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Ryan Ahmed
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 much faster rate compared to the traditional methods.
Register for this course and see more details by visiting:
OpenCourser.com/course/4ijnnk/machine
What's inside
Syllabus
Project Overview
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
Taught by Ryan Ahmed, who are recognized for their work in Machine Learning and Deep Learning for Mining Industry
Teaches data analysis and machine learning techniques for predicting silica concentrate in iron ore concentrate, a skill useful in the mining industry
Builds a strong foundation in a particular area of machine learning
May require learners to come in with basic experience in machine learning
Save this course
Save Machine/Deep Learning for Mining Quality Prediction-Enhanced to your list so you can find it easily later:
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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 Machine/Deep Learning for Mining Quality Prediction-Enhanced with these
activities:
Review the basics of linear regression
Show steps
Reviewing the basics of linear regression will help you to better understand the more advanced concepts covered in this course.
Browse courses on
Linear Regression
Show steps
-
Read a textbook chapter or online article on linear regression
-
Watch a video tutorial on linear regression
-
Complete some practice problems on linear regression
Join a study group or online forum
Show steps
Joining a study group or online forum will help you to connect with other students who are taking this course and learn from each other.
Browse courses on
Machine Learning
Show steps
-
Find a study group or online forum
-
Join the group or forum
-
Participate in discussions
-
Help other students
Follow tutorials on XGBoost
Show steps
Following tutorials on XGBoost will help a student to gain a better understanding of the Gradient Boosting algorithm and how it can be used to solve real-world problems.
Browse courses on
XGBoost
Show steps
-
Identify relevant tutorials
-
Complete the tutorials
-
Practice using XGBoost on your datasets
Four other activities
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Show all seven activities
Solve the Hands-on exercises
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Solving the Hands-on exercises will help a student to reinforce their understanding of regression models and how they can be applied to real-world problems.
Browse courses on
Machine Learning
Show steps
-
Download the exercise datasets
-
Follow the instructions to solve the exercises
-
Compare your solutions with the provided answer key
Participate in a data science workshop
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Participating in a data science workshop will help you to develop new skills and refine existing ones.
Browse courses on
Machine Learning
Show steps
-
Find a data science workshop
-
Register for the workshop
-
Attend the workshop
-
Complete the workshop exercises
-
Network with other participants
Write a blog post about your experience with this project
Show steps
Writing a blog post about your experience with this project will help you to reflect on what you have learned and solidify your understanding of the concepts covered in the course.
Browse courses on
Machine Learning
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-
Choose a topic for your blog post
-
Write a draft of your blog post
-
Edit and revise your blog post
-
Publish your blog post
Build a machine learning model to predict the price of a house
Show steps
Building a machine learning model to predict the price of a house will help you to apply the skills you have learned in this course to a real-world problem.
Browse courses on
Machine Learning
Show steps
-
Gather data on house prices
-
Clean and prepare the data
-
Build a machine learning model
-
Evaluate the performance of the model
-
Deploy the model to a production environment
Review the basics of linear regression
Show steps
Reviewing the basics of linear regression will help you to better understand the more advanced concepts covered in this course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Read a textbook chapter or online article on linear regression
- Watch a video tutorial on linear regression
- Complete some practice problems on linear regression
Join a study group or online forum
Show steps
Joining a study group or online forum will help you to connect with other students who are taking this course and learn from each other.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a study group or online forum
- Join the group or forum
- Participate in discussions
- Help other students
Follow tutorials on XGBoost
Show steps
Following tutorials on XGBoost will help a student to gain a better understanding of the Gradient Boosting algorithm and how it can be used to solve real-world problems.
Browse courses on
XGBoost
Show steps
Show steps
Show steps
- Identify relevant tutorials
- Complete the tutorials
- Practice using XGBoost on your datasets
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve the Hands-on exercises
Show steps
Solving the Hands-on exercises will help a student to reinforce their understanding of regression models and how they can be applied to real-world problems.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Download the exercise datasets
- Follow the instructions to solve the exercises
- Compare your solutions with the provided answer key
Participate in a data science workshop
Show steps
Participating in a data science workshop will help you to develop new skills and refine existing ones.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a data science workshop
- Register for the workshop
- Attend the workshop
- Complete the workshop exercises
- Network with other participants
Write a blog post about your experience with this project
Show steps
Writing a blog post about your experience with this project will help you to reflect on what you have learned and solidify your understanding of the concepts covered in the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a topic for your blog post
- Write a draft of your blog post
- Edit and revise your blog post
- Publish your blog post
Build a machine learning model to predict the price of a house
Show steps
Building a machine learning model to predict the price of a house will help you to apply the skills you have learned in this course to a real-world problem.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Gather data on house prices
- Clean and prepare the data
- Build a machine learning model
- Evaluate the performance of the model
- Deploy the model to a production environment
Career center
Learners who complete Machine/Deep Learning for Mining Quality Prediction-Enhanced will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs and develops machine learning models to solve real-world problems. This course can provide you with the skills and knowledge necessary to become a successful Machine Learning Engineer. Machine Learning Engineers are in high demand, and the skills learned in this course can help up your chances of getting a job in this field.
