Description

Recognised Accreditation

This course is accredited by continuing professional development (CPD). CPD UK is globally recognised by employers, professional organisations, and academic institutions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement.

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Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements.

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Improve your employment prospects

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Promotes career advancement

Enhances your CV

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Demonstrate your dedication

Showcases your professional capabilities

What is IPHM?

The IPHM is an Accreditation Board that provides Training Providers with international and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation is a guarantee of quality and skill.

Benefits of IPHM

It will help you establish a positive reputation in your chosen field

You can join a network and community of successful therapists that are dedicated to providing excellent care to their client

You can flaunt this accreditation in your CV

It is a worldwide recognised accreditation

What is Quality Licence Scheme?

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.

Benefits of Quality License Scheme

Certificate is valuable

Provides a competitive edge in your career

It will make your CV stand out

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
Introduction 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	00:05:00
	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	00:08:00
	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	00:09:00
	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	00:03:00
	SVM based Regression Model in R	00:11:00
Assessment
	Assessment - Machine Learning Masterclass	00:10:00
Certificate of Achievement
	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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Machine Learning Masterclass

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