Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur (Author)

Publisher: COMPUTER SCIENCE

Availability: In Stock
SKU: 49972
Number of Pages: 301

Rs.790.00

Rs.995.00

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Description

📘 Title Name: Approaching (Almost) Any Machine Learning Problem
✍️ Author: Abhishek Thakur
📦 Quality: White Paper – Pakistan Print

🔹 Introduction:

Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur is a practical, hands-on guide crafted for data science learners, ML enthusiasts, and professionals. Written by a Kaggle Grandmaster, this book focuses on the real-world workflow of building machine learning solutions — from problem understanding to deployment. With clear explanations and implementation-focused guidance, it transforms complex ML concepts into actionable learning for students and professionals alike.

🔑 Key Points:

Provides a complete end-to-end ML workflow including data cleaning, feature engineering, model training, and evaluation.
Focuses on practical applications rather than just theory — ideal for Kaggle and real-world projects.
Covers supervised learning, validation strategies, cross-validation, and model selection techniques.
Demonstrates Python-based machine learning approaches with real datasets and coding examples.
Offers insights on handling imbalanced data, ensembling methods, and improving model accuracy.

🧠 Conclusion:

Abhishek Thakur’s Approaching (Almost) Any Machine Learning Problem serves as a roadmap for mastering ML through practice. Whether you're preparing for data science jobs, competitions, or applied machine learning projects, this book guides you step-by-step to think like a real ML engineer — making it an essential learning companion for modern AI and data professionals.

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