Overview
Are you looking to enhance your machine learning proficiency to help boost your career prospects? Building a satisfying career can be a lifelong process but if you want to gain a competitive edge in your job search, look no further!
With this Machine Learning course, you will build the solid foundation you need to kickstart a successful career in the machine learning industry.
The course covers the essential things you need to know to boost your career within the machine learning. It provides you with the ability to improve your earning potential.
Covering everything you need to know to boost your career within machine learning, this industry recognised certification includes 113 comprehensive bite-sized modules that you can learn in your own time, pace and environment. Complete your course and earn your qualification in just 11 hours 57 minutes with dedicated online support you can trust and rely on.
Why Learn With Queen Mary Academy?
- 50,000+ students worldwide
- Start a new career or advance your current one & land your dream job
- Most in-demand skills for today’s job market
- Award-winning Customer Support
- Partnered with biggest accreditors in the world
Benefits You Will Gain
- No hidden fees.
- 24/7 access to the Learning Portal.
- Receive the UK and globally recognised certificate on completion of the course.
- Qualified tutor support and exceptional customer service.
- Access the Learning Portal whenever you want
- Learn from industry-leading experts.
Who is this course for?
Whether you are looking to enhance your employment prospects, boost your CV with essential skills or climb the career ladder to a higher position, this Machine Learning course will work as an initial step towards earning an internationally-recognised qualification that could prove invaluable for your career.
This Machine Learning course has been designed for learners who are looking to gain the skills and credentials to fast track a fulfilling career in machine learning.
Entry Requirement
The Machine Learning course is for learners of all levels, with no specific enrolment requirements; all you need is the commitment to learn, knowledge of the English language, and basic numeracy and IT skills. Students must be over the age of 16.
Certification
Once you have successfully completed the Machine Learning course, you will receive an accredited certificate from Queen Mary Academy as validation of your new skills. Certification is available in PDF format, at the cost of £12, or a hard copy can be sent via post at the cost of £27.
Career path
On successful completion of the Machine Learning course, learners will have the knowledge, skills and credentials to enter the relevant job market, with the confidence to explore a wide range of industry-related professions. Students will be able to add this qualification to their CV/resume, giving them a head start in their chosen field.
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 | 00:05:00 | ||
| 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 | 00:09:00 | ||
| 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 | 00:03:00 | ||
| SVM based Regression Model in R | 00:11:00 | ||
| Order Your Certificate Now | |||
| Order Your Certificate | 00:00:00 | ||
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