by Valeriy Babushkin | Released June 2018 | ISBN: 9781789135817

Transform your simple machine learning model into a cutting edge powerful versionAbout This VideoLearn from a Kaggle competition master and a Team Lead at the largest search engine company in Russia—a great mixture of competition experience and Industrial knowledgeLearn the techniques currently used among Kaggle top-tier competitors and best practices in real-life projects to upgrade your skillsWe guide you through supervised learning from basic linear to ensemble models, by extending the capabilities of your ML system to build high-performance modelsIn DetailMachine learning allows us to interpret data structures and fit that data into models to identify patterns and make predictions. Python makes this easier with its huge set of libraries that can be easily used for machine learning. In this course, you will learn from a top Kaggle master to upgrade your Python skills with the latest advancements in Python.It is essential to keep upgrading your machine learning skills as there are immense advancements taking place every day. In this course, you will get hands-on experience of solving real problems by implementing cutting-edge techniques to significantly boost your Python Machine Learning skills and, as a consequence, achieve optimized results in almost any project you are working on.Each technique we cover is itself enough to improve your results. However; combining them together is where the real magic is. Throughout the course, you will work on real datasets to increase your expertise and keep adding new tools to your machine learning toolbox.By the end of this course, you will know various tips, tricks, and techniques to upgrade your machine learning algorithms to reduce common problems, all the while building efficient machine learning models.All the code and supporting files for this course are available on GitHub at:https://github.com/PacktPublishing/Python-Machine-Learning-Tips-Tricks-and-Techniques Show and hide more

1. Chapter 1 : Improving Your Models Using Feature Engineering
   • The Course Overview 00:02:20
   • Using Feature Scaling to Standardize Data 00:12:58
   • Implementing Feature Engineering with Logistic Regression 00:03:17
   • Extracting Data with Feature Selection and Interaction 00:07:30
   • Combining All Together 00:03:31
   • Build Model Based on Real-World Problems 00:03:59
2. Chapter 2 : Feature Improvement with Non Linear Classification Techniques
   • Support Vector Machines 00:07:27
   • Implementing kNN on the Data Set 00:09:02
   • Decision Tree as Predictive Model 00:08:12
   • Tricks with Dimensionality Reduction 00:05:34
   • Combining All Together 00:05:37
3. Chapter 3 : Power of Ensemble Learning with Python
   • Random Forest for Classification 00:06:07
   • Gradient Boosting Trees and Bayes Optimization 00:09:38
   • CatBoost to Handle Categorical Data 00:05:08
   • Implement Blending 00:08:13
   • Implement Stacking 00:07:35
4. Chapter 4 : Recommender Systems
   • Memory-Based Collaborative Filtering 00:06:35
   • Item-to-Item Recommendation with kNN 00:06:44
   • Applying Matrix Factorization on Datasets 00:08:25
   • Wordbatch for Real-World Problem 00:05:14
5. Chapter 5 : Boost Your Overall Model Robustness
   • Validation Dataset Tuning 00:06:03
   • Regularizing Model to Avoid Overfitting 00:04:29
   • Adversarial Validation 00:05:47
   • Perform Metric Selection on Real Data 00:17:28
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