Course Content
Introduction
Introduction
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Course Promo
15:36
Machine Learning Terminology Course Agenda
06:55
What Will I Learn?
05:05
When To Use Machine Learning?
03:34
Your Feedback
02:43
Machine Learning Problem Framing
In this section you will learn how to frame a machine learning problem in detail
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The Business and ML Problem
09:44
Types of Machine Learning
09:58
Machine Learning Algorithms
15:36
Working With DataSets
In this section you will explore and learn about data sets. For example, you will learn how to prepare raw data to be used with machine learning
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Different Types of Data
04:44
Data Collection & Integration
06:54
Data Cleaning
05:50
Data Splitting and Shuffling
07:35
Data Visualization & Feature Engineering
In this section you will learn about how to visualize your data and use feature engineering with given data sets
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What Are Features In Machine Learning?
04:03
Numeric Value Binning
07:01
Model Training & Evaluation
In this section you will learn about training your machine learning model and finally evaluating your machine learning model
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Parameter Tuning
05:18
Model Overfitting Vs Underfitting
07:25
Business Goal Evaluation
In this section you will learn how to evaluate your business or organizational goals and objectives before prediction
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Goal Evaluation
04:22
Augmenting Data
05:38
Predictions
05:05
Machine Learning In Action Demo
Machine Learning In Action
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ML Demo – Creating A Data source and Model
18:29
Preparing Data
15:52
Conclusion & Bonus
In this section you will find bonus lectures and valuable resources
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FREE ClayDesk WebHosting
04:52
Machine Learning Glossary
Machine Learning Terminology & Process For Beginners
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