Python Machine Learning 1st edition by Wei Meng Lee – Ebook PDF Instant Download/Delivery: B07QDC84YR, 978-1119545675
Full download Python Machine Learning 1st edition after payment
Product details:
ISBN 10: B07QDC84YR
ISBN 13: 978-1119545675
Author: Wei Meng Lee
Python makes machine learning easy for beginners and experienced developers
With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart—it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today.
Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand.
- Python data science—manipulating data and data visualization
- Data cleansing
- Understanding Machine learning algorithms
- Supervised learning algorithms
- Unsupervised learning algorithms
- Deploying machine learning models
Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level.
Python Machine Learning 1st Table of contents:
CHAPTER 1: Introduction to Machine Learning
What Is Machine Learning?
Getting the Tools
Summary
CHAPTER 2: Extending Python Using NumPy
What Is NumPy?
Creating NumPy Arrays
Array Indexing
Reshaping Arrays
Array Math
Array Assignment
Summary
CHAPTER 3: Manipulating Tabular Data Using Pandas
What Is Pandas?
Pandas Series
Pandas DataFrame
Summary
CHAPTER 4: Data Visualization Using matplotlib
What Is matplotlib?
Plotting Line Charts
Plotting Bar Charts
Plotting Pie Charts
Plotting Scatter Plots
Plotting Using Seaborn
Summary
CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning
Introduction to Scikit‐learn
Getting Datasets
Getting Started with Scikit‐learn
Data Cleansing
Summary
CHAPTER 6: Supervised Learning—Linear Regression
Types of Linear Regression
Linear Regression
Polynomial Regression
Summary
CHAPTER 7: Supervised Learning—Classification Using Logistic Regression
What Is Logistic Regression?
Using the Breast Cancer Wisconsin (Diagnostic) Data Set
Summary
CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines
What Is a Support Vector Machine?
Kernel Trick
Types of Kernels
Using SVM for Real‐Life Problems
Summary
CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
What Is K‐Nearest Neighbors?
Summary
CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means
What Is Unsupervised Learning?
Using K‐Means to Solve Real‐Life Problems
Summary
CHAPTER 11: Using Azure Machine Learning Studio
What Is Microsoft Azure Machine Learning Studio?
Summary
CHAPTER 12: Deploying Machine Learning Models
Deploying ML
Case Study
Deploying the Model
Creating the Client Application to Use the Model
People also search for Python Machine Learning 1st :
python machine learning libraries
isro free python machine learning course
free python machine learning course
scikit learn python machine learning
coursera python machine learning