Offered By: IBM
Winning Techniques for Your Next Kaggle Data Science Contest
The field of data science is rapidly growing, and the need for individuals in traditional industries, such as Agriculture, Transportation, Construction, Retail, Hospitality and Tourism, to acquire these skills is also increasing. If you are an expert in these industries and would like to have a competitive edge in your career and the ability to drive innovation in your industry, then you found the right place!
Continue readingGuided Project
Data Science
111 EnrolledAt a Glance
The field of data science is rapidly growing, and the need for individuals in traditional industries, such as Agriculture, Transportation, Construction, Retail, Hospitality and Tourism, to acquire these skills is also increasing. If you are an expert in these industries and would like to have a competitive edge in your career and the ability to drive innovation in your industry, then you found the right place!
A Look at the Project Ahead
This guided project provides a unique opportunity for you to learn about data science and machine learning in a practical way. We will guide you through the full machine-learning project cycle, which typically includes the following steps:
- Problem formulation: defining the problem and understanding the business context.
- Data collection: gathering and acquiring data from various sources.
- Data preprocessing: cleaning, transforming, and preparing data for analysis.
- Exploratory data analysis: understanding the structure, contents, and patterns in the data through visualizations and summary statistics.
- Feature engineering: creating new features from the existing data.
- Model selection: choosing an appropriate model or algorithm for the problem.
- Model training: fitting the model to the data and tuning its hyperparameters.
- Model evaluation: assessing the performance of the model using appropriate metrics and techniques.
This comprehensive guide is hard to find on the internet and will equip you with a solid foundation in the data science process. After completing the project, you will be able to use real-world data to make predictions and apply machine learning algorithms to analyze and draw insights from data.
Learning Objectives
- Learn the full machine learning project cycle, from data exploration to model evaluation.
- Gain hands-on experience with data cleaning and preparation techniques.
- Understand the importance of feature engineering and how to apply it to real-world datasets.
- Learn how to select and train machine learning models for different problems.
- Learn how to make predictions and draw insights from data using machine learning algorithms.
- Understand the working mechanisms of three famous boosting algorithms: CatBoost, LightGBM, and XGBoost.
- Discover how to use ensemble learning techniques to combine model predictions and achieve even better results.