by Jesus Salcedo | Released July 2018 | ISBN: 9781789342628

Get efficient in performing data mining and machine learningAbout This VideoThis comprehensive video tutorial will ensure that you build on your knowledge of data mining and learn how to apply machine learning techniques in the field of data science.You will learn when to use different data mining techniques, how to set up different analyses, and how to interpret the results.This video course follows a step-by-step approach to ensure that you improve model development.In Detail30% of data mining vacancies also involve machine learning. And those that do are 30% better paid than the rest. If you're involved in data mining you need to get on top of machine learning, before it gets on top of you.Hands-On Machine Learning for Data Mining gives you everything you need to bring the power of machine learning into your data mining work. This video course will enable you to pair the best algorithms with the right tools and processes. You will see how systems can learn from data, identify patterns and make predictions on data with minimal human intervention.All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Hands-on-Machine-Learning-for-Data-Mining-V- Show and hide more

1. Chapter 1 : Getting Started with Machine Learning
   • The Course Overview 00:02:41
   • Characteristics and Examples of Machine Learning Models 00:02:16
   • Working with Neural Networks: Theory 00:08:43
   • Working with Neural Networks: Demonstration 00:21:28
   • Working with Support Vector Machines: Theory 00:04:05
   • Working with Support Vector Machines: Demonstration 00:10:12
2. Chapter 2 : Understanding Models
   • General Model Interpretation 00:04:36
   • Using Graphs to Interpret Machine Learning Models 00:10:04
   • Using Statistics to Interpret Machine Learning Models 00:07:18
   • Using Decision Trees to Interpret Machine Learning Models 00:05:49
3. Chapter 3 : Improving Individual Models
   • Modifying Model Options 00:05:34
   • Using Different Models 00:03:23
   • Removing Noise 00:04:58
   • Doing Additional Data Preparation 00:05:31
   • Balancing Data (Over/Under Sampling) 00:11:08
4. Chapter 4 : Combining Models
   • Combine Models 00:13:37
   • Propensity Scores 00:06:26
   • Meta-Level Modeling 00:04:18
   • Error Modeling 00:07:07
   • Boosting and Bagging 00:11:12
   • Continuous Outcomes 00:14:27
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