CPD Accredited Course
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Course Overview
Gear up to get well equipped in Machine Learning Basics now, make up your mind to be the best in the business! All you need is proper training, firm support and push to shine in your vocation, and Compliance Central is determined to provide you with it all! Explore what you got in this exclusive Machine Learning Basics, start learning and excel in it! This amazing Machine Learning Basics course has been designed and developed by the industry specialists who had been to the business for years, went through ups-and-downs, climbed up the success ladder with sheer excellence! You’ll get to-the-point knowledge both practical and theoretical, and gain valuable insights on the business which will help you understand the drill better than ever! Join today, be skilled, learn with positive energy and enthusiasm, create an excellent career using your full potential! Brace yourself, enrol now for an amazing venture!This Machine Learning Basics Course Package Includes
- Comprehensive lessons and training provided by experts on Machine Learning Basics
- Interactive online learning experience provided by qualified professionals in your convenience
- 24/7 Access to the course materials and learner assistance
- Easy accessibility from any smart device (Laptop, Tablet, Smartphone etc.)
- A happy and handy learning experience for the professionals and students
- 100% learning satisfaction, guaranteed by Compliance Central
Learning Outcome
Upon successful completion of this highly appreciated Machine Learning Basics course, you’ll be a skilled professional, besides—- You can provide services related to Machine Learning Basics with complete knowledge and confidence
- You’ll be competent and proficient enough to start a Machine Learning Basics related business on your own
- You can train up others and grow an efficient peer community on your locality and serve people
- It will enhance your portfolio, you can use the certificate as proof of your efficiency to the employer
- It will boost up your productivity, you can use the skill and credentials, and become more competent in your vocation with increased earning!
Certification
You can instantly download your certificate for £4.79 right after finishing the Machine Learning Basics course. The hard copy of the certification will also be sent right at your doorstep via post for £10.79. All of our courses are continually reviewed to ensure their quality, and that provide appropriate current training for your chosen subject. As such, although certificates do not expire, it is recommended that they are reviewed or renewed on an annual basis.Who Is This Course For
Compliance Central aims to prepare efficient human resources for the industry and make it more productive than ever. This helpful course is suitable for any person who is interested in Machine Learning Basics. There are no pre-requirements to take it. You can attend the course if you are a student, an enthusiast or a- Employee
- Employer
- Manager
- Supervisor
- Entrepreneur
- Business Professional
- Company Leader
- HR Professional
Course Currilcum
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- Introduction to Supervised Machine Learning 00:06:00
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- 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
- 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
- The Basic idea of Regression Trees 00:18:00
- Regression Trees with Minitab. Example. Bike Sharing: Part 1 00:15:00
- Regression Trees with Minitab. Example. Bike Sharing: Part 2 00:10:00
- 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
- 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 TotalCharge: Without Validation 00:19:00
- 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
Student Reviews
Ben lim
Gaining improve knowledge in the construction project management and the course is easy to understand.
Mr Brian Joseph Keenan
Very good and informative and quick with marking my assignments and issuing my certificate.
Sarah D
Being a support worker I needed add a child care cert in my portfolio. I have done the course and that was really a good course.
Sam Ryder
The first aid course was very informative with well organised curriculum. I already have some bit and pieces knowledge of first aid, this course helped me a lot.
Ben lim
Gaining improve knowledge in the construction project management and the course is easy to understand.
Thelma Gittens
Highly recommended. The module is easy to understand and definitely the best value for money. Many thanks
BF Carey
First course with Compliance Central. It was a good experience.
Course Currilcum
-
- Introduction to Supervised Machine Learning 00:06:00
-
- 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
- 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
- The Basic idea of Regression Trees 00:18:00
- Regression Trees with Minitab. Example. Bike Sharing: Part 1 00:15:00
- Regression Trees with Minitab. Example. Bike Sharing: Part 2 00:10:00
- 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
- 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 TotalCharge: Without Validation 00:19:00
- 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
