Terms to remember while building a Machine Learning Model
There are different terms we should remember while training our machine learning model, so that we can use our machine learning model more effectively and properly.
Creating Tensors in Tensorflow
Every program we developed is started by declaring the variables, most Tensorflow applications we start developing is started by creating the tensors.
The Evolution of Machine Learning
The development of Machine Learning started from very early. Different scientists and researchers hard work and research made much Machine Learning possible in a way we are learning now.
Choosing a Classification Algorithm
Choosing a appropriate algorithm for classificaion of a particular problem task requires a lot of practice, each algorithms has its own quirks and is based on certain assumptions.
A Roadmap For Building Machine Learning Systems
To develop a machine learning model there is roadmap which you can follow so that you can build the best model with the best accuracy.
Cross-Validation in Machine Learning
Cross-validation is used to test how effective our machine learning model performs. Our Model's performance is dependent on the way we split the data.
Beginning Python visualization: crafting visual transformation scripts Book
Beginning Python Visualization: Crafting Visual Transformation Scripts talks about turning many types of small data sources into useful visual data. And you will learn Python as part of the bargain.
Linear, Higher and n-dimensional Space in Machine Learning
Usually refers to number of attributes.There are different types of dimension in which we can represent our data points.Linear,Higher,n-dimensional space.
Difference between Dimensions, Attribute and Feature in Machine Learning
Dimensions Usually refers to the number of attributes.Attributes Is one particular type of data in your points.Feature It may have multiple meaning depending on the context.
Machine Learning in Python Book
Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python.
Python Machine Learning By Example Book
Master the art of building your own machine learning systems with this example-based practical guide
Logistic Regression In ML
Logistic Regression as a method of classification problem.Logistic regression will allow us to solve Classification Problem where we will be working with Discrete Categories.