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Machine Learning Course

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  • £25.00 (Ex. VAT)
  • Self-Paced Online Learning
  • Course Duration: 13 Hours
  • Course Duration:
  • High-Quality Study Materials
  • CPD Approved Member
  • Recommended by 96% of Students
  • Instant Access 24/7
  • 225 Students Enrolled

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29 STUDENTS ENROLLED

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Machine Learning Course Description

Learn the Latest Skills | Accredited by CPD UK and IPHM | Recognised Certificate | MCQ Based Exam & Tutor Support | Interactive Video Training | Lifetime Access

This course is endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries.

This machine learning online course will guide you in setting R studio and R crash course, data types, statistics, and process to describe the data graphically. You will have a basic knowledge of measures of centers and dispersion. You will be introduced to machine learning, data preprocessing for regression analysis, linear regression models, and regression models other than OLS. This interactive course will educate you on classification models, the three classification models, logistic regression, linear discriminant analysis, and much more. 

By the end of the course, you will have deep knowledge of machine learning, statistics, regression analysis, linear regression, and classification models. This course and/or training programme has been endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. This course and/or training programme is not regulated by Ofqual and is not an accredited qualification. Your training provider will be able to advise you on any further recognition, for example progression routes into further and/or higher education. For further information please visit the Learner FAQs on the Quality Licence Scheme website.


Learning Outcomes:

  • Understand how to create a support vector machine model in R
  • Get to know about Kernel-based support vector machines
  • Understand support vector classifiers and their limitations
  • Know the content flow, the concept of a hyperplane, and maximum margin classifier
  • Learn ensemble technique 3 – GBM, Adaboost, and XGBoost
  • Know the ensemble technique – random forest
  • Understand the ensemble technique – bagging
  • Know the advantages and disadvantages of decision trees
  • Understand a regression tree and the basics of decision trees
  • Learn the simple classification trees and the data set for classification problem
  • Understand how to build a classification tree in R
  • Understand how to compare results from 3 models
  • Learn K-nearest neighbours
  • Understand discriminant analysis

Accredited by CPD UK and IPHM

                                                                   

Accredited by The CPD Certification Service                           International Practitioners of Holistic Medicine (IPHM)


How Will I Benefit?

  • Boost your career in machine learning
  • Deepen your knowledge and skills in your chosen field just in hours not years!
  • Study a course that is easy to follow.
  • Save money and time by studying at your convenient time
  • Have access to a tutor whenever you are in need

So, what are you thinking about! Start getting the benefits by enrolling today!


Why Choose this Machine Learning Course:

  • Accredited by The CPD UK
  • Accredited by International Practitioners of Holistic Medicine (IPHM)
  • Lifetime Access
  • High-quality e-learning study materials 
  • Learn the most in-demand skills
  • Self-paced, no fixed schedules
  • MCQ Exam and 24/7 Support Included
  • Available to students anywhere in the world
  • No hidden fee
  • Study in a user-friendly, advanced online learning platform

Who is this machine learning course for?

This comprehensive course is suitable for those who want a broader understanding of machine learning, data preprocessing for regression analysis, linear regression models, and logistic regression. It is ideal for those who want to take their career to the next level in Machine learning.


Entry Requirement

  • There are no academic entry requirements for this machine learning course, and it is open to students of all academic backgrounds. 
  • As long as you are aged seventeen or over and have a basic grasp of English, numeracy and ICT, you will be eligible to enrol.

Method of Assessment

On successful completion of the course, you will be required to sit an online multiple-choice assessment. The assessment will be evaluated automatically and the results will be given to you immediately.


Certificate of Achievement

Endorsed Certificate from Quality Licence Scheme

After successfully passing the MCQ exam you will be eligible to obtain the machine learning Endorsed Certificate by Quality Licence Scheme. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organization for providing high-quality vocational qualifications across a wide range of industries. It will give you a competitive advantage in your career, making you stand out from all other applicants and employees. There is a Quality Licence Scheme endorsement fee to obtain an endorsed certificate which is £65.

Certificate of Achievement from Lead Academy

After successfully passing the MCQ exam you will be eligible to order your certificate of achievement as proof of your new skill. The certificate of achievement is an official credential that confirms that you successfully finished a course with Lead Academy. Certificate can be obtained in PDF version at a cost of £12, and there is an additional fee to obtain a printed copy certificate which is £35.


Career path

This machine learning course opens a brand new door for you to enter the relevant job market and also provides you with the chance to accumulate in-depth knowledge at the side of needed skills to become flourishing in no time. You will also be able to add your new skills to your CV, enhance your career and become more competitive in your chosen industry.

 

