Training Your Systems with Python Statistical Modeling. With the help of this course you can Learn statistical analysis by using various machine learning models.
This course was created by Packt Publishing. It was rated 4.7 out of 5 by approx 13659 ratings. There are approx 58083 users enrolled with this course, so don’t wait to download yours now. This course also includes 4 hours on-demand video, 1 Supplemental Resource, Full lifetime access, Access on mobile and TV & Certificate of Completion.
What Will You Learn?
Find correlations in your data using SciPy
Train different machine learning models and evaluate their results
Make predictions using Naïve Bayes Algorithm with the help of Python code
Employ support vector machines for classification and detection
Employ ridge and lasso regression models
Train a neural network
Python, a multi-paradigm programming language, has become the
language of choice for data scientists for data analysis, visualization,
and machine learning. This course takes you through the various
different concepts that get you acquainted and working with the
different aspects of Machine Learning.
You’ll start by diving into
classical statistical analysis, where you will learn to compute
descriptive statistics with Pandas. From there, you will be introduced
to supervised learning, where you will explore the principles of machine
learning and train different machine learning models. Next, you’ll work
with binary prediction models, such as data classification using
K-nearest neighbors, decision trees, and random forests.
that, you’ll work with algorithms for regression analysis, and employ
different types of regression, such as ridge and lasso regression, and
spline interpolation using SciPy. Then, you’ll work on neural networks,
train them, and employ regression on neural networks. You’ll be
introduced to clustering, and learn to evaluate cluster model results,
as well as employ different clustering types such as hierarchical and
spectral clustering. Finally, you’ll learn about the dimensionality
reduction concepts such as principal component analysis and low
About the Author :
Curtis Miller is
Associate Instructor at the University of Utah, and an MSTAT student. He
is currently involved in research on data analysis from statistical and
computer science perspectives. Curtis has published research on policy
and economic issues.