Data Science Projects: Data Analysis, ML, and Deployment

Data Science Projects: Data Analysis, ML, and Deployment, available at $19.99, has an average rating of 4.48, with 8 lectures, based on 294 reviews, and has 15047 subscribers.

You will learn about Data Analysis and Visualization: Learn to analyze data and create visualizations with Pandas and Matplotlib Machine Learning Fundamentals: Build, train, and evaluate ML models, mastering algorithms and their real-world applications to solve data problems. Feature Engineering: Learn feature engineering to create impactful features, improving model performance and prediction accuracy effectively. Streamlit for Interactive Apps: Develop interactive web apps with Streamlit to deploy and visualize ML models, offering a user-friendly experience. FastAPI for Model Deployment: Create scalable APIs with FastAPI to deploy ML models, enabling real-time predictions and seamless integration. End-to-End Project Workflow: Experience the full data science lifecycle from data cleaning and exploration to model deployment with hands-on projects. This course is ideal for individuals who are Aspiring Data Scientists or Intermediate Data Analysts or Machine Learning Enthusiasts or Python Developers or Students in Data Science It is particularly useful for Aspiring Data Scientists or Intermediate Data Analysts or Machine Learning Enthusiasts or Python Developers or Students in Data Science.

Enroll now: Data Science Projects: Data Analysis, ML, and Deployment

Summary

Title: Data Science Projects: Data Analysis, ML, and Deployment

Price: $19.99

Average Rating: 4.48

Number of Lectures: 8

Number of Published Lectures: 8

Number of Curriculum Items: 8

Number of Published Curriculum Objects: 8

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

• Data Analysis and Visualization: Learn to analyze data and create visualizations with Pandas and Matplotlib
• Machine Learning Fundamentals: Build, train, and evaluate ML models, mastering algorithms and their real-world applications to solve data problems.
• Feature Engineering: Learn feature engineering to create impactful features, improving model performance and prediction accuracy effectively.
• Streamlit for Interactive Apps: Develop interactive web apps with Streamlit to deploy and visualize ML models, offering a user-friendly experience.
• FastAPI for Model Deployment: Create scalable APIs with FastAPI to deploy ML models, enabling real-time predictions and seamless integration.
• End-to-End Project Workflow: Experience the full data science lifecycle from data cleaning and exploration to model deployment with hands-on projects.

Who Should Attend

• Aspiring Data Scientists
• Intermediate Data Analysts
• Machine Learning Enthusiasts
• Python Developers
• Students in Data Science

Target Audiences

• Aspiring Data Scientists
• Intermediate Data Analysts
• Machine Learning Enthusiasts
• Python Developers
• Students in Data Science