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Ryan Ahmed
In this 1.5-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Simple and Multiple Linear Regression.
- Import Key python libraries, datasets and perform data visualization
- Perform exploratory data analysis and standardize the training and testing data.
- Train and Evaluate different regression models using Sci-kit Learn library.
- Build and train an Artificial Neural Network to perform regression.
- Understand the difference between various regression models KPIs such as MSE, RMSE, MAE, R2, and adjusted R2.
Read more
In this 1.5-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Simple and Multiple Linear Regression.
- Import Key python libraries, datasets and perform data visualization
- Perform exploratory data analysis and standardize the training and testing data.
- Train and Evaluate different regression models using Sci-kit Learn library.
- Build and train an Artificial Neural Network to perform regression.
- Understand the difference between various regression models KPIs such as MSE, RMSE, MAE, R2, and adjusted R2.
Read more
In this 1.5-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Simple and Multiple Linear Regression.
- Import Key python libraries, datasets and perform data visualization
- Perform exploratory data analysis and standardize the training and testing data.
- Train and Evaluate different regression models using Sci-kit Learn library.
- Build and train an Artificial Neural Network to perform regression.
- Understand the difference between various regression models KPIs such as MSE, RMSE, MAE, R2, and adjusted R2.
- Assess the performance of regression models and visualize the performance of the best model using various KPIs.
Register for this course and see more details by visiting:
OpenCourser.com/course/ukyiim/mining
What's inside
Syllabus
Mining Quality Prediction
In this hands-on project, we will train machine learning and deep learning models to predict the % of Silica Concentrate in the Iron ore concentrate per minute. This project could be practically used in Mining Industry to get the % Silica Concentrate at a much faster rate compared to the traditional methods. In this hands-on project we will go through the following tasks: (1) Understand the Problem Statement, (2) Import libraries and datasets, (3) Perform Exploratory Data Analysis, (4) Perform Data Visualization, (5) Create Training and Testing Datasets, (6) Train and Evaluate a Gradient Boosting Regressor Model, (7) Train and Evaluate a Decision Tree Regressor Model,(8) Train and Evaluate a Random Forest Regressor Model, (9) Train and Evaluate an Artificial Neural Network Model, (10) Calculate and Print Regression model KPIs.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a foundation for understanding linear regression
Develops models for data visualization and prediction
Covers a range of supervised learning algorithms
Utilizes industry-standard library Scikit-learn
Builds practical skills in model evaluation and performance assessment
Emphasizes the theory behind linear regression
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Save Mining Quality Prediction Using Machine & Deep Learning to your list so you can find it easily later:
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Reviews summary
Well-received course on mining quality prediction
Learners say this course is an excellent introduction to mining quality prediction using machine and deep learning. They largely agree that the guided projects are engaging and practical and provide learners with a strong foundation in this field. One learner notes that the instructor's videos lag, but otherwise, this well-received course is very informative.
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Mining Quality Prediction Using Machine & Deep Learning with these
activities:
Recall basic statistics concepts
Show steps
Refresh your memory on foundational statistics concepts.
Browse courses on
Statistics
Show steps
-
Review the basics of statistics, such as mean, median, and variance.
-
Complete a few practice problems on basic statistics.
Review basic Python programming concepts
Show steps
Ensure a solid foundation in Python before starting the course.
Browse courses on
Python
Show steps
-
Review the basics of Python programming, such as variables, data types, and control flow.
-
Complete a few simple Python coding exercises.
Read 'An Introduction to Statistical Learning'
Show steps
Expand your knowledge of linear regression by reading a comprehensive textbook.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read Chapters 5 and 6 of the book.
-
Complete the exercises at the end of each chapter.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Create a cheat sheet on linear regression formulas
Show steps
Reinforce your understanding of linear regression formulas by creating a cheat sheet.
Browse courses on
Simple Linear Regression
Show steps
-
Identify the key linear regression formulas.
-
Write out the formulas clearly on a single page.
Follow a tutorial on Linear Regression
Show steps
Complete this tutorial to better grasp the fundamentals of linear regression.
Browse courses on
Simple Linear Regression
Show steps
-
Find a comprehensive tutorial on linear regression.
-
Work through the tutorial, taking notes and completing any exercises.
Solve practice problems on linear regression
Show steps
Enhance your understanding of linear regression concepts by solving practice problems.
Browse courses on
Simple Linear Regression
Show steps
-
Obtain a collection of practice problems on linear regression.
-
Solve the problems, showing your work and checking your answers.
Build a regression model using Python
Show steps
Apply your knowledge of linear regression by building a real-world model.
Browse courses on
Simple Linear Regression
Show steps
-
Choose a dataset that is suitable for linear regression.
-
Preprocess the data and split it into training and testing sets.
-
Build and train a linear regression model on the training set.
-
Evaluate the performance of the model on the testing set.
Develop a slide presentation on linear regression
Show steps
Enhance your understanding of linear regression by explaining it to others.
Browse courses on
Simple Linear Regression
Show steps
-
Create a PowerPoint or Google Slides presentation on linear regression.
-
Include sections on the concepts, applications, and limitations of linear regression.
