Data science is one of the crucial sought-after fields in today’s job market. With the ever-increasing amount of information being generated on daily basis, businesses are in need of expert data scientists who can extract meaningful insights from the vast amount of knowledge available. Because of this, data science has grow to be a highly competitive field, and constructing a robust portfolio is important to face out from the gang.
In this text, we’ve got curated a listing of 10 end-to-end guided projects that can aid you hone your data science skills while creating a sturdy portfolio. These projects cover a spread of topics, including data cleansing, data visualization, machine learning, and more. So whether you’re a beginner or an experienced data scientist looking to boost your skills, these projects will offer you invaluable hands-on experience and aid you develop a well-rounded portfolio.
The primary project is constructing an This isa15 hours live implementation of an Automatic Speech Recognition System. It includes the whole project flow ranging from the business problem statement to the deployment part.
The second guided project is on Udemy. On this project-based course, you’re going to use AWS Sagemaker, AWS API Gateway, Lambda, React.js, Node.js, Express.js MongoDB, and DigitalOcean to create a secure, scalable, and robust production-ready enterprise-level image classifier.
You will probably be using best practices and establishing IAM policies to first create a secure environment in AWS. Then you definately will probably be using AWS’ built-in SageMaker Studio Notebooks, where you will probably be shown how you should use any custom dataset you would like.
You’ll perform Exploratory data evaluation on our dataset with Matplotlib, Seaborn, Pandas, and Numpy. After getting insightful information in regards to the dataset, you’ll arrange the Hyperparameter Tuning Job in AWS, where you’ll learn the right way to use GPU instances to hurry up training and the right way to use multi-GPU instance training.
You’ll then evaluate the training jobs and have a look at some metrics comparable to Precision, Recall, and F1 Rating. Upon evaluation, you’ll deploy the deep learning model on AWS with the assistance of AWS API Gateway and Lambda functions.
You’ll then test our API with Postman and see if we get inference results after that’s accomplished, and can secure our endpoints and arrange autoscaling to forestall latency issues. Finally, you’ll construct our web application which may have access to the AWS API. After that, you’ll deploy our web application to DigitalOcean.
The third guided end-to-end project is by Ken Jee. On this project, you’ll first collect data science job requirements and expected salary data using web scraping from Glassdoor. Then the info is cleaned and explored, and modeled. The model will then be put right into a production environment using Flask.
The fourth end-to-end project is . On this guided project, you may also undergo an end-to-end project to predict the actual estate price. As usual, it starts with an issue statement, data collection, data cleansing, feature engineering model constructing, and deploying the model using flask and likewise on AWS EC2.
The fifth project is constructing a . On this project, you’ll construct a mobile app using React Native to categorise potato disease using a deep learning model trained on the collected data and deployed on GCP.
The sixth guided project is the On this project, you’ll Construct an internet site to categorise sports celebrity images using a deep learning model trained on the collected data model deployed on the Flask server.
The seventh project is the . On this project, you’ll construct a real-time data evaluation application for E-commerce sales data using tools comparable to Kafka, Spark, Apache Cassandra, and superset.
The ninth project is constructing an This project goals to develop an AI-Based Hybrid Recommender System that mixes the strengths of multiple suggestion techniques to supply more accurate and diverse recommendations to users. Specifically, the system will incorporate each content-based and collaborative filtering approaches to supply personalized recommendations based on user behavior, preferences, and similarities with other users.
The AI-Based Hybrid Recommender System will utilize machine learning algorithms and natural language processing techniques to investigate user data, including user rankings, browsing history, and product features. The system will then generate recommendations based on this evaluation and supply users with a listing of services or products that they’re prone to be involved in.
The project will involve designing and developing the AI-Based Hybrid Recommender System, integrating it with existing systems, and testing its performance and accuracy. The project team will work collaboratively to discover the very best combination of suggestion techniques and algorithms to make sure the system offers probably the most accurate and diverse recommendations possible.
The last project is constructing an . This project goals to develop an AI-Based Hybrid Recommender System that mixes the strengths of multiple suggestion techniques to supply more accurate and diverse recommendations to users. Specifically, the system will incorporate each content-based and collaborative filtering approaches to supply personalized recommendations based on user behavior, preferences, and similarities with other users.
The AI-Based Hybrid Recommender System will utilize machine learning algorithms and natural language processing techniques to investigate user data, including user rankings, browsing history, and product features. The system will then generate recommendations based on this evaluation and supply users with a listing of services or products that they’re prone to be involved in.
The project will involve designing and developing the AI-Based Hybrid Recommender System, integrating it with existing systems, and testing its performance and accuracy. The project team will work collaboratively to discover the very best combination of suggestion techniques and algorithms to make sure the system offers probably the most accurate and diverse recommendations possible.
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