Python Machine Learning Machine Learning and Deep Learning with Python scikit-learn and Tensorflow
- A simple language has been used.
- Many examples have been given, both theoretically and programmatically.
- Screenshots showing program outputs have been added.
- To help you understand the basics of machine learning and deep learning.
- Understand the various categories of machine learning algorithms.
- To help you understand how different machine learning algorithms work.
- You will learn how to implement various machine learning algorithms programmatically in Python.
- To help you learn how to use Scikit-Learn and TensorFlow Libraries in Python.
- To help you know how to analyze data programmatically to extract patterns, trends, and relationships between variables.
- Anybody who is a complete beginner to machine learning in Python.
- Anybody who needs to advance their programming skills in Python for machine learning programming and deep learning.
- Professionals in data science.
- Professors, lecturers or tutors who are looking to find better ways to explain machine learning to their students in the simplest and easiest way.
- Students and academicians, especially those focusing on neural networks, machine learning, and deep learning.
- Python 3.X
- Numpy
- Pandas
- Matplotlib
The Author guides you on how to install the rest of the Python libraries that are required for machine learning and deep learning.
What is inside the book:- Getting Started
- Environment Setup
- Using Scikit-Learn
- Linear Regression with Scikit-Learn
- k-Nearest Neighbors Algorithm
- K-Means Clustering
- Support Vector Machines
- Neural Networks with Scikit-learn
- Random Forest Algorithm
- Using TensorFlow
- Recurrent Neural Networks with TensorFlow
- Linear Classifier
Publisher Name | Independently Published |
---|---|
Author Name | Hagendorf, Col |
Format | Audio |
Bisac Subject Major | COM |
Language | NG |
Isbn 10 | 1090434162 |
Isbn 13 | 9781090434166 |
Target Age Group | min:NA, max:NA |
Series | 0008793461 |
Dimensions | 00.90" H x 00.06" L x 00.00" W |
Page Count | 178 |