Machine Learning with Python
Unlocking AI Potential with Python and Machine Learning
- 146 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
Unlock the secrets of data science and machine learning with our comprehensive Python course, designed to take you from basics to complex algorithms effortlessly
Key Features
- Navigate through Python's machine learning libraries effectively
- Learn exploratory data analysis and data scrubbing techniques
- Design and evaluate machine learning models with precision
Book Description
The course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling.The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts.Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.
What you will learn
- Analyze datasets for insights
- Scrub data for model readiness
- Understand key ML algorithms
- Design and validate models
- Apply Linear and Logistic Regression
- Utilize K-Nearest Neighbors and SVMs
Who this book is for
This course is ideal for aspiring data scientists and professionals looking to integrate machine learning into their workflows. A basic understanding of Python and statistics is beneficial.
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Information
Table of contents
- FOREWORD
- DATASETS USED IN THIS BOOK
- INTRODUCTION
- DEVELOPMENT ENVIRONMENT
- MACHINE LEARNING LIBRARIES
- EXPLORATORY DATA ANALYSIS
- DATA SCRUBBING
- PRE-MODEL ALGORITHMS
- SPLIT VALIDATION
- MODEL DESIGN
- LINEAR REGRESSION
- LOGISTIC REGRESSION
- SUPPORT VECTOR MACHINES
- k-NEAREST NEIGHBORS
- TREE-BASED METHODS
- NEXT STEPS
- APPENDIX 1: INTRODUCTION TO PYTHON
- APPENDIX 2: PRINT COLUMNS
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