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 with different functions in Tensorflow
We can make the tensors from the known values. The tf package provides seven function which we can use to from tensors with known values. Constant,zeros,ones,fill etc.
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.
Exploring the Tensorflow
Once you install the Tensorflow, that contains the wide variety of files and folders. Two top level folders are particularly important. The core directory and the contrib directory.
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.
Learning scikit-learn: Machine Learning in Python Book
Machine learning, the art of creating applications that learn from experience and data, has been around for many years.With Learning scikit-learn: Machine Learning in Python, you will learn to incorporate machine learning in your applications.
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.
Model Evaluation and Bias-Variance Trade-Off In Machine Learning
We should spent our time on Model Evaluation and Bias-Variance Trade-Off to bring the best model which do not underfit or overfit the data.