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[FreeCoursesOnline.Me] [Packt] Python Machine Learning Tips, Tricks, And Techniques [FCO]
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Transform your simple machine learning model into a cutting edge powerful version
Video Details
ISBN 9781789135817
Course Length 2 hour 46 minutes
Table of Contents
• Improving Your Models Using Feature Engineering
• Feature Improvement with Non Linear Classification Techniques
• Power of Ensemble Learning with Python
• Recommender Systems
• Boost Your Overall Model Robustness
Learn
• Tips and tricks to speed up your modeling process and obtain better results
• Make predictions using advanced regression analysis with Python
• Modern techniques for solving supervised learning problems
• Various ways to use ensemble learning with Python to derive optimum results
• Build your own recommendation engine and perform collaborative filtering
• Give your production machine learning system improved reliability
About
Machine 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
Style and Approach
We practice real datasets from different fields, progressively increasing our expertise and putting new tools at our disposal. With a combination of these tools, almost any machine learning problem can be solved much faster and with far better overall results.
Features:
• Learn 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 knowledge
• Learn the techniques currently used among Kaggle top-tier competitors and best practices in real-life projects to upgrade your skills
• We guide you through supervised learning from basic linear to ensemble models, by extending the capabilities of your ML system to build high-performance models
Author
Valeriy Babushkin
Valeriy Babushkin has done an M. Sc. and has 5+ years' experience in industrial data science and academia. He is a Kaggle competition master and a 2018 IEEE SP Cup finalist. He has been a Data Science Team Lead at Yandex (the largest search engine in Russia; it outperforms Google) and runs an online taxi service (he acquired Uber in Russia and 15 other countries) and the biggest e-commerce platform in Russia.
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FILE LIST
Filename
Size
0. Websites you may like/How you can help Team-FTU.txt
229 B
01 - The Course Overview.mp4
27 MB
02 - Using Feature Scaling to Standardize Data.mp4
37.5 MB
03 - Implementing Feature Engineering with Logistic Regression.mp4
11.6 MB
04 - Extracting Data with Feature Selection and Interaction.mp4
21.9 MB
05 - Combining All Together.mp4
13.1 MB
06 - Build Model Based on Real-World Problems.mp4
14.6 MB
07 - Support Vector Machines.mp4
23.3 MB
08 - Implementing kNN on the Data Set.mp4
34.2 MB
09 - Decision Tree as Predictive Model.mp4
27.1 MB
10 - Tricks with Dimensionality Reduction.mp4
20.5 MB
11 - Combining All Together.mp4
21.3 MB
12 - Random Forest for Classification.mp4
23.9 MB
13 - Gradient Boosting Trees and Bayes Optimization.en.ttml
17.8 KB
13 - Gradient Boosting Trees and Bayes Optimization.mp4
33.4 MB
14 - CatBoost to Handle Categorical Data.en.ttml
10.5 KB
14 - CatBoost to Handle Categorical Data.mp4
20.1 MB
15 - Implement Blending.en.ttml
14.9 KB
15 - Implement Blending.mp4
27.4 MB
16 - Implement Stacking.en.ttml
13.6 KB
16 - Implement Stacking.mp4
31.2 MB
17 - Memory-Based Collaborative Filtering.en.ttml
12.3 KB
17 - Memory-Based Collaborative Filtering.mp4
21.3 MB
18 - Item-to-Item Recommendation with kNN.en.ttml
12.5 KB
18 - Item-to-Item Recommendation with kNN.mp4
21.2 MB
19 - Applying Matrix Factorization on Datasets.en.ttml
15.2 KB
19 - Applying Matrix Factorization on Datasets.mp4
27.5 MB
20 - Wordbatch for Real-World Problem.en.ttml
9.7 KB
20 - Wordbatch for Real-World Problem.mp4
23.2 MB
21 - Validation Dataset Tuning.en.ttml
10.8 KB
21 - Validation Dataset Tuning.mp4
22.1 MB
22 - Regularizing Model to Avoid Overfitting.en.ttml
8.6 KB
22 - Regularizing Model to Avoid Overfitting.mp4
14.6 MB
23 - Adversarial Validation.en.ttml
9.7 KB
23 - Adversarial Validation.mp4
20 MB
24 - Perform Metric Selection on Real Data.en.ttml
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