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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 2397 | yes | Coursera |
The Syllabus
Videos
- Welcome
- The Klopper Research Group
- Assumptions
- Learning a computer language
- Why the Wolfram language?
- Getting Mathematica
- The new Wolfram Cloud
- The Wolfram Cloud
- The Wolfram Programming Lab
- Free-form input and Wolfram Alpha in the Cloud
- Mathematica
- Free-form input and Wolfram Alpha in the desktop
- Help and documentation
- Assignment notebooks
Readings
- How this Course Works
- Welcome to Module 1
- Meet the Course Instructor
- Module 1 Notebook
- Welcome to Wolfram Cloud
- Welcome to Module 3
- Module 3 Notebook
- Module 3 Exercise
Videos
- Create Your Own Computational Essay
- Simulated data demonstration - part 1
- Simulated data demonstration - part 2
- Simple arithmetic
- Addition and subtraction
- Multiplication and division
- Powers
- Arithmetical order
- Calculating a mean
- Working with data
- Lists part 1
- Lists part 2
- Tables
- Index
- Datasets
- Selecting
- Dataset functions
- Creating lists from datasets
- Spreadsheets
- Spreadsheets in the cloud
Readings
- Welcome to Module 4
- Module 4 Notebook
- Welcome to Module 5
- Module 5 Exercise
- Welcome to Module 6
- Module 6 Notebook
- Module 6 Exercise
- Coronavirus data analysis
Practice Exercise
- Modules 1 to 5
Videos
- Summary Statistics
- Descriptive statistics
- Data import for descriptive statistics
- Creating lists for descriptive statistics
- Point estimates
- Measures of dispersion
- Data Visualization
- Data import for visualization
- Scatter plots
- Box plots
- Histograms
- Bar and pie charts
- Distributions
- Probability
- PDF and CDF
- Discrete distributions
- Continuous distributions
- Sampling distributions
- Simulated data
- 01: Introduction to neural networks
- 02: Introduction to machine learning
- 03: The fundamentals
- 04: Basic framework of a neural network
- 05: Layers in a neural network
- 06: Reviewing a neural network
- 07: From inputs to predictions
- 08: Finding a solution
Readings
- Welcome to Module 7
- Module 7 Notebook
- Module 7 Exercise
- Welcome to Module 8
- Module 8 Notebook
- Module 8 Exercise
- Welcome to Module 9
- Module 9 Notebook
- Module 9 Exercise
- Neural networks in the Wolfram language
Practice Exercise
- Modules 6 to 9
- Honors: Deep learning basics
Videos
- Inferential Statistics
- Linear regression
- Importing data
- Descriptive statistics and visualization
- Linear model
- Comparing means
- Data import
- Comparing two means
- Comparing more than two means
- Comparing categorical variables
- Contingency tables
- Chi-squared test
- Creating a Computational Essay
- Data import
- Main research question
- Secondary research questions
- Congratulations on reaching the end
- 09: Introduction to Wolfram Language machine learning
- 10: Automated Machine Learning
- 11: Running an automated algorithm
- 12: Testing the automated algorithm
- 13: Setting the method to neural network
- 14: Normalizing the data
- 15: Manually created neural networks
- 16: Regression - part 1
- 17: Regression - part 2
- 18: Regression - part 3
Readings
- Welcome to Module 10
- Module 10 Notebook
- Module 10 Exercise
- Welcome to Module 11
- Module 11 Notebook
- Module 11 Exercise
- Welcome to Module 12
- Module 12 Notebook
- Module 12 Exercise
- Welcome to Module 13
- Module 13 Notebook
- Final Exam Instructions
- Continuing your journey with deep neural networks
Practice Exercise
- Modules 10 to 13
- Final Exam
- Honors: Deep learning functions
Instructors
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