test their skills and knowledge. There are no entry requirements for this course. However, an eye for detail and a creative mind is essential. Entry Requirement This course is available to all learners of all academic backgrounds. A good understanding of the English language, numeracy, and ICT are required to attend this course. CPD Certificate from Course Gate  At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £4.99 and the hard copy for £9.99. Also, you can order both PDF and hardcopy certificates for £12.99. Career Path On successfully completing the Landscape Sketching Course, learners can progress to a more advanced program from our course list. Career opportunities in this field include freelancing or working in-house, within a range of professional settings, with the opportunity to earn a high salary. Related professions in this industry include: Professional Landscape Sketch Designer Professional Landscape Sketch Tricks and Hints Tutor Course Curriculum Module 01: Introduction Introduction 00:03:00 Module 02: First approach, first sketch Preparation to sketching 00:02:00 Placement, measurement, outline 00:05:00 We do sketch: when to stop? 00:06:00 Module 03: Sketching without shading, language of lines The line language 00:06:00 Preparatory part of the line sketch 00:05:00 Using the line language in sketch 00:05:00 Module 04: Linear perspective in landscape Types of linear perspective 00:06:00 One-point perspective scheme in landscape 00:10:00 Two-point perspective scheme in landscape 00:06:00 Module 05: Addendum Adding human figures and cars to landscape 00:05:00 Supplemental Files 00:15:00 Module 06: Two-point perspective Grid, helping with perspective drawing 00:05:00 Using perspective grid in sketch 00:07:00 Finalizing sketch with perspective grid 00:05:00 Module 07: Three-point perspective Three-point perspective using grid 00:07:00 Three-point perspective sketch from life 00:08:00 Three-point perspective without horizon line 00:05:00 Supplemental Files 2 00:35:00 Certificate and Transcript Order Your Certificates or Transcripts 00:00:00

Learn how to style HTML components with CSS3 to build websites and web pages that are visually appealing from beginning to end. Those who want to learn CSS should take this course.

Use deep learning, Computer Vision, and machine learning techniques to build an autonomous car with Python

This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.

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

In this course you will learn how to use the power of Python to train your machine such that your machine starts learning just like human and based on that learning, your machine starts making predictions as well!

