Udemy – Data Science Marathon: 120 Projects To Build Your Portfolio 2024-11

Udemy – Data Science Marathon: 120 Projects To Build Your Portfolio 2024-11 Downloadly IRSpace

Apr 18, 2025 - 17:50 Updated: Aug 17, 2025 - 18:03

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Udemy – Data Science Marathon: 120 Projects To Build Your Portfolio 2024-11

Data Science Marathon: 120 Projects To Build Your Portfolio. In this course, you will solve business problems using data science in a hands-on way. You will learn how to build and deploy projects in machine learning, data mining, artificial intelligence, machine learning automation, deep learning, natural language processing (NLP), and web applications (using Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud). Data science is a field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data scientists apply machine learning algorithms to numbers, text, images, video, audio, and more to create artificial intelligence (AI) systems to perform tasks that would normally require human intelligence. In return, these systems produce insights that analysts and business users can translate into tangible business value. More companies are realizing the importance of data science, artificial intelligence, and machine learning. Regardless of industry or size, organizations that want to remain competitive in the era of big data must effectively develop and implement data science capabilities or else they will be left behind.

What you will learn in this course:

Case studies and real projects to understand how things are done in the real world
Implementing machine learning algorithms, presenting data mining projects to management
Using Scikit-Learn for machine learning tasks
Exploring how to deploy machine learning models
Having a great intuition of many machine learning models
Learning which machine learning model to choose for each type of problem
Learn best practices about data mining workflows
Learn data preprocessing, data cleaning, and big data analysis.
Learn to use NumPy for numeric data
Using Matplotlib to create fully custom data visualizations with Python
Explore large datasets and manipulate data using Pandas
Learn to use Seaborn for statistical charts