24 Guided Projects for Data Science Students: Enroll Now!

Share on facebook
Facebook
Share on twitter
Twitter
Share on whatsapp
WhatsApp
Share on linkedin
LinkedIn
Share on email
Email

Coursera’s Guided Projects offer an interactive experience that includes step-by-step instructions from a subject matter expert.

Learn a job-relevant skill that you can use today in under 2 hours through an interactive experience. Access everything you need right in your browser and complete your project confidently.

You can download and keep any of your created files from the Guided Project.

The following are 24 guided projects for Data Science students.

1. Facial Expression Recognition with Keras

2. Custom Prediction Routine on Google AI Platform

3. COVID19 Data Analysis Using Python

4. Linear Regression with NumPy and Python

5. Build a Data Science Web App with Streamlit and Python

6. Computer Vision – Object Tracking with OpenCV and Python

7. Basic Image Classification with TensorFlow

8. Clustering Geolocation Data Intelligently in Python

9. Computer Vision – Image Basics with OpenCV and Python

10. Image Data Augmentation with Keras

11. Image Classification with CNNs using Keras

12. Predicting House Prices with Regression using TensorFlow

13. Perform Sentiment Analysis with scikit-learn

14. Computer Vision – Object Detection with OpenCV and Python

15. Linear Regression with Python

16. Support Vector Machines with scikit-learn

17. Logistic Regression with NumPy and Python

18. Build a Machine Learning Web App with Streamlit and Python

19. Analyze Box Office Data with Plotly and Python

20. Image Compression with K-Means Clustering

21. Predict Future Product Prices Using Facebook Prophet

22. Image Super Resolution Using Autoencoders in Keras

23. Traffic Sign Classification Using Deep Learning in Python/Keras

24. Image Denoising Using AutoEncoders in Keras and Python

1. Facial Expression Recognition with Keras

In this 2-hour long project-based course, you will build and train a convolutional neural network (CNN) in Keras from scratch to recognize facial expressions. The data consists of 48×48 pixel grayscale images of faces. The objective is to classify each face based on the emotion shown in the facial expression into one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).

You will use OpenCV to automatically detect faces in images and draw bounding boxes around them. Once you have trained, saved, and exported the CNN, you will directly serve the trained model to a web interface and perform real-time facial expression recognition on video and image data.

Click here to enroll.

2. Custom Prediction Routine on Google AI Platform

In this 2-hour long project-based course, you will learn how to deploy, and use a model on Google’s AI Platform. Normally, any model trained with the TensorFlow framework is quite easy to deploy, and you can simply upload a Saved Model on Google Storage, and create an AI Platform model with it. But, in practice, we may not always use TensorFlow.

Fortunately, the AI Platform allows for custom prediction routines as well and that’s what we are going to focus on. Instead of converting a Keras model to a TensorFlow Saved Model, we will use the h5 file as is. Additionally, since we will be working with image data, we will use this opportunity to look at encoding and decoding of byte data into string for data transmission and then encoding of the received data in our custom prediction routine on the AI Platform before using it with our model.

Click here to enroll.

3. COVID19 Data Analysis Using Python

In this project, you will learn how to preprocess and merge datasets to calculate needed measures and prepare them for an Analysis. In this project, we are going to work with the COVID19 dataset, published by John Hopkins University, which consists of the data related to the cumulative number of confirmed cases, per day, in each Country.

Also, we have another dataset consist of various life factors, scored by the people living in each country around the globe. We are going to merge these two datasets to see if there is any relationship between the spread of the virus in a country and how happy people are, living in that country.

Click here to enroll.

4. Linear Regression with NumPy and Python

n this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals.

Click here to enroll.

5. Build a Data Science Web App with Streamlit and Python

In this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code! Prior experience with writing simple Python scripts and using pandas for data manipulation is recommended.

Click here to enroll.

6. Computer Vision – Object Tracking with OpenCV and Python

In this 1-hour long project-based course, you will learn how to do Computer Vision Object Tracking from Videos. At the end of the project, you’ll have learned how Optical and Dense Optical Flow work, how to use MeanShift and CamShist and how to do a Single and a Multi-Object Tracking.

Click here to enroll.

