Coursera Machine Learning Guided Projects are designed to help students gain more experience in Machine Learning by doing. By taking Coursera Machine Learning projects, you become more confident as you gain more knowledge.
If you’ve enrolled or completed one of the best machine learning courses of 2020, these guided projects will help you become a Machine Learning Engineer. Here we’ve compiled the list of Machine Learning projects that will help you practice and gain more hands-on experience.
The best thing about Coursera Machine Learning projects is that they provide all the necessary tools. All you’ve to do is follow the instructor’s guide to complete the project. The cost of the Coursera Machine Learning project is $10.
Why is it Important?
In today’s world, just having the Machine Learning certification won’t help you get a job. But it will help you much if you’ve worked on enough projects to have to necessary skills. We recommend that you take more than a few Coursera guided projects to add value to your resume.
Now let’s check out all the best Machine Learning guided projects offered by Coursera. You may also check out all the Machine Learning projects listed in Coursera.
Best Coursera Machine Learning Projects 2020
1. Unsupervised Machine Learning for Customer Market Segmentation
This is the best Machine Learning project for beginners. Here you will train unsupervised machine learning algorithms to carry out customer market segmentation. Learn how you can leverage ML to convert marketing departments and carry out customer segmentation.
Using the elbow method, you’ll learn to obtain the optimal number of clusters. Then you’ll compile and fit unsupervised machine learning models, Principal Component Analysis (PCA). The duration of the guided project is 2 hours. Over 2k students have enrolled for this machine learning project tutorial.
2. Predictive Modelling with Azure Machine Learning Studio
Using Azure Machine Learning Studio, you will build a predictive model without any programming language. To be precise, you will learn to predict the delays in flight using the weather data. The weather data is obtained by the National Oceanic and Atmospheric Association (NOAA) and US Bureau of Transportation Statistics.
To run the experiments on the Microsoft Azure platform, you get $200 worth of free credit and instructions to set up the Azure Machine Learning account. Using Azure’s drag-and-drop modules, you will operationalize the machine learning workflows. Over 2k students have taken this ML project on the Azure platform.
3. Machine Learning Pipelines with Azure ML Studio
This is a project-based course where you will learn to build an end-to-end machine learning pipeline in Azure ML Studio. All this is done without writing a single line of programming code. Here you will build a model where it predicts if the annual income of an individual is more or less than $50,000.
The project course begins with a split-screen video where you will follow the instructions by the guide. All the instructions are provided with step-by-step for ease of learning. After you’ve evaluated the model on test data, you will then deploy the trained model on the Azure Machine Learning web service.
This is a beginner level project course on Machine Learning on the Azure platform. You can complete the project in about 2 hours. So far, over 2.7k students have taken this project course.
4. Machine Learning Feature Selection in Python
This is the best Machine Learning project with Python. Using Python programming language here, you will learn the basic principles of feature selection and extraction. The projects use the ML concepts of Select-K-Best knn-based filtering, Pearson correlation filtering, Recursive feature elimination (RFE), and more.
You will use the Scikit-Learn functions of Python ML library to focus on simple implementation. All the projects function are done on Linux OS. You will also learn to show feature importance estimation, dimensionality reduction, and lasso regularization techniques.
5. Automatic Machine Learning with H2O AutoML and Python
This guided project is an innovative machine learning project on Automatic Machine Learning with H2O AutoML and Python. To take part in this project, it is recommended that you know the Python programming language and Scikit-learn. The process of tuning and training a model is done using H2O’s AutoML.
By the end of the project, you have mastered AutoML and learn to apply automatic ML. You will be able to solve a business analytics problem with AutoML. The project duration is 90 minutes long.
6. Machine Learning with H2O Flow
Machine Learning with H2O Flow is a guided project in Machine Learning from Coursera. Here you will train your ML models with AutoML and H2O flow. All this is done without writing any scripting codes. H20 is the best open-source ML, and AI platform, and this project will teach you all the necessary skills.
H2O has APIs available in Python, R, Scala, and Flow. To get the most out of this project, it is recommended that you know the basics of ML. Having prior experience in training Machine Learning model will help in completing this project. The outcome of this project is that you will solve business analytics problems with AutoML and Flow.
7. Build a Machine Learning Web App with Streamlit and Python
If you’ve previous hands-on experience in writing simple Python scripts, this ML project is highly recommended. Using Python and Streamlit, you will learn to build an interactive web application. This project helps even if you have zero experience in web development.
The web application that you build in this project allows the user to choose the type of classification algorithm they want to use. What’s interesting about this is that you use less than 100 lines of Python code. The instructor does a good job of providing the step-by-step instructions.
8. Visual Machine Learning with Yellowbrick
Visual Machine Learning with the Yellowbrick project course will teach you to check a random forest classifier’s performance. Using the visual diagnostic tools from Yellowbrick, a random forest classifier is done on the Poker Hand data set. The visual Machine Learning topic covered here is both essential and crucial.
The hands-on project is done on Coursera’s Rhyme platform. All the hands-on projects are done on the web browser. To do this, you will be given access to pre-configured cloud desktops to concentrate on doing to project instead of installing the necessary software.
9. Evaluate Machine Learning Models with Yellowbrick
You will learn to build and test a logistic regression classifier using scikit-learn. And learn to steer your Machine Learning workflow using visualization and model diagnostic tools from Yellowbrick. You will build a project to predict if the room in a given apartment is occupied or not. The data to make a prediction is based on humidity, CO2 levels, light, and temperature.
The ML model used here is a logistic regression model for binary classification. This is an intermediate level project on Coursera and needs about 3 hours to complete to project. To work on this project, you will get access to a pre-configured desktop with Yellowbrick, Python, Scikit-learn, and Jupyter installed.
10. Machine Learning: Predict Poisonous Mushrooms using a Random Forest Model and the FFTrees Package in R
This is an interesting Machine Learning project for students where you will learn to predict poisonous mushrooms. To do this, you will use the Random Forest Model and FFTrees package using R. Then, you will check the results using a Confusion Matrix.
Using the R function, you will complete a random training and test set from a single data source. Then practice data exploration using gglot2 and R programming language. This project is an interesting one would take about one hour to complete the project.
11. Machine Learning: Create a Neural Network that Predicts whether an Image is a Car or Airplane
This is a simple project to make a prediction based on the image. Here you will build the Neural Network Model using Keras and the MNIST Data Set. You have significant practice in evaluating model performance and using One Hot Encoding to build a classifier. The neural network model will then used to check if the image is an airplane or a car.
This is the list of best Coursera Machine Learning Guided Projects in 2020. To become successful in the Machine Learning career, we recommend taking more than a few projects to have vast exposure in the field.