Course Description:
Machine Learning (ML) is the subset of Artificial Intelligence (AI) which is one of the major pillars of the 4th Industrial Revolution(4IR). ML focuses on finding interpretable patterns and structures of data and enables us in learning, reasoning, and decision making in a fast and efficient way.
“The world’s most valuable resource is no longer oil, but data”- The Economists. It is now obvious that to survive in this data-driven world every organization needs to have the right knowledge of leveraging data in the most effective way. Machine Learning model development is the first step to enter this domain of Artificial intelligence and Big data. ML has applications in all types of industries including telecommunication, manufacturing, retail, healthcare and life sciences, travel and hospitality, financial services, energy, and utilities.
This Machine Learning course will show hand-on program development in R. R is now a leading programming language for data analytics and ML. The course starts with an overview of ML, R, and Rstudio. It shows different data transformation and data visualization techniques to equip learners with expertise in data handling. Including essential statistics, this course covers two major topics of predictive analytics – Regression and Classification. With interesting exercises in R, you will be able to handle any strategic challenge of predicting future situations from previous data. This course also covers clustering analysis, Association Rule Learning, and Time Series analysis which will help organizations to find patterns from data and suggest customers/service segmentation, recommendation, and trend forecasting. In the last part, advanced machine learning techniques like NLP and Deep Learning will be covered.
Audience:
- All professionals willing to excel with the data-driven mindset
- Organizations trying to boost performance
- Strategic consultants
- Entrepreneurs
- Students preparing for an excellent career
Prerequisites:
There are no specified prerequisites for this course. Anyone having basic understandings of mathematics and statistics can complete this course to the end.
Why this Course?
With Hands-on R programming, this course covers all essential machine learning techniques with the most suitable way for all professionals. Exercise cases are carefully chosen so that learners can relate this to their work and bring excellence in their organizations. The main objective of this course is to equip business organizations in Bangladesh to become more competitive in the world.
Notes:
- Download the required documents from 1.3: Download documents
- After completing each lesson, you have to click on the “Complete” button to go to the next lesson.
- Must fill up your first name and last name for your certificate.
- You have to complete all quizzes with a minimum 70% correct answer & within 5 Re-take.
- Complete every lesson of this course serially.
- If you click on the “FINISH COURSE” button then the course will be finished, you will unable to complete the next lessons or parts of this course and a Certificate will be generated.
- When you click on the “RE-TAKE” button of the course profile, this course will start as a new course. You have to complete this from the first lesson to the last lesson.
Course Instructor
Course & Training of this Instructor:
Course Features
- Lectures 130
- Quizzes 10
- Duration 7 hours
- Skill level All levels
- Language Bangla
- Students 117
- Certificate Yes
- Assessments Self
- Module 1 : Machine Learning is the Future
- Module 2 : Introducing R and RStudio
- Module 3 : Data Processing
- R Overview Of Module 3
- R Data-Types
- R Data-Structure
- Using String
- Using Matrix
- Data Transformation
- Work with Data Set
- How to Filter Data
- Data Arrange
- Data Selection
- Using Mutate Function
- Using Gather Function
- Date & Time Data
- Using Formula Of Date & Time
- Arrange Date & Time
- Finding Date & Time
- Missing Data
- Further Study
- Quiz
- Module 4 : Data Visualization
- Module 5: Essential Statistics
- Module 6 : Regression Analysis
- Linear Regression
- Solving Problem of Regression Analysis
- Randomly Data Divide
- Simple Linear Regression
- Multiple Linear Regression
- Data Prediction
- Support Vector Regression(SVR)
- Working With Support Vector Regression
- Decision Tree Regression
- Using Algorithm With Decision Tree Regression
- Decision Tree Visualization
- Random Forest Regression
- Working With Random Forest Regression
- Predicted Value
- Model Comparison
- View Smooth Carve
- Predicting From Production Curve
- Further Study
- Quiz
- Module 7 : Classification Analysis
- Overview Module 7
- Review Data
- Data Coveting
- Divided Datasets Into Training & Test Datasets
- Applying Formula of Logistic Regression
- K-Nearest Neighbors(K-Nn) Classification
- Working With K-Nearest Neighbors(K-Nn) Classification
- Support Vector Machines (SVM)
- Working with Support Vector Machines Classification
- Calculating Ypred Value
- Naive Bayes Classification
- Working with Naive Bayes Classification
- Decision Tree Classification
- Working with Decision Tree Classification
- Decision Tree Visualization
- Random Forest Classification
- Working with Random Forest Classification
- Apply Modelin Production Data
- Further Study
- Quiz
- Module 8 : Clustering Analysis
- Module 9 : Association Rule Learning & Time Series Analysis
- Module 10 : Advanced Machine Learning
- Overview of Module 10
- Natural Language Processing
- Row Data To Corpus
- Bag of Words Model
- Dividing Data To Training & Test Sets-1
- Random Forest Prediction
- Deep Learning (Artificial Neural Network)
- Datasets
- Data Conversion
- Divide Data Set
- Using H2o Deep Learning Model 1
- Using H2o Deep Learning Model 2
- Calculating Ypred Value
- Further Study
- Conclusion
- Quiz
Md. Mazharul Islam
Basic of R
The Course is very informative and I learn the basics of R from here. Thank you.Ahmed Khaled
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