7. Basic Image Classification with TensorFlow

In this 2-hour long project-based course, you will learn the basics of using Keras with TensorFlow as its backend and use it to solve a basic image classification problem. By the end of this project, you will have created, trained, and evaluated a Neural Network model that will be able to predict digits from hand-written images with a high degree of accuracy. You also will have learned the fundamentals of neural networks, TensorFlow, and Keras.

Click here to enroll.

8. Clustering Geolocation Data Intelligently in Python

In this 1.5-hour long project, you will learn how to clean and preprocess geolocation data for clustering. You will learn how to export this data into an interactive file that can be better understood for the data. You will learn how to cluster initially with a K-Means approach, before using a more complicated density-based algorithm, DBSCAN. We will discuss how to evaluate these models, and offer improvements to DBSCAN with the introduction of HDBSCAN.

Click here to enroll.

9. Computer Vision – Image Basics with OpenCV and Python

In this 1-hour long project-based course, you will learn how to do Computer Vision on images with OpenCV and Python using Jupyter Notebook.

This course runs on Coursera’s hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and OpenCV pre-installed.

Prerequisites:
In order to be successful in this project, you should have a basic knowledge of Python.

Click here to enroll.

10. Image Data Augmentation with Keras

In this 1.5-hour long project-based course, you will learn how to apply image data augmentation in Keras. We are going to focus on using the ImageDataGenerator class from Keras’ image preprocessing package, and will take a look at a variety of options available in this class for data augmentation and data normalization.

Since this is a practical, project-based course, you will need to prior experience with Python programming, convolutional neural networks, and Keras with a TensorFlow backend.

Data augmentation is a technique used to create more examples, artificially, from an existing dataset. This is useful if your dataset is small and you want to increase the number of examples. Data augmentation can often solve over-fitting so that your model generalizes well after training. For images, a variety of augmentation can be applied to increase the number of examples.

Click here to enroll.

11. Image Classification with CNNs using Keras

In this 1-hour long project-based course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend, and you will learn to train CNNs to solve Image Classification problems. In this project, we will create and train a CNN model on a subset of the popular CIFAR-10 dataset.

This course runs on Coursera’s hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed.

Prerequisites:
In order to be successful in this project, you should be familiar with python and convolutional neural networks.

Click here to enroll.

12. Predicting House Prices with Regression using TensorFlow

In this 2-hour long project-based course, you will learn the basics of using Keras with TensorFlow as its backend and you will learn to use it to solve a basic regression problem. By the end of this project, you will have created, trained, and evaluated a neural network model that, after the training, will be able to predict house prices with a high degree of accuracy.

Click here to enroll.

13. Perform Sentiment Analysis with scikit-learn

In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. We will use the popular IMDB data set. Our goal is to use a simple logistic regression estimator from scikit-learn for document classification.

This course runs on Coursera’s hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed.

Click here to enroll.

14. Computer Vision – Object Detection with OpenCV and Python

In this 1-hour long project-based course, you will learn how to do Computer Vision Object Detection from Images and Videos. At the end of the project, you’ll have learned how to detect faces, eyes and a combination of them both from images, how to detect people walking and cars moving from videos and finally how to detect a car’s plate.

This course runs on Coursera’s hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed.

Click here to enroll.

15. Linear Regression with Python

In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it’s still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process.

Since this is a practical, project-based course, you will need to have a theoretical understanding of linear regression, and gradient descent. We will focus on the practical aspect of implementing linear regression with gradient descent, but not on the theoretical aspect.

Click here to enroll

16. Support Vector Machines with scikit-learn

In this project, you will learn the functioning and intuition behind a powerful class of supervised linear models known as support vector machines (SVMs). By the end of this project, you will be able to apply SVMs using scikit-learn and Python to your own classification tasks, including building a simple facial recognition model. This course runs on Coursera’s hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed.

Click here to enroll.

17. Logistic Regression with NumPy and Python

Welcome to this project-based course on Logistic with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent, cost function, and logistic regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals.

By the time you complete this project, you will be able to build a logistic regression model using Python and NumPy, conduct basic exploratory data analysis, and implement gradient descent from scratch. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory.