11 in 1 "Solidworks" Bundle only at £53 Boost Your Career with Apex Learning and Get Noticed By Recruiters in this Hiring Season! Get Hard Copy + PDF Certificates + Transcript + Student ID Card worth £160 as a Gift - Enrol Now Give a compliment to your career and take it to the next level. This Solidworks bundle will provide you with the essential knowledge to shine in your professional career. Whether you want to develop skills for your next job or elevate your skills for your next promotion, this Solidworks bundle will help you stay ahead of the pack. Throughout the Solidworks programme, it stresses how to improve your competency as a person in your chosen field while also outlining essential career insights in the relevant job sector. Along with this Solidworks course, you will get 10 premium courses, an originalhardcopy, 11 PDF certificates (Main Course + Additional Courses) Student ID card as gifts. This Solidworks Bundle Consists of the following Premium courses: Course 01: Solidworks: Beginner to Advanced Course 02: Solidworks Foundation Training Course 03: Finite Element Analysis Using Solidworks Course 04: Drawing and Illustration Level 2 Course 05: Digital Art - Sketching In Photoshop Course 06: Diploma in Animation Design Course 07: Level 2 Adobe Graphics Design Course Course 08: Adobe Illustrator CC Masterclass Course 09: Advanced Diploma in User Experience UI/UX Design Course 10: Video Editing Course 11: Toon Boom: Create Your First Character Enrol now in Solidworks to advance your career, and use the premium study materials from Apex Learning. How will I get my Certificate? After successfully completing the course, you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (Previously it was £10 * 11 = £110) Hard Copy Certificate: Free (For The Title Course) PS The delivery charge inside the UK is £3.99, and the international students have to pay £9.99. The bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your Solidworks expertise and essential knowledge, which will assist you in reaching your goal. Moreover, you can learn from any place in your own time without travelling for classes. Curriculum of Bundle Course 01: Solidworks: Beginner to Advanced Introduction About the course Course concept Getting to Know Solidworks Creating a New Document User Interface Mouse Movements - Sketch Mouse Movements - Part Sketch Creating a Sketch Document Basic Sketch Tools Dimensions and Relations Mirror Entities Linear and Circular Sketch Pattern Units Sketch to Part Conversion Opening/Saving Part Document Part Modeling Basics Extruded Boss Base Extruded Boss Base Additional Extruded Cut Sketch on the Part Sketch on the New Plane Draft Shell Fillet and Chamfer Convert and Offset Entities Lofted Boss Revolved Boss Swept Boss Part and Sketch Modification Part Modeling Advanced 3D Sketch Density and Material Measuring Tools Linked Dimensions Equations Mirror Hole Wizard Reference Geometry Feature Scope Configurations And Design Table Creating a Design Table Adding Configurations Assembly Basics Inserting Part Relative to the Origin Inserting Parts Relative to Planes Standard Mates Move Components Mirror Component Assembly Advanced Distance and Angle Profile Centre Symmetry and Width Path Mate Linear Coupler Collision Detection Interference Detection Assembly Features Subassembly Drawing Basics Creating a Drawing Sheet Orthographic Views and Edit Sheet Projected View and View Properties Smart Dimension and Model Item Saving Options Relative to Model View Section View Auxiliary View Detail View Broken Out Section Break View Alternate Position View View Focus Drawing Advanced Bill of Materials (BOM) Bom Sort Missing Item in Bom Custom Properties Equations in Bom Show/Hide Component Linked Notes Measuring Tool Isometric Section View Appearance, Scene and Rendering Applying Materials Appearance Working with Woods Working with Glass Scene Light Camera Decals Text Options Rendering Panel Schedule Rendering Animation Creating Motion Study Animation Wizard Applying Motor Appearance and Camera Position Walkthrough Part-1 Walkthrough Part-2 Course 02: Solidworks Foundation Training Unit 01 Introduction Unit 02 Material, Appearance and Scene Unit 03 Lights and Camera Unit 04 Decals and Text Unit 05 Rendering Unit 06 Animation Unit 07 Project Course 03: Finite Element Analysis Using Solidworks INTRODUCTION FEA BASICS FEA ADVANCED Course 04: Drawing and Illustration Level 2 Module 01: Introduction Module 02: Perspective Module 03: Shading Module 04: Measuring Module 05: Still Life Drawing Module 06: Drawing a Portrait Module 07: Drawing a Tree Course 05: Digital Art - Sketching In Photoshop Module 01: Introduction Module 02: Introduction to Photoshop Module 03: Customizing Your Workspace Module 04: Creating a New Document Module 05: Installing Brush Module 06: The Layers Panel And much more... Course 06: Diploma in Animation Design Module 01: INTRODUCTION Module 02: THE 12 PRINCIPLES OF ANIMATION Module 03: JUMPING ELEPHANT Module 04: WALKCYCLE Module 05: RUNCYCLE Module 06: DIALOGUE Course 07: Level 2 Adobe Graphics Design Course Module 01: Basics of Graphic Design Module 02: Foundation in Design Module 03: Adobe Photoshop Basics Module 04: Designers go to Software Module 05: Adobe Illustrator Introduction and Typography Module 06: Creating Logos with Illustrator Module 07: Logo Design Basics Module 08: Adobe in Design and Print Work Introduction Course 08: Adobe Illustrator CC Masterclass Section 01: Getting Started Section 02: Getting Comfortable in the Illustrator Environment Section 03: Working With Selections Section 04: Drawing Shapes Section 05: Managing Objects Section 06: Working With Colour Section 07: Working With Type Section 08: Going Further With Illustrator Section 09: Saving and Exporting Your Work Section 10: Wrapping Up Course 09: Advanced Diploma in User Experience UI/UX Design Module 01: UX/UI Course Introduction Module 02: Introduction To The Web Industry Module 03: Foundations of Graphic Design Module 04: UX Design (User Experience Design) Module 05: UI Design (User Interface Design) Module 06: Optimization Module 07: Starting a Career in UX/UI Design Course 10: Video Editing Module 1- Introduction to the Course Module 2- Download Davinci Resolve Module 3- Project Settings and Overview of Resolve Module 4- The Media Tab Module 5- Cut Tab Overview Module 6- Making Ins and Outs in the Cut Tab with Precision And much more... Course 11: Toon Boom: Create Your First Character Module 01: Introduction Module 02: Class project Module 03: Creating new scene Module 04: Workspace Module 05: Importing images and assets Module 06: Basic Drawing tools And much more... CPD 110 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this bundle. Requirements This course has been designed to be fully compatible with tablets and smartphones. Career path Having this expertise will increase the value of your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included Certificate of completion Hard copy certificate - Included P.S. The delivery charge inside the UK is £3.99, and the international students have to pay £9.99.