-
Practice delivering the presentation to a friend or family member.
Contribute to an open-source linear regression library
Show steps
Deepen your understanding of linear regression by contributing to a real-world project.
Browse courses on
Simple Linear Regression
Show steps
-
Choose an open-source linear regression library to contribute to.
-
Review the documentation and codebase of the library.
-
Identify an area where you can make a meaningful contribution.
-
Make a pull request to the library with your changes.
Recall basic statistics concepts
Show steps
Refresh your memory on foundational statistics concepts.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review the basics of statistics, such as mean, median, and variance.
- Complete a few practice problems on basic statistics.
Review basic Python programming concepts
Show steps
Ensure a solid foundation in Python before starting the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the basics of Python programming, such as variables, data types, and control flow.
- Complete a few simple Python coding exercises.
Read 'An Introduction to Statistical Learning'
Show steps
Expand your knowledge of linear regression by reading a comprehensive textbook.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 5 and 6 of the book.
- Complete the exercises at the end of each chapter.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Create a cheat sheet on linear regression formulas
Show steps
Reinforce your understanding of linear regression formulas by creating a cheat sheet.
Browse courses on
Simple Linear Regression
Show steps
Show steps
Show steps
- Identify the key linear regression formulas.
- Write out the formulas clearly on a single page.
Follow a tutorial on Linear Regression
Show steps
Complete this tutorial to better grasp the fundamentals of linear regression.
Browse courses on
Simple Linear Regression
Show steps
Show steps
Show steps
- Find a comprehensive tutorial on linear regression.
- Work through the tutorial, taking notes and completing any exercises.
Solve practice problems on linear regression
Show steps
Enhance your understanding of linear regression concepts by solving practice problems.
Browse courses on
Simple Linear Regression
Show steps
Show steps
Show steps
- Obtain a collection of practice problems on linear regression.
- Solve the problems, showing your work and checking your answers.
Build a regression model using Python
Show steps
Apply your knowledge of linear regression by building a real-world model.
Browse courses on
Simple Linear Regression
Show steps
Show steps
Show steps
- Choose a dataset that is suitable for linear regression.
- Preprocess the data and split it into training and testing sets.
- Build and train a linear regression model on the training set.
- Evaluate the performance of the model on the testing set.
Develop a slide presentation on linear regression
Show steps
Enhance your understanding of linear regression by explaining it to others.
Browse courses on
Simple Linear Regression
Show steps
Show steps
Show steps
- Create a PowerPoint or Google Slides presentation on linear regression.
- Include sections on the concepts, applications, and limitations of linear regression.
- Practice delivering the presentation to a friend or family member.
Contribute to an open-source linear regression library
Show steps
Deepen your understanding of linear regression by contributing to a real-world project.
Browse courses on
Simple Linear Regression
Show steps
Show steps
Show steps
- Choose an open-source linear regression library to contribute to.
- Review the documentation and codebase of the library.
- Identify an area where you can make a meaningful contribution.
- Make a pull request to the library with your changes.
Career center
Learners who complete Mining Quality Prediction Using Machine & Deep Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use machine learning, deep learning, and other analytical approaches to solve business problems. This course can help you develop the skills needed to succeed in this role by providing a foundation in regression modeling, a technique commonly used in data science. You will learn how to train and evaluate different regression models using Python libraries such as Sci-kit Learn. This knowledge will be valuable in developing predictive models for a variety of applications, such as predicting customer churn, forecasting sales, and optimizing marketing campaigns.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course can help you build a foundation in machine learning by providing hands-on experience with regression modeling. You will learn how to train and evaluate different regression models using Python libraries such as Sci-kit Learn. This knowledge will be valuable in developing machine learning models for a variety of applications, such as image recognition, natural language processing, and predictive analytics.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course can help you develop the skills needed to succeed in this role by providing a foundation in regression modeling, a technique commonly used in data analysis. You will learn how to train and evaluate different regression models using Python libraries such as Sci-kit Learn. This knowledge will be valuable in developing data-driven insights and making informed decisions.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to inform decision-making. This course can help you develop the skills needed to succeed in this role by providing a foundation in regression modeling, a technique commonly used in statistical analysis. You will learn how to train and evaluate different regression models using Python libraries such as Sci-kit Learn. This knowledge will be valuable in developing statistical models for a variety of applications, such as forecasting, risk assessment, and quality control.
