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Machine Learning Masterclass

Machine Learning Masterclass

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Online Learning

Advance your machine learning expertise through our master class. Dive into the latest advancements, explore practical applications, and develop proficiency in creating intelligent solutions. Join this course to stay ahead in this dynamic field.

What will you learn from this course?

  • Get an introduction to machine learning and how it works
  • Learn aspects and key elements of machine learning 
  • Know the basic types of machine learning 
  • Learn setting up RStudio and R crash course
  • Understand the use of statistics and data mining
  • Learn how to build a machine-learning model
  • Get narrative ideas about data processing and regression for analysis
  • Learn the data set and three classifications of models
  • Know the applications of machine learning 
  • Learn simple decision and classification trees
  • Understand ensemble technique, margin and vector classifier

Machine Learning Masterclass Course

This Machine learning certificate course can be your ultimate educational analysis about machine learning. This course has the efficiency to progress your learning on various concepts such as the fundamentals, types,Rstudio and R crash course, relevance of data mining, machine learning model and so on. Professional training will be included and tutors can help you to choose a career path in machine learning.

  • Fundamentals of machine learning
  • Applications of machine learning
  • Advantages of machine learning and its importance in daily life
  • Techniques and decision trees of machine learning

Who is this Course for?

  • This course is for programmers who want develop a career in machine learning and discover new inventions
  • This course can be enrolled by those who already learned Python and want to implement in machine learning
  • This course has foundational education on machine learning and can be taken by those who are complete beginners
  • This course is also open for professional programmers who already started their career in machine learning and want to educate themselves more
  • Individuals who have passion towards machine learning and want to commit to a future in this field can join this course.

Entry Requirements

This Machine learning online has no academic prerequisites and is open to students from all academic disciplines. You will, however, need a laptop, desktop, tablet, or smartphone and a reliable internet connection.

Assessment

This Machine learning course for beginners assesses learners through multiple-choice questions (MCQs). Upon successful completion of the modules, learners must answer MCQs to complete the assessment procedure. 

  • Through the MCQs, it is measured how much a learner can grasp from each section. 
  • In the assessment, the pass mark is 60%.

Advance Your Career

This Machine learning fundamentals course will provide you with significant opportunities to enter the relevant job market and select your desired career path. Additionally, by showcasing these skills on your resume, you will be able to develop your career, face more competitors in your chosen sector, and increase your level of competition.

If you are looking for a SQL Masterclass course enrol into our affordable and highly informative course, that will open your door towards a wide range of opportunities within your chosen sector.

Recognised Accreditation

 

CPD

CPD certificates are accepted by thousands of professional bodies and government regulators here in the UK and around the world. Many organisations look for employees with CPD requirements, which means, that by doing this course, you would be a potential candidate in your respective field.

QLS

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.

Course Curriculum

Setting up R Studio and R crash course

Installing R and R studio

Basics of R and R studio

Packages in R

Inputting data part 1: Inbuilt datasets of R

Inputting data part 2: Manual data entry

Inputting data part 3: Importing from CSV or Text files

Creating Barplots in R

Creating Histograms in R

Basics of Statistics

Types of Data

Types of Statistics

Describing the data graphically

Measures of Centers

Measures of Dispersion

Introduction to Machine Learning

Introduction to Machine Learning

Building a Machine Learning Model

Data Preprocessing for Regression Analysis

Gathering Business Knowledge

Data Exploration

The Data and the Data Dictionary

Importing the dataset into R

Univariate Analysis and EDD

EDD in R

Outlier Treatment

Outlier Treatment in R

Missing Value imputation

Missing Value imputation in R

Seasonality in Data

Bi-variate Analysis and Variable Transformation

Variable transformation in R

Non Usable Variables

Dummy variable creation: Handling qualitative data

Dummy variable creation in R

Correlation Matrix and cause-effect relationship

Correlation Matrix in R

Linear Regression Model

The problem statement

Basic equations and Ordinary Least Squared (OLS) method

Assessing Accuracy of predicted coefficients

Assessing Model Accuracy – RSE and R squared

Simple Linear Regression in R

Multiple Linear Regression

The F – statistic

Interpreting result for categorical Variable

Multiple Linear Regression in R

Test-Train split

Bias Variance trade-off

Test-Train Split in R

Regression models other than OLS

Linear models other than OLS

Subset Selection techniques

Subset selection in R

Shrinkage methods – Ridge Regression and The Lasso

Ridge regression and Lasso in R

Classification Models: Data Preparation

The Data and the Data Dictionary

Importing the dataset into R

EDD in R

Outlier Treatment in R

Missing Value imputation in R

Variable transformation in R

Dummy variable creation in R

The Three classification models

Three Classifiers and the problem statement

Why can’t we use Linear Regression?

