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Machine Learning Masterclass
By Study Plex
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- Completion Certificate
- 24/7 Technical Support
Highlights
On-Demand course
All levels
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.
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.
What is CPD?
Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements.
Benefits of CPD
Improve your employment prospects
Boost your job satisfaction
Promotes career advancement
Enhances your CV
Provides you with a competitive edge in the job market
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 |
About The Provider
Study Plex
Birmingham
Study Plex is a company focused on providing a range of learning and development opportunities, to entertain and educate our learner base through the use of modern learning technology.
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