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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 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.
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
Introduction
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
Types of Data
Types of Statistics
Describing the data graphically
Measures of Centers
Measures of Dispersion
Introduction to Machine Learning
Building a Machine Learning Model
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
The problem statement
Basic equations and Ordinary Least Squared (OLS) method
Assessing Accuracy of predicted coefficients
Assessing Model Accuracy โ RSE and R squared
Simple Linear Regression in R
Multiple Linear Regression
The F โ statistic
Interpreting result for categorical Variable
Multiple Linear Regression in R
Test-Train split
Bias Variance trade-off
Test-Train Split in R
Linear models other than OLS
Subset Selection techniques
Subset selection in R
Shrinkage methods โ Ridge Regression and The Lasso
Ridge regression and Lasso in R
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
Three Classifiers and the problem statement
Why canโt we use Linear 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 in R
Test-Train Split
Test-Train Split in R
K-Nearest Neighbors classifier
K-Nearest Neighbors in R
Understanding the results of classification models
Summary of the three models
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
Classification Trees
The Data set for Classification problem
Building a classification Tree in R
Advantages and Disadvantages of Decision Trees
Bagging
Bagging in R
Random Forest technique
Random Forest in R
Boosting techniques
Gradient Boosting in R
AdaBoosting in R
XGBoosting in R
Content flow
The Concept of a Hyperplane
Maximum Margin Classifier
Limitations of Maximum Margin Classifier
Support Vector classifiers
Limitations of Support Vector Classifiers
Kernel Based Support Vector Machines
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
- Individual
- Business
Course info
Duration
Accredited by
Tutor Support
Course Access
771 Minutes
CPD UK & QLS
Included
1 Year
Course Review
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 donโt 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 itโs 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 learnerโs 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.