My reflections on Class 11 of the InnoQuest AI/ML Training Program, where I revisited foundational concepts like cross-validation and XGBoost's DMatrix while highlighting the session's strengths and areas for growth.
The InnoQuest Cohort-1, Assignment 2 focused on cleaning and preprocessing the Lending-company.csv dataset for analysis. Key Takeaway: Cleaned datasets unlock smarter, data-driven decisions!
The Innoquest Cohort-1 Class 9 covered Colab and Kaggle, delved into model training, and reflected on beginner-level teaching practices versus industry standards.
Very interesting lecture led by the inspiring Dr. Ahmed Raza Shahid, delved deep into the fundamentals of Machine Learning, blending theory with practical insights.
Exploring results Interpretation. Transforming analysis into actionable insights that drive innovation, optimize processes, and support impactful decision-making.
Built an ML model for absenteeism prediction, focusing on data encoding, feature analysis, and practical insights. Also mentioned some suggestions for improvement and a real-world case study.
Discover how my first assignment in the InnoQuest Cohort 1 Data Science module revisited the basics of pandas, reinforcing essential skills in data manipulation and analysis.
In this blog post, I share key takeaways from Class 5 of the InnoQuest Cohort-1 AI/ML program, focusing on real-world data preprocessing and feature engineering using the California Housing and Absenteeism datasets.
Discover insights from InnoQuest Cohort-1 Class 4. Learn about EDA, statistical measures, Pareto distribution, correlation vs. covariance, and effective data visualization techniques.
Day 3 of InnoQuest Bootcamp Cohort-1 focused on data cleaning and visualization with Pandas! 🐼 From managing NaN values to crafting impactful visuals, it was a session full of practical insights.
