Applied Machine Leaing Solutions with Python: Production-ready ML Projects Using Cutting-edge Libraries and Powerful Statistical Techniques (English Edition)

by: Siddhanta Bhatta (Author)

Publication Date: 2021/9/1

Language: English

Print Length: 356 pages

ISBN-10: 9391030432

ISBN-13: 9789391030438

Book Description

A problem-focused guide for tackling industrial machine leaing issues with methods and frameworks chosen by experts.Key Features● Popular techniques for problem formulation, data collection, and data cleaning in machine leaing.● Comprehensive and useful machine leaing tools such as MLFlow, Streamlit, and many more.● Covers numerous machine leaing libraries, including Tensorflow, FastAI, Scikit-Lea, Pandas, and Numpy.DescriptionThis book discusses how to apply machine leaing to real-world problems by utilizing real-world data. In this book, you will investigate data sources, become acquainted with data pipelines, and practice how machine leaing works through numerous examples and case studies.The book begins with high-level concepts and implementation (with code!) and progresses towards the real-world of ML systems. It briefly discusses various concepts of Statistics and Linear Algebra. You will lea how to formulate a problem, collect data, build a model, and tune it. You will lea about use cases for data analytics, computer vision, and natural language processing. You will also explore nonlinear architecture, thus enabling you to build models with multiple inputs and outputs. You will get trained on creating a machine leaing profile, various machine leaing libraries, Statistics, and FAST API.Throughout the book, you will use Python to experiment with machine leaing libraries such as Tensorflow, Scikit-lea, Spacy, and FastAI. The book will help train our models on both Kaggle and our datasets.What you will lea● Construct a machine leaing problem, evaluate the feasibility, and gather and clean data.● Lea to explore data first, select, and train machine leaing models.● Fine-tune the chosen model, deploy, and monitor it in production.● Discover popular models for data analytics, computer vision, and Natural Language Processing.Who this book is forThis book caters to beginners in machine leaing, software engineers, and students who want to gain a good understanding of machine leaing concepts and create production-ready ML systems. This book assumes you have a beginner-level understanding of Python.Table of Contents1. Introduction to Machine Leaing2. Problem Formulation in Machine Leaing3. Data Acquisition and Cleaning4. Exploratory Data Analysis5. Model Building and Tuning6. Taking Our Model into Production7. Data Analytics Use Case8. Building a Custom Image Classifier from Scratch9. Building a News Summarization App Using Transformers10. Multiple Inputs and Multiple Output Models11. Contributing to the Community12. Creating Your Project13. Crash Course in Numpy, Matplotlib, and Pandas14. Crash Course in Linear Algebra and Statistics15. Crash Course in FastAPIAbout the Authors Siddhanta Bhatta is a Machine Leaing engineer with 6 years of experience in building machine leaing products. He is currently working as a Senior Software Engineer in Data Analytics, Machine Leaing, and Deep Leaing. He has built multiple data apps in various domains such as vision, NLP, Data Analytics, and many more. He is a Microsoft-certified data scientist who believes in data literacy.LinkedIn Profile: https://www.linkedin.com/in/siddhanta-bhatta-377880a7/Blog Link: https://joyofunderstanding926957091.wordpress.com/

About the Author

A problem-focused guide for tackling industrial machine leaing issues with methods and frameworks chosen by experts.Key Features● Popular techniques for problem formulation, data collection, and data cleaning in machine leaing.● Comprehensive and useful machine leaing tools such as MLFlow, Streamlit, and many more.● Covers numerous machine leaing libraries, including Tensorflow, FastAI, Scikit-Lea, Pandas, and Numpy.DescriptionThis book discusses how to apply machine leaing to real-world problems by utilizing real-world data. In this book, you will investigate data sources, become acquainted with data pipelines, and practice how machine leaing works through numerous examples and case studies.The book begins with high-level concepts and implementation (with code!) and progresses towards the real-world of ML systems. It briefly discusses various concepts of Statistics and Linear Algebra. You will lea how to formulate a problem, collect data, build a model, and tune it. You will lea about use cases for data analytics, computer vision, and natural language processing. You will also explore nonlinear architecture, thus enabling you to build models with multiple inputs and outputs. You will get trained on creating a machine leaing profile, various machine leaing libraries, Statistics, and FAST API.Throughout the book, you will use Python to experiment with machine leaing libraries such as Tensorflow, Scikit-lea, Spacy, and FastAI. The book will help train our models on both Kaggle and our datasets.What you will lea● Construct a machine leaing problem, evaluate the feasibility, and gather and clean data.● Lea to explore data first, select, and train machine leaing models.● Fine-tune the chosen model, deploy, and monitor it in production.● Discover popular models for data analytics, computer vision, and Natural Language Processing.Who this book is forThis book caters to beginners in machine leaing, software engineers, and students who want to gain a good understanding of machine leaing concepts and create production-ready ML systems. This book assumes you have a beginner-level understanding of Python.Table of Contents1. Introduction to Machine Leaing2. Problem Formulation in Machine Leaing3. Data Acquisition and Cleaning4. Exploratory Data Analysis5. Model Building and Tuning6. Taking Our Model into Production7. Data Analytics Use Case8. Building a Custom Image Classifier from Scratch9. Building a News Summarization App Using Transformers10. Multiple Inputs and Multiple Output Models11. Contributing to the Community12. Creating Your Project13. Crash Course in Numpy, Matplotlib, and Pandas14. Crash Course in Linear Algebra and Statistics15. Crash Course in FastAPIAbout the Authors Siddhanta Bhatta is a Machine Leaing engineer with 6 years of experience in building machine leaing products. He is currently working as a Senior Software Engineer in Data Analytics, Machine Leaing, and Deep Leaing. He has built multiple data apps in various domains such as vision, NLP, Data