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Learn complete hands-on Regression analysis for practical Statistical modelling and Machine Learning in R

Overview This comprehensive course on 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 Machine Learning with Python 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 Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our 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 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 4 sections • 21 lectures • 01:34:00 total length •Introduction to types of ML algorithm: 00:02:00 •SVM - Python Implementation: 00:06:00 •Introduction to types of ML algorithm: 00:02:00 •Importing a dataset in python: 00:02:00 •Resolving Missing Values: 00:06:00 •Managing Category Variables: 00:04:00 •Training and Testing Datasets: 00:07:00 •Normalizing Variables: 00:02:00 •Normalizing Variables - Python Code: 00:03:00 •Summary: 00:01:00 •Simple Linear Regression - How it works?: 00:04:00 •Simple Linear Regreesion - Python Implementation: 00:07:00 •Multiple Linear Regression - How it works?: 00:01:00 •Multiple Linear Regression - Python Implementation: 00:09:00 •Decision Trees - How it works?: 00:05:00 •Random Forest - How it works?: 00:03:00 •Decision Trees and Random Forest - Python Implementation: 00:04:00 •kNN - How it works?: 00:02:00 •kNN - Python Implementation: 00:10:00 •Decision Tree Classifier and Random Forest Classifier in Python: 00:10:00 •SVM - How it works?: 00:04:00

The purpose of this course is to teach you how to use Python for machine learning to create real-world algorithms. You will gain an in-depth understanding of the fundamentals of deep learning. This course will help you explore different frameworks in Python to solve real-world problems using the core concepts of deep learning and artificial intelligence.

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

A step-by-step guide that walks you through the fundamentals of Python programming followed using Python libraries to create random forest from scratch. A comprehensive course designed for both beginners with some programming experience or even those who know nothing about ML and random forest!

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Learn to design, plan, and scale cloud implementations with Google Cloud Platform's BigQuery. This course will walk you through the fundamentals of applied machine learning and BigQuery ML along with its history, architecture, and use cases.

Register on the Machine Learning Basics 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 a digital certificate as a proof of your course completion. The Machine Learning Basics 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 Machine Learning Basics Receive an 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) Certificate of Achievement Endorsed Certificate of Achievement from the Quality Licence Scheme Upon successful completion of the final assessment, you will be eligible to apply for the Quality Licence Scheme Endorsed Certificate of achievement. This certificate will be delivered to your doorstep through the post for £119. An extra £10 postage charge will be required for students leaving overseas.  CPD Accredited Certificate After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  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 Machine Learning Basics, 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. Course Content Section 01: Introduction Introduction to Supervised Machine Learning 00:06:00 Section 02: Regression Introduction to Regression 00:13:00 Evaluating Regression Models 00:11:00 Conditions for Using Regression Models in ML versus in Classical Statistics 00:21:00 Statistically Significant Predictors 00:09:00 Regression Models Including Categorical Predictors. Additive Effects 00:20:00 Regression Models Including Categorical Predictors. Interaction Effects 00:18:00 Section 03: Predictors Multicollinearity among Predictors and its Consequences 00:21:00 Prediction for New Observation. Confidence Interval and Prediction Interval 00:06:00 Model Building. What if the Regression Equation Contains 'Wrong' Predictors? 00:13:00 Section 04: Minitab Stepwise Regression and its Use for Finding the Optimal Model in Minitab 00:13:00 Regression with Minitab. Example. Auto-mpg: Part 1 00:17:00 Regression with Minitab. Example. Auto-mpg: Part 2 00:18:00 Section 05: Regression Trees The Basic idea of Regression Trees 00:18:00 Regression Trees with Minitab. Example. Bike Sharing: Part1 00:15:00 Regression Trees with Minitab. Example. Bike Sharing: Part 2 00:10:00 Section 06: Binary Logistics Regression Introduction to Binary Logistics Regression 00:23:00 Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC 00:20:00 Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1 00:16:00 Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2 00:18:00 Section 07: Classification Trees Introduction to Classification Trees 00:12:00 Node Splitting Methods 1. Splitting by Misclassification Rate 00:20:00 Node Splitting Methods 2. Splitting by Gini Impurity or Entropy 00:11:00 Predicted Class for a Node 00:06:00 The Goodness of the Model - 1. Model Misclassification Cost 00:11:00 The Goodness of the Model - 2 ROC. Gain. Lit Binary Classification 00:15:00 The Goodness of the Model - 3. ROC. Gain. Lit. Multinomial Classification 00:08:00 Predefined Prior Probabilities and Input Misclassification Costs 00:11:00 Building the Tree 00:08:00 Classification Trees with Minitab. Example. Maintenance of Machines: Part 1 00:17:00 Classification Trees with Miitab. Example. Maintenance of Machines: Part 2 00:10:00 Section 08: Data Cleaning Data Cleaning: Part 1 00:16:00 Data Cleaning: Part 2 00:17:00 Creating New Features 00:12:00 Section 09: Data Models Polynomial Regression Models for Quantitative Predictor Variables 00:20:00 Interactions Regression Models for Quantitative Predictor Variables 00:15:00 Qualitative and Quantitative Predictors: Interaction Models 00:28:00 Final Models for Duration and Total Charge: Without Validation 00:18:00 Underfitting or Overfitting: The 'Just Right Model' 00:18:00 The 'Just Right' Model for Duration 00:16:00 The 'Just Right' Model for Duration: A More Detailed Error Analysis 00:12:00 The 'Just Right' Model for Total Charge 00:14:00 The 'Just Right' Model for Toral Charge: A More Detailed Error Analysis@@ 00:06:00 Section 10: Learning Success Regression Trees for Duration and TotalCharge 00:18:00 Predicting Learning Success: The Problem Statement 00:07:00 Predicting Learning Success: Binary Logistic Regression Models 00:17:00 Predicting Learning Success: Classification Tree Models 00:09:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 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.

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.

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!