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Machine Learning Basics Course
- 30 Day Money Back Guarantee
- Completion Certificate
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Highlights
On-Demand course
11 hours 17 minutes
All levels
Description
Overview
By enroling in Machine Learning Basics, you can kickstart your vibrant career and strengthen your profound knowledge. You can learn everything you need to know about the topic.
The Machine Learning Basics course includes all of the most recent information to keep you abreast of the employment market and prepare you for your future. The curriculum for this excellent Machine Learning Basics course includes modules at all skill levels, from beginner to expert. You will have the productivity necessary to succeed in your organisation once you have completed our Machine Learning Basics Program.
So enrol in our Machine Learning Basics course right away if you're keen to envision yourself in a rewarding career.
Description
Enroling in this Machine Learning Basics course can improve your Machine Learning Basics perspective, regardless of your skill levels in the Machine Learning Basics topics you want to master. If you're already a Machine Learning Basics expert, this peek under the hood will provide you with suggestions for accelerating your learning, including advanced Machine Learning Basics insights that will help you make the most of your time. This Machine Learning Basics course will act as a guide for you if you've ever wished to excel at Machine Learning Basics.
Why Choose Us?
This course is accredited by the CPD Quality Standards.
Lifetime access to the whole collection of the learning materials.
Online test with immediate results.
Enroling in the course has no additional cost.
You can study and complete the course at your own pace.
Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.
Certificate of Achievement
Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £9.99.
Who Is This Course For?
This Machine Learning Basics course is a great place to start if you're looking to start a new career in Machine Learning Basics field. This training is for anyone interested in gaining in-demand Machine Learning Basics proficiency to help launch a career or their business aptitude.
Requirements
The Machine Learning Basics course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of Machine Learning Basics, without any fuss.
Career Path
This Machine Learning Basics training will assist you develop your Machine Learning Basics ability, establish a personal brand, and present a portfolio of relevant talents. It will help you articulate a Machine Learning Basics professional story and personalise your path to a new career. Furthermore, developing this Machine Learning Basics skillset can lead to numerous opportunities for high-paying jobs in a variety of fields.
Order Your Certificate To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:
Course Curriculum
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 TotalCharge | 00:14:00 | ||
The 'Just Right' Model for ToralCharge: 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 | 00:16:00 | ||
Predicting Learning Success: Classification Tree Models | 00:09:00 | ||
Order Your Certificate | |||
Order Your Certificate | 00:00:00 |
About The Provider
NextGen Learning
London, United Kingdom
NextGen Learning offers futuristic learning tailored for emerging leaders. Dedicated to empowering learners, this platform boasts a vast array of courses, crafted by industry...
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