Click here to enroll.

18. Build a Machine Learning Web App with Streamlit and Python

Welcome to this hands-on project on building your first machine learning web app with the Streamlit library in Python. By the end of this project, you are going to be comfortable with using Python and Streamlit to build beautiful and interactive ML web apps with zero web development experience! We are going to load, explore, visualize and interact with data, and generate dashboards in less than 100 lines of Python code! Our web application will allows users to choose what classification algorithm they want to use and let them interactively set hyper-parameter values, all without them knowing to code!

Prior experience with writing simple Python scripts and using pandas for data manipulation is recommended. It is required that you have an understanding of Logistic Regression, Support Vector Machines, and Random Forest Classifiers and how to use them in scikit-learn.

Click here to enroll.

19. Analyze Box Office Data with Plotly and Python

Welcome to this project-based course on Analyzing Box Office Data with Plotly and Python. In this course, you will be working with the The Movie Database (TMDB) Box Office.

Prediction data set. The motion picture industry is raking in more revenue than ever with its expansive growth the world over. Can we build models to accurately predict movie revenue? Could the results from these models be used to further increase revenue? We try to answer these questions by way of exploratory data analysis (EDA) and feature engineering. We will primarily use Plotly for data visualization. Plotly Python which is Plotly’s Python graphing library makes interactive, publication-quality graphs ready for both online and offline use.

Click here to enroll.

20. Image Compression with K-Means Clustering

In this project, you will apply the k-means clustering unsupervised learning algorithm using scikit-learn and Python to build an image compression application with interactive controls. By the end of this 45-minute long project, you will be competent in pre-processing high-resolution image data for k-means clustering, conducting basic exploratory data analysis (EDA) and data visualization, applying a computationally time-efficient implementation of the k-means algorithm, Mini-Batch K-Means, to compress images, and leverage the Jupyter widgets library to build interactive GUI components to select images from a drop-down list and pick values of k using a slider.

Click here to enroll

21. Predict Future Product Prices Using Facebook Prophet

In this 1-hour long project-based course, you will be able to:

  • Understand the theory and intuition behind Facebook times series forecasting tool
  • Import Key libraries, dataset and visualize dataset
  • Build a time series forecasting model using Facebook Prophet to predict future product prices
  • Compile and fit time series forecasting model to training data
  • Assess trained model performance

Click here to enroll.

22. Image Super Resolution Using Autoencoders in Keras

Welcome to this 1.5 hours long hands-on project on Image Super Resolution using Autoencoders in Keras. In this project, you’re going to learn what an autoencoder is, use Keras with Tensorflow as its backend to train your own autoencoder, and use this deep learning powered autoencoder to significantly enhance the quality of images. That is, our neural network will create high-resolution images from low-res source images.

Click here to enroll.

23. Traffic Sign Classification Using Deep Learning in Python/Keras

In this 1-hour long project-based course, you will be able to:

  • Understand the theory and intuition behind Convolutional Neural Networks (CNNs).
  • Import Key libraries, dataset and visualize images.
  • Perform image normalization and convert from color-scaled to gray-scaled images.
  • Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend.
  • Compile and fit Deep Learning model to training data.
  • Assess the performance of trained CNN and ensure its generalization using various KPIs.
  • Improve network performance using regularization techniques such as dropout.

Click here to enroll.

24. Image Denoising Using AutoEncoders in Keras and Python

In this 1-hour long project-based course, you will be able to:

  • Understand the theory and intuition behind Autoencoders
  • Import Key libraries, dataset and visualize images
  • Perform image normalization, pre-processing, and add random noise to images
  • Build an Autoencoder using Keras with Tensorflow 2.0 as a backend
  • Compile and fit Autoencoder model to training data
  • Assess the performance of trained Autoencoder using various KPIs

Click here to enroll.

Disclaimer : We try to ensure that the information we post on Noticebard.com is accurate. However, despite our best efforts, some of the content may contain errors. You can trust us, but please conduct your own checks too.

Share on facebook
Facebook
Share on twitter
Twitter
Share on whatsapp
WhatsApp
Share on linkedin
LinkedIn
Share on email
Email

Leave a Comment

x