Download Udemy – Data Science Bundle: 180 Hands-On Projects – Course 1 of 3 2023-10

Description

Data Science Bundle Course: 180 Hands-On Projects – Course 1 of 3. This course is a comprehensive and dynamic introduction to the world of data science with a focus on practical application. The first part of a three-part program, this course will provide you with the skills you need to master data science and become a skilled practitioner. In this course, you’ll learn how to build and deploy models for machine learning, artificial intelligence, natural language processing, and more, using the Python programming language and powerful web frameworks like Flask and Django. You’ll also gain the skills to scale your projects to the cloud using platforms such as Heroku, AWS, Azure, GCP, IBM Watson, and Streamlit. Emphasizing experiential learning, this course will engage you with 180 real-world projects that will help you hone your skills in a variety of data science areas, including:

Forecast: forecast renewable energy production, diamond price, ethereum price, stress level and more
Classification: Classification of brain tumors, age and gender detection from X-ray images, detection of COVID-19, detection of DeepFakes and more
Analytics: Sensor data analysis, demand forecasting, text analytics, and more

In addition to practical training, this course will also teach you the concepts of data science theory in depth. You will learn how to implement machine learning algorithms from scratch, choose the right models for different tasks, and use powerful libraries like SciKit-Learn and Matplotlib for data analysis and visualization.

What you will learn in Data Science Bundle: 180 Hands-On Projects – Course 1 of 3

Fundamentals of Machine Learning with Python: Master the leading language of data science and learn how to build robust machine learning models
Deploying machine learning models: learning how to deploy machine learning models in real and practical environments
Choosing the Right Model: Developing strong intuition for choosing the right machine learning models for different tasks
Implement Algorithms: Implement popular machine learning algorithms from scratch to deeply understand their performance
Powerful libraries: use SciKit-Learn and Matplotlib to efficiently perform various machine learning and data visualization tasks
Deep learning and neural networks: Getting to know the concepts of deep learning, transfer learning and neural networks