Course Curriculum

Welcome to the course
Introduction 00:02:00
Setting up R Studio and R crash course
Installing R and R studio 00:05:00
Basics of R and R studio 00:10:00
Packages in R 00:10:00
Inputting data part 1: Inbuilt datasets of R 00:04:00
Inputting data part 2: Manual data entry 00:03:00
Inputting data part 3: Importing from CSV or Text files 00:06:00
Creating Barplots in R 00:13:00
Creating Histograms in R 00:06:00
Basics of Statistics
Types of Data 00:04:00
Types of Statistics 00:02:00
Describing the data graphically 00:11:00
Measures of Centers 00:07:00
Measures of Dispersion 00:04:00
Intorduction to Machine Learning
Introduction to Machine Learning 00:16:00
Building a Machine Learning Model 00:08:00
Data Preprocessing for Regression Analysis
Gathering Business Knowledge 00:03:00
Data Exploration 00:03:00
The Data and the Data Dictionary 00:07:00
Importing the dataset into R 00:03:00
Univariate Analysis and EDD 00:03:00
EDD in R 00:12:00
Outlier Treatment 00:04:00
Outlier Treatment in R 00:04:00
Missing Value imputation 00:03:00
Missing Value imputation in R 00:03:00
Seasonality in Data 00:03:00
Bi-variate Analysis and Variable Transformation 00:16:00
Variable transformation in R 00:09:00
Non Usable Variables 00:04:00
Dummy variable creation: Handling qualitative data 00:04:00
Dummy variable creation in R 00:05:00
Correlation Matrix and cause-effect relationship 00:10:00
Correlation Matrix in R 00:08:00
Linear Regression Model
The problem statement 00:01:00
Basic equations and Ordinary Least Squared (OLS) method 00:08:00
Assessing Accuracy of predicted coefficients 00:14:00
Assessing Model Accuracy – RSE and R squared 00:07:00
Simple Linear Regression in R 00:07:00
Multiple Linear Regression 00:05:00
The F – statistic 00:08:00
Interpreting result for categorical Variable 00:05:00
Multiple Linear Regression in R 00:07:00
Test-Train split 00:09:00
Bias Variance trade-off 00:06:00
Test-Train Split in R 00:08:00
Regression models other than OLS
Linear models other than OLS 00:04:00
Subset Selection techniques 00:11:00
Subset selection in R 00:07:00
Shrinkage methods – Ridge Regression and The Lasso 00:07:00
Ridge regression and Lasso in R 00:12:00
Classification Models: Data Preparation
The Data and the Data Dictionary 00:08:00
Importing the dataset into R 00:03:00
EDD in R 00:11:00
Outlier Treatment in R 00:04:00
Missing Value imputation in R 00:03:00
Variable transformation in R 00:06:00
Dummy variable creation in R 00:05:00
The Three classification models
Three Classifiers and the problem statement 00:03:00
Why can’t we use Linear Regression? 00:04:00
Logistic Regression
Logistic Regression 00:08:00
Training a Simple Logistic model in R 00:03:00
Results of Simple Logistic Regression 00:04:00
Logistic with multiple predictors 00:02:00
Training multiple predictor Logistic model in R 00:01:00
Confusion Matrix 00:03:00
Evaluating Model performance 00:07:00
Predicting probabilities, assigning classes and making Confusion Matrix in R 00:06:00
Linear Discriminant Analysis
Linear Discriminant Analysis 00:09:00
Linear Discriminant Analysis in R 00:09:00
K-Nearest Neighbors
Test-Train Split 00:09:00
Test-Train Split in R 00:09:00
K-Nearest Neighbors classifier 00:08:00
K-Nearest Neighbors in R 00:08:00
Comparing results from 3 models
Understanding the results of classification models 00:06:00
Summary of the three models 00:04:00
Simple Decision Trees
Basics of Decision Trees 00:10:00
Understanding a Regression Tree 00:10:00
The stopping criteria for controlling tree growth 00:03:00
The Data set for this part 00:03:00
Importing the Data set into R 00:06:00
Splitting Data into Test and Train Set in R 00:05:00
Building a Regression Tree in R 00:14:00
Pruning a tree 00:04:00
Pruning a Tree in R 00:09:00
Simple Classification Tree
Classification Trees 00:06:00
The Data set for Classification problem 00:01:00
Building a classification Tree in R 00:09:00
Advantages and Disadvantages of Decision Trees 00:01:00
Ensemble technique 1 - Bagging
Bagging 00:06:00
Bagging in R 00:06:00
Ensemble technique 2 - Random Forest
Random Forest technique 00:04:00
Random Forest in R 00:04:00
Ensemble technique 3 - GBM, AdaBoost and XGBoost
Boosting techniques 00:07:00
Gradient Boosting in R 00:07:00
AdaBoosting in R 00:09:00
XGBoosting in R 00:16:00
Maximum Margin Classifier
Content flow 00:01:00
The Concept of a Hyperplane 00:05:00
Maximum Margin Classifier 00:03:00
Limitations of Maximum Margin Classifier 00:02:00
Support Vector Classifier
Support Vector classifiers 00:10:00
Limitations of Support Vector Classifiers 00:01:00
Support Vector Machines
Kernel Based Support Vector Machines 00:06:00
Creating Support Vector Machine Model in R
The Data set for the Classification problem 00:01:00
Importing Data into R 00:08:00
Test-Train Split 00:05:00
Classification SVM model using Linear Kernel 00:16:00
Hyperparameter Tuning for Linear Kernel 00:06:00
Polynomial Kernel with Hyperparameter Tuning 00:10:00
Radial Kernel with Hyperparameter Tuning 00:06:00
The Data set for the Regression problem 00:02:00
SVM based Regression Model in R 00:11:00
Supplementary Resources
Supplementary Resources – Machine Learning Course 01:00:00
Assessment
Assessment – Machine Learning Course 00:10:00
Order Your Certificate Now
Order Your Certificate of Achievement 00:00:00
Get Your Insurance Now
Get Your Insurance Now 00:00:00
Feedback
Feedback 00:00:00

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Course Fee

  • £25.00 (Ex. VAT)
  • Self-Paced Online Learning
  • Course Duration: 13 Hours
  • Course Duration:
  • High-Quality Study Materials
  • CPD Approved Member
  • Recommended by 96% of Students
  • Instant Access 24/7
  • 225 Students Enrolled

Instructors

29 STUDENTS ENROLLED

APPROVED MEMBER

14 Days Money Back Guarantee

  • £25.00 (Ex. VAT)
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