Price: ₹ 258.00
(as of Sep 20,2021 19:33:59 UTC – Details)
Despite being one of the hottest disciplines in the Tech industry right now, Artificial Intelligence and Machine Learning remain a little elusive to most.The erratic availability of resources online makes it extremely challenging for us to delve deeper into these fields. Especially when gearing up for job interviews, most of us are at a loss due to the unavailability of a complete and uncondensed source of learning.
Cracking the Machine Learning Interview
- Equips you with 225 of the best Machine Learning problems along with their solutions.
- Requires only a basic knowledge of fundamental mathematical and statistical concepts.
- Assists in learning the intricacies underlying Machine Learning concepts and algorithms suited to specific problems.
- Uniquely provides a manifold understanding of both statistical foundations and applied programming models for solving problems.
- Discusses key points and concrete tips for approaching real life system design problems and imparts the ability to apply them to your day to day work.
This book covers all the major topics within Machine Learning which are frequently asked in the Interviews. These include:
- Supervised and Unsupervised Learning
- Classification and Regression
- Decision Trees
- K-Nearest Neighbors
- Logistic Regression
- Support Vector Machines
- Neural Networks
- Dimensionality Reduction
- Feature Extraction
- Feature Engineering
- Model Evaluation
- Natural Language Processing
- Real life system design problems
- Mathematics and Statistics behind the Machine Learning Algorithms
- Various distributions and statistical tests
This book can be used by students and professionals alike. It has been drafted in a way to benefit both, novices as well as individuals with substantial experience in Machine Learning.
Following Cracking The Machine Learning Interview diligently would equip you to face any Machine Learning Interview.
We have also provided Python code snippets for some of the questions using Scikit-Learn.
You can find them on github as well: