Build powerful Machine Learning models using Python with hands-on practical examples in just a week
About This Video
- A good understanding of Machine learning to start creating practical solutions.
- Get an intuitive understanding of many machine learning algorithms
- Build many different Machine Learning models and learn to combine them to solve problems
Machine learning is one of the most sought-after skills in the market. But have you ever wondered where to start or found the course not so easy to follow. With this hands-on and practical machine learning course, you can learn and start applying machine learning in less than a week without having to be an expert mathematician.
In this course, you will be introduced to a new machine learning aspect in each section followed by a practical assignment as a homework to help you in efficiently implement the learnings in a practical manner. With the systematic and fast-paced approach to this course, learn machine learning using Python in the most practical and structured way to develop machine learning projects in Python in a week.
This course is structured to unlock the potential of Python machine learning in the shortest amount of time. If you are looking to upgrade your machine learning skills using Python in the quickest possible time, then this course is for you!
This course uses Python 3.6 while not the latest version available, it provides relevant and informative content for legacy users of Python.
Table of contents
- Chapter 1 : Enter the Machine Learning World!
- Chapter 2 : Build Your First Predicting Model
Chapter 3 : Image Classification Using Supervised Learning
- Review of Predicting Energy Output of a Power Plant 00:07:11
- Logistic Regression 00:05:51
- Classifying Images Using Logistic Regression 00:05:16
- Support Vector Machines 00:01:56
- Kernels in a SVM 00:01:20
- Classifying Images Using Support Vector Machines 00:03:01
- Assignment – Start Image Classifying Using Support Vector Machines 00:03:25
Chapter 4 : Improving Model Accuracy
- Review of Classifying Images Using Support Vector Machines 00:05:17
- Model Evaluation 00:03:06
- Better Measures than Accuracy 00:05:13
- Understanding the Results 00:02:44
- Improving the Models 00:02:57
- Assignment – Getting Better Test Sample Results by Measuring Model Performance 00:01:51
- Chapter 5 : Finding Patterns and Structures in Unlabeled Data
- Chapter 6 : Sentiment Analysis Using Neural Networks
Chapter 7 : Mastering Kaggle Titanic Competition Using Random Forest
- Review of Building a Sentiment Analyser ANN 00:05:24
- Decision Trees 00:03:04
- Working of a Decision Tree 00:03:08
- Techniques to Further Improve a Model 00:01:48
- Random Forest as an Improved Machine Learning Approach 00:01:44
- Weekend Task – Solving Titanic Problem Using Random Forest 00:02:06
- Title: Python Machine Learning in 7 Days
- Release date: June 2018
- Publisher(s): Packt Publishing
- ISBN: 9781788999137
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