Hands-on Machine Learning for Data Mining

Hands-on Machine Learning for Data Mining

Get efficient in performing data mining and machine learning

Bestseller
Created By: Jesus Salcedo
15.87 9.52

About This Course

30% 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-

Other Information

  • Certificate will provided in this course on Completion
  • Full lifetime access
  • Available on Mobile & Laptop

What Students Will Learn In Your Course?

  • Explore the lethal combination of Data Mining and Machine Learning to hone your model-building skills and improve your models
  • Understand the inner workings of your models
  • Get to grips with three ways in which you can decipher how machine learning models can be interpreted
  • Derive worthwhile insights from your data by developing efficient predictive models to predict future results accurately
  • Maximize your productivity by analyzing your models and interpreting their accuracy in a well organized manner
  • Combine the results of two or more models

Are There Any Course Requirements Or Prerequisites?

No prior knowledge in machine learning is assumed.

Who Are Your Target Students?

If you are a data mining professional who wishes to get a ticket to a 30% higher salary by adding machine learning to your skill set, then this is the ideal course for you.

Course Content

  • 21 lectures
  • 02:44:45
  • The Course Overview
    00:02:41
  • Characteristics and Examples of Machine Learning Models
    00:02:15
  • Working with Neural Networks: Theory
    00:08:43
  • Working with Neural Networks: Demonstration
    00:21:28
  • Working with Support Vector Machines: Theory
    00:04:04
  • Working with Support Vector Machines: Demonstration
    00:10:11
  • General Model Interpretation
    00:04:36
  • Using Graphs to Interpret Machine Learning Models
    00:10:03
  • Using Statistics to Interpret Machine Learning Models
    00:07:18
  • Using Decision Trees to Interpret Machine Learning Models
    00:05:49
  • 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
  • Combine Models
    00:13:36
  • Propensity Scores
    00:06:25
  • Meta-Level Modeling
    00:04:17
  • Error Modeling
    00:07:07
  • Boosting and Bagging
    00:11:12
  • Continuous Outcomes
    00:14:26
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Packt Publication

  • 4.48 (12)
  • 13 Reviews
  • 12 Students
  • 935 Courses