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As an Applied AI Engineer with experience in both academic and professional AI/ML projects, I recently attended the 11th session of the InnoQuest Cohort-1 Professional AI/ML Training Program. In this post, I’ll share my key takeaways and reflections from the class, offering an honest perspective while acknowledging the value it brought.
Key Focus of the Session
The session revolved around the practical implementation of core machine learning models, including:
- Support Vector Machines (SVM)
- Decision Trees
- Random Forests
- XGBoost
This was a continuation of the theoretical discussions from previous classes, and the emphasis was on translating concepts into code. The instructor meticulously covered nearly all relevant parameters for these algorithms, catering especially to newcomers by explaining details like x_train, x_test, and the workings of features at a code level.
Highlights of My Learning Experience
While the session had its strengths and areas for improvement, it was an insightful experience that provided both new knowledge and a chance to revisit foundational concepts.
What I Found Valuable
- DMatrix in XGBoost: The session introduced me to this data structure, but then after reading more about it, I got more clear understanding of its role in improving computational efficiency and usage in XGBoost.
- Cross-Validation: The lecture provided a helpful refresher on cross-validation, reinforcing best practices for evaluating model performance.
- Attention to Minor Details: The instructor’s focus on explaining basic steps and processes, like splitting datasets and understanding parameter roles, was particularly useful for beginners. These foundational aspects often get overlooked in advanced settings.
Constructive Observations
- Instructional Style: The instructor, while knowledgeable, did not excel in demonstrating practical, real-world approaches that align with industry standards. For instance, referring to feature encoding as “feature engineering” felt imprecise and could potentially confuse learners.
- Simplicity of Datasets: The datasets used in the examples were overly simplistic—one dataset consisted of only 14 rows. While this simplicity can aid learning for newcomers, it doesn’t reflect the complexity of real-world data scenarios.
- Limited Practical Expertise: The focus on “how to” implement models was evident, but a deeper dive into advanced, professional techniques would have elevated the session. This may have been influenced by the instructor’s experience level.
Broader Reflections
For students new to machine learning, this session offered a strong foundation, with clear explanations and practical demonstrations. However, for experienced professionals like me, the session served more as a review of fundamental concepts rather than a source of advanced insights.
It’s worth noting that the effort to highlight every detail can sometimes slow the pace for those already familiar with the material. Nonetheless, the instructor’s approach was beneficial for bridging the gap between theoretical knowledge and hands-on implementation.
Conclusion
Class 11 of the InnoQuest Cohort-1 Professional AI/ML Training Program was a valuable stepping stone in the learning journey. While it leaned towards catering to beginners, it provided me with useful refreshers and a few new perspectives. As I continue documenting my experience with this program, I look forward to deeper dives into more complex topics and applications in future sessions.
For fellow learners and professionals, the session underscored an important reminder: every learning opportunity, regardless of its depth, can contribute to your growth if approached with an open mind.
Stay tuned for my next post as I continue to share insights from this program and beyond. If you have any thoughts or similar experiences, feel free to connect and discuss!