Register on the MATLAB Simulink for Electrical Power Engineering today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get an e-certificate as proof of your course completion. The MATLAB Simulink for Electrical Power Engineering is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The MATLAB Simulink for Electrical Power Engineering Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the MATLAB Simulink for Electrical Power Engineering, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Certification Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Course Content Unit 1- Applications on Matrices in MATLAB Module 1- Solving One Non Linear Equation in MATLAB Using Fzero Function 00:15:00 Module 2-Example 1 on Solving Multiple Non Linear Equations in MATLAB Using Fsolve Function 00:15:00 Module 3- Example 2 on Solving Multiple Non Linear Equations in Matlab Using Fsolve 00:13:00 Module 4-Application Multi Level Inverter Part 1 00:25:00 Module 5- Application Multi Level Inverter Part 2 00:05:00 Unit 2-Power Electronics Simulations Using Simulink in MATLAB Module 1-Introduction to MATLAB Simulations Using Simulink 00:04:00 Module 2-Half Wave Uncontrolled Rectifier with R Load Principle of Operation 00:21:00 Module 3- Half Wave Controlled Rectifier R Load Principle of Operation 00:05:00 Module 4-Simulation of Half Wave Controlled Rectifier Using Simulink In Matlab 00:26:00 Module 5- Principle of Operation of Fully Controlled Bridge Rectifier Part 1 00:06:00 Module 6- Principle of Operation of Fully Controlled Bridge Rectifier Part 2 00:06:00 Module 7-Simulation of Bridge Controlled Rectifier 00:16:00 Module 8-AC Chopper with R Load Principle of Operation 00:14:00 Module 9- Simulation of AC Chopper with R and RL Loads in MATLAB 00:11:00 Module 10- Buck Regulator Principle of Operation Part 1 00:16:00 Module 11-Buck Regulator Principle of Operation Part 2 00:17:00 Module 12-Simulation of Buck Regulator in MATLAB 00:14:00 Module 13-Boost Regulator Principle of Operation 00:23:00 Module 14- Simulation of Boost Regulator in MATLAB 00:12:00 Module 15-Buck-Boost Regulator Principle of Operation 00:17:00 Module 16- Simulation of Buck-Boost Regulator 00:09:00 Module 17- Single Phase Half Bridge R-Load 00:15:00 Module 18- Single Phase Half Bridge RL-Load 00:08:00 Module 19-Simulation of Single Phase Half Bridge Inverter 00:18:00 Module 20-Single Phase Bridge Inverter R-Load 00:06:00 Module 21-Single Phase Bridge Inverter RL-Load 00:07:00 Module 22-Simulation of Single Phase Bridge Inverter 00:10:00 Module 23-Three Phase Inverters and Obtaining The Line Voltages 00:15:00 Module 24-Three Phase Inverters and Obtaining The Phase Voltages 00:17:00 Module 25-Simulation of Three Phase Inverter 00:17:00 Module 26-Simulation of Charging and Discharging Capacitor Using Matlab 00:10:00 Unit 3- Solar Energy Simulation Using Simulink in MATLAB Module 1-Separately Excited DC Machine 00:21:00 Module 2-DC Motor Modelling without Load Using Simulink in MATLAB 00:25:00 Module 3-DC Motor Modelling with Load Using Simulink in MALTAB 00:23:00 Module 4-DC Motor Block Simulation Using Power Library in MATLAB 00:16:00 Unit 4- DC Motor Simulation Using Simulink in MATLAB Module 1-Construction and Principle of Operation of Synchronous Generator 00:29:00 Module 2-Equivalent Circuit and Phasor Diagram of Non Salient Synchronous Machine 00:29:00 Module 3-Equivalent Circuit and Phasor Diagram of Salient Synchronous Machine 00:39:00 Module 4-Simulation of Synchronous Machine Connected to Small Power System 00:38:00 Unit 5- Induction Motor Simulation Using Simulink in MATLAB Module 1-Construction and Theory of Operation of Induction Machines 00:27:00 Module 2-Equivalent Circuit and Power Flow in Induction Motor 00:23:00 Module 3-Torque-Speed Characteristics of Induction Motor 00:20:00 Module 4- Simulation of Induction Motor or Asynchronous Motor Using Simulink 00:33:00 Unit 6- Synchronous Generator Simulation in Simulink of MATLAB Module 1- Importing Data from PSCAD Program for Fault Location Detection to MATLAB Program 00:37:00 Unit 7- Power System Simulations Module 1-How to Implement PID Controller in Simulink of MATLAB 00:14:00 Module 2-Tuning a PID Controller In MATLAB Simulink 00:17:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join 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? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. 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 And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 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: 00:04:00 •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: 00:06:00 •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