Logistic Regression

Logistic Regression

Training a Simple Logistic model in R

Results of Simple Logistic Regression

Logistic with multiple predictors

Training multiple predictor Logistic model in R

Confusion Matrix

Evaluating Model performance

Predicting probabilities, assigning classes and making Confusion Matrix in R

Linear Discriminant Analysis

Linear Discriminant Analysis

Linear Discriminant Analysis in R

K-Nearest Neighbors

Test-Train Split

Test-Train Split in R

K-Nearest Neighbors classifier

K-Nearest Neighbors in R

Comparing results from 3 models

Understanding the results of classification models

Summary of the three models

Simple Decision Trees

Basics of Decision Trees

Understanding a Regression Tree

The stopping criteria for controlling tree growth

The Data set for this part

Importing the Data set into R

Splitting Data into Test and Train Set in R

Building a Regression Tree in R

Pruning a tree

Pruning a Tree in R

Simple Classification Tree

Classification Trees

The Data set for Classification problem

Building a classification Tree in R

Advantages and Disadvantages of Decision Trees

Ensemble technique 2 - Random Forest

Random Forest technique

Random Forest in R

Ensemble technique 3 - GBM, AdaBoost and XGBoost

Boosting techniques

Gradient Boosting in R

AdaBoosting in R

XGBoosting in R

Maximum Margin Classifier

Content flow

The Concept of a Hyperplane

Maximum Margin Classifier

Limitations of Maximum Margin Classifier

Support Vector Classifier

Support Vector classifiers

Limitations of Support Vector Classifiers

Support Vector Machines

Kernel Based Support Vector Machines

Creating Support Vector Machine Model in R

The Data set for the Classification problem

Importing Data into R

Test-Train Split

Classification SVM model using Linear Kernel

Hyperparameter Tuning for Linear Kernel

Polynomial Kernel with Hyperparameter Tuning

Radial Kernel with Hyperparameter Tuning

The Data set for the Regression problem

SVM based Regression Model in R

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771 Minutes

CPD UK & QLS

Included

1 Year

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Course Reviews

FAQs

Machine learning is a branch of artificial intelligence and computer science that focuses on using data and algorithms to imitate human learning.

The 4 basics of machine learning are supervised learning, unsupervised learning,semi-supervised and reinforcement learning.

Few examples of machine learning can be mentioned such as image recognition, speech recognition, medical diagnosis, statistical arbitrage, predictive analytics etc.

There are several benefits you can get by learning machines such as natural language processing, recognising images, data mining, autonomous vehicles and so on.

Intelligent computer uses AI to think like a human and perform tasks on its own and machine learning is how a computer develops its intelligence.

Python can be the key language for machine learning since it has in-built libraries and packages that provide base-level code so machine learning engineers don’t have to start writing from scratch.

There are basically three types of machine learning which are superived, unsupervised, and reinforcement learning.

Machine learning has a great future ahead and it’s already impacting our daily lives with its innovative inventions.

You can learn machine learning by teaching yourself essential maths skills, study basic computer science skills , earn a necessary degree, learn a programming language, learn specifics about machine skills etc.

After successfully registering for the course, you will receive an email regarding your login details, allowing you access to your online training course.

Upon successfully enrolling for a course, you will receive an email with your login details. Kindly check your inbox/junk folder and spam folder, or you can contact our award-winning customer support team via [email protected].

Sure, you can give any of our courses to your friends or dearest ones. After purchasing any course as a gift, kindly provide the learner’s details via [email protected]. and then we will send your gift to the learner with the login details of the course.

Definitely, you can enrol for this self-paced online video training course. Once you purchase the course, you will receive your login details via email. As you will have lifetime access to the login portal, hence you can complete your course at your convenient time.

Our online courses come with lifetime access with no time limit for completion. Each course is fully accessible from a tablet, mobile or laptop, as long as there is a secure internet connection.

Upon successfully completing the course, you can order your certificate of achievement as proof of your new skill.

After successfully passing the MCQ exam, you will be eligible to order the Machine Learning Certificate by the Quality Licence Scheme.

No prior knowledge is necessary for the course, making it ideal for beginners. However, a positive attitude toward learning is always a plus.