Data Scientist
Data Scientist
A Data Scientist uses data to solve problems. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate. This skill is in high demand in many industries, and the skills learned in this course can make you more competitive in the job market as a Data Scientist.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make better decisions. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which is a valuable skill for any business. The skills learned in this course can help make you a more competitive candidate for Data Analyst positions.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course teaches you the fundamentals of machine learning and deep learning, which are important skills for any Software Engineer. The skills learned in this course can help you become a more well-rounded Software Engineer and increase your chances of success in this role.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze data and make predictions. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which is a valuable skill if you want to be a success in Quantitative Analysis.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve problems in various industries. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which is a valuable skill in this field.
Business Analyst
Business Analyst
A Business Analyst uses data to understand business needs and develop solutions. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which is valuable information to use when performing your job duties. The skills learned in this course can make you more competitive for Business Analyst positions.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which is a valuable skill for Statisticians to hone.
Data Engineer
Data Engineer
A Data Engineer builds and maintains data pipelines and infrastructure. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which can be useful in building more accurate and efficient data pipelines.
Financial Analyst
Financial Analyst
A Financial Analyst uses data and analysis to make investment decisions. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which can be applied to a variety of financial problems and models.
Market Researcher
Market Researcher
A Market Researcher collects and analyzes data to understand customer behavior. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which is a valuable skill if you want to become a successful Market Researcher.
Product Manager
Product Manager
A Product Manager plans and executes the development of a product. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which can be useful in understanding customer needs and developing better products.
Operations Manager
Operations Manager
An Operations Manager plans and executes the operations of a business. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which can be helpful in optimizing operations.
Risk Analyst
Risk Analyst
A Risk Analyst identifies and assesses risks. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which can be applied to risk modeling.
Consultant
Consultant
A Consultant provides advice and guidance to businesses. This course teaches you how to use machine learning and deep learning to predict the % of Silica Concentrate in the Iron ore concentrate, which can be valuable information to share with clients.
For more career information including salaries, visit:
OpenCourser.com/course/4ijnnk/machine
Reading list
We've selected 12 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
Machine/Deep Learning for Mining Quality Prediction-Enhanced.
Save
Is the definitive reference on deep learning. It covers the latest advances in the field and provides a comprehensive overview of the theory and practice of deep learning.
Save
Provides a comprehensive overview of data mining concepts and techniques, including machine learning and deep learning algorithms. It valuable resource for gaining a deeper understanding of the field and its applications.
Provides a comprehensive overview of data mining techniques for large-scale datasets. It covers topics such as data cleaning, feature engineering, and model selection.
Provides a practical guide to deep learning using Python. It covers the latest advances in the field and provides a comprehensive overview of the theory and practice of deep learning.
Provides a comprehensive overview of data mining and machine learning techniques. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a comprehensive overview of machine learning and data mining techniques. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Covers a wide range of machine learning concepts and algorithms, including supervised and unsupervised learning, feature engineering, and model evaluation. It practical guide for implementing machine learning solutions.
Provides a hands-on guide to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for learning how to implement machine learning models in practice.
Provides a practical guide to deep learning using Fastai and PyTorch. It covers the latest advances in the field and provides a comprehensive overview of the theory and practice of deep learning.
Provides a practical guide to machine learning using R. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a practical guide to machine learning. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a gentle introduction to machine learning. It covers the basic concepts of machine learning and provides a practical guide to implementing machine learning models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4ijnnk/machine
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Taught by Ryan Ahmed, who are recognized for their work in Machine Learning and Deep Learning for Mining Industry
Teaches data analysis and machine learning techniques for predicting silica concentrate in iron ore concentrate, a skill useful in the mining industry
Builds a strong foundation in a particular area of machine learning
May require learners to come in with basic experience in machine learning
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