ChatGPT for Data Science and Building ML Models
ChatGPT for Data Science and Building ML Models, Learn to build your Machine Learning Model using ChatGPT in 1 hour and bring business value for your organization.
By the end of this course, participants will have the knowledge and skills to leverage ChatGPT as a valuable tool throughout the data science and machine learning pipeline, from data exploration and model development to evaluation and reporting. This comprehensive training will empower them to excel in real-world data science projects and contribute meaningfully to their organizations’ success.
Below are the objectives or outcomes that learners can expect to achieve after completing your course on ChatGPT for Data Science and ML Model Building:
1. Proficient Interaction with ChatGPT for Data Science:
- Learners will develop the skills and knowledge needed to effectively interact with ChatGPT, utilizing its natural language processing capabilities to streamline data science tasks.
- They will gain the ability to ask relevant questions, request data summaries, and seek insights from ChatGPT to enhance their data exploration and analysis process.
2. Data Exploration and Visualization with ChatGPT:
- Participants will learn how to harness ChatGPT’s capabilities to assist in data exploration.
- They will acquire the skills to employ ChatGPT for generating descriptive statistics, data visualizations, and exploratory analysis, enabling them to uncover valuable insights from their datasets.
3. Building Regression Models with XGBoost:
- Learners will become proficient in utilizing ChatGPT for guidance on building regression models, with a specific focus on the powerful XGBoost algorithm.
- They will learn the best practices for feature selection, hyperparameter tuning, and model training with ChatGPT’s assistance, resulting in robust and accurate regression models.
4. Advanced Model Evaluation Techniques:
- Participants will gain expertise in evaluating machine learning models using advanced techniques such as cross-validation, RMSE (Root Mean Square Error), and R-squared.
- They will be able to leverage ChatGPT’s insights to interpret model performance metrics, identify areas for improvement, and make data-driven decisions for model selection and refinement.
5. Creating Comprehensive End-to-End Reports:
- Learners will acquire the ability to interact with ChatGPT to compile and generate comprehensive reports summarizing their end-to-end data science and machine learning work.
- They will learn to effectively communicate their findings, model results, and recommendations in a professional and concise manner, enhancing their ability to convey the value of their work to stakeholders.