Business Analyst
Business Analyst
Business Analysts use data and analysis to solve business problems. This course can help you develop the skills needed to succeed in this role by providing a foundation in regression modeling, a technique commonly used in business analysis. You will learn how to train and evaluate different regression models using Python libraries such as Sci-kit Learn. This knowledge will be valuable in developing data-driven insights and making informed recommendations.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can help you develop the skills needed to succeed in this role by providing a foundation in regression modeling, a technique commonly used in quantitative analysis. You will learn how to train and evaluate different regression models using Python libraries such as Sci-kit Learn. This knowledge will be valuable in developing quantitative models for a variety of applications, such as risk management, portfolio optimization, and trading strategies.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to improve the efficiency and effectiveness of operations. This course can help you develop the skills needed to succeed in this role by providing a foundation in regression modeling, a technique commonly used in operations research. You will learn how to train and evaluate different regression models using Python libraries such as Sci-kit Learn. This knowledge will be valuable in developing analytical models for a variety of applications, such as supply chain management, logistics, and scheduling.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior and market trends. This course can help you develop the skills needed to succeed in this role by providing a foundation in regression modeling, a technique commonly used in market research. You will learn how to train and evaluate different regression models using Python libraries such as Sci-kit Learn. This knowledge will be valuable in developing market research models for a variety of applications, such as product development, advertising campaigns, and customer segmentation.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course can help you develop the skills needed to succeed in this role by providing a foundation in regression modeling, a technique commonly used in actuarial science. You will learn how to train and evaluate different regression models using Python libraries such as Sci-kit Learn. This knowledge will be valuable in developing actuarial models for a variety of applications, such as insurance pricing, risk management, and financial planning.
Risk Manager
Risk Manager
Risk Managers identify, assess, and manage risks. This course can help you develop the skills needed to succeed in this role by providing a foundation in regression modeling, a technique commonly used in risk management. You will learn how to train and evaluate different regression models using Python libraries such as Sci-kit Learn. This knowledge will be valuable in developing risk management models for a variety of applications, such as risk assessment, portfolio optimization, and business continuity planning.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This course can help you develop the skills needed to succeed in this role by providing a foundation in regression modeling, a technique commonly used in financial analysis. You will learn how to train and evaluate different regression models using Python libraries such as Sci-kit Learn. This knowledge will be valuable in developing financial models for a variety of applications, such as stock valuation, portfolio management, and credit analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful in developing the skills needed to succeed in this role by providing a foundation in machine learning and deep learning. You will learn how to train and evaluate different machine learning and deep learning models using Python libraries such as Sci-kit Learn and TensorFlow. This knowledge may be valuable in developing software applications for a variety of domains, such as image recognition, natural language processing, and predictive analytics.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computing technologies. This course may be useful in developing the skills needed to succeed in this role by providing a foundation in machine learning and deep learning. You will learn how to train and evaluate different machine learning and deep learning models using Python libraries such as Sci-kit Learn and TensorFlow. This knowledge may be valuable in developing new algorithms and techniques for a variety of applications, such as artificial intelligence, robotics, and cybersecurity.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course may be useful in developing the skills needed to succeed in this role by providing a foundation in machine learning and deep learning. You will learn how to train and evaluate different machine learning and deep learning models using Python libraries such as Sci-kit Learn and TensorFlow. This knowledge may be valuable in developing data pipelines for a variety of applications, such as data warehousing, data mining, and big data analytics.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to make informed business decisions. This course may be useful in developing the skills needed to succeed in this role by providing a foundation in machine learning and deep learning. You will learn how to train and evaluate different machine learning and deep learning models using Python libraries such as Sci-kit Learn and TensorFlow. This knowledge may be valuable in developing business intelligence dashboards and reports for a variety of applications, such as customer segmentation, market forecasting, and risk assessment.
For more career information including salaries, visit:
OpenCourser.com/course/ukyiim/mining
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Mining Quality Prediction Using Machine & Deep Learning.
Save
Is considered the bible of deep learning. It provides a comprehensive overview of the field, covering both theoretical foundations and practical applications. It is an invaluable resource for anyone interested in learning about deep learning.
Save
Provides a comprehensive overview of data mining concepts and techniques. It covers topics such as data preprocessing, data mining algorithms, and data visualization. It valuable resource for anyone interested in learning about the fundamentals of data mining.
Provides a comprehensive overview of the mathematical foundations of machine learning. It covers topics such as linear algebra, calculus, probability, and optimization. It valuable resource for anyone interested in learning about the mathematical underpinnings of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone interested in learning about the foundational concepts of machine learning.
Provides a hands-on guide to machine learning, covering both theoretical foundations and practical applications. It focuses on using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It great resource for those interested in implementing machine learning algorithms.
Provides a comprehensive overview of statistical learning methods for sparse data. It covers topics such as regularized regression, variable selection, and high-dimensional data analysis. It valuable resource for anyone interested in learning about machine learning for sparse data.
Provides a comprehensive overview of convex optimization. It covers topics such as convex sets, convex functions, and convex optimization algorithms. It valuable resource for anyone interested in learning about convex optimization.
Provides a comprehensive overview of numerical optimization methods. It covers topics such as unconstrained optimization, constrained optimization, and nonlinear programming. It valuable resource for anyone interested in learning about optimization algorithms.
Covers the fundamentals of data mining, focusing on algorithms for mining massive datasets. It provides an overview of techniques such as frequent itemset mining, clustering, and anomaly detection. It valuable resource for anyone interested in learning about data mining.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ukyiim/mining
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Effort
1.5 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Provides a foundation for understanding linear regression
Develops models for data visualization and prediction
Covers a range of supervised learning algorithms
Utilizes industry-standard library Scikit-learn
Builds practical skills in model evaluation and performance assessment
Emphasizes the theory behind linear regression
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