Table of Contents
Introduction
Lecture 16 of the Innoquest Cohort-1 Professional AI/ML Training by Innovista marked an exciting milestone in our journey as AI/ML practitioners. This session was focused on translating theoretical knowledge into practical implementation, enabling us to master real-world applications of advanced machine learning techniques. The hands-on approach provided an invaluable learning experience that further solidified my understanding of these concepts.
Key Concepts Implemented
Dimensionality Reduction with PCA
Principal Component Analysis (PCA) is a powerful tool for reducing the dimensionality of datasets while retaining most of the variance. In this session:
- We worked with a dataset comprising 64 independent features.
- Multiple approaches to PCA were explored, achieving around 97% accuracy by:
- Training a binary classifier on all 64 features.
- Using only 40 principal components.
- The results showcased PCA’s capability to balance computational efficiency and model performance effectively.
K-Nearest Neighbors (KNN) Clustering
The class delved into KNN clustering and methods for determining the optimal number of clusters:
- Techniques such as silhouette score and elbow method were employed to evaluate cluster quality.
- These tools are instrumental in improving clustering outcomes by finding the perfect balance between overfitting and underfitting.
You may wonder: The quality of this algorithm’s results may decrease over time as consumer behaviors evolve, but don’t forget the iterative training that we implement after putting our model into production.
Hierarchical Clustering
We implemented both agglomerative and divisive hierarchical clustering techniques, along with the ward linkage method, which minimizes the variance within clusters:
- The ward linkage method was particularly effective in forming well-separated and meaningful clusters.
- This technique’s visual representation in dendrograms added another layer of interpretability to the clustering process.
Anomaly Detection
Anomaly detection is crucial for identifying outliers in datasets. In this session:
- We practiced anomaly detection using boxplots and the Interquartile Range (IQR) method.
- Strategies for handling detected anomalies were also discussed, ensuring robust model performance.
Recommendation Systems
Building on prior knowledge, we implemented both collaborative filtering and content-based filtering methods:
- Using the IMDB dataset, we practiced building systems capable of personalized recommendations.
- This exercise was particularly engaging as it tied back to my previous work on an end-to-end movie recommendation system hosted on Streamlit, which I developed under the guidance of Nitish Singh (CampusX).
Lessons Learned
This session reinforced the importance of practical implementation in mastering AI/ML techniques. While coding might seem less challenging now, I’ve realized that writing code while learning something new is a powerful way to deepen understanding. My iterative approach—exploring every unfamiliar concept or error until it’s fully understood—has been instrumental in my learning journey.
Future Plans
Real-world, purposeful projects remain my primary inspiration. Although the fast pace of this training has slowed the implementation of some planned projects, I am committed to completing and sharing these soon. These projects aim to address real-world challenges using AI/ML and reflect my dedication to creating impactful solutions.
Closing Thoughts
Lecture 16 was another step forward in my journey toward becoming a proficient AI/ML practitioner. The balance of theoretical knowledge and practical implementation continues to drive my growth in this field. If you’re someone who shares a passion for innovation and problem-solving, let’s explore how we can collaborate to achieve meaningful results together.