Book Image

Machine Learning Solutions

Book Image

Machine Learning Solutions

Overview of this book

Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.
Table of Contents (19 chapters)
Machine Learning Solutions
Foreword
Contributors
Preface
Index

Strategy for winning hackathons


If you want to win hackathons or any Kaggle competition, then these are the things to keep in mind: 

  • Try to start as soon as you encounter the competition.

  • Do exploratory data analysis properly. This will really help you build a good solution.

  • Try to achieve a base-line model in the first five iterations.

  • Track your own progress as well as track progress of others by checking out public and private leaderboards.

  • Try different approaches and be innovative.

  • Ask appropriate questions in forums but don't waste too much of your time.

  • If you feel that in order to win you need to form a team, then ask other members to join you.

  • When you create your team does matter. You need to form a team at least 15 days before the deadline of the competition so your team can work together and come up with the best possible approaches.

  • The selection of team members also plays an important role. You should team up with those who have a complementary skill set so you can take advantage of each other's knowledge. 

  • Understand your team member's solutions and discuss approaches in depth. 

  • Your team members should know each other's strengths and weaknesses so it is easy to collaborate. 

  • Your team should discuss opportunities--meaning, you need to discuss other possible approaches in order to try them if possible. 

  • If you lose the competition, don't get disheartened. Try harder next time and learn what went wrong this time. List those things that went wrong and next time you won't make those kinds of mistakes. Try to improve those things. You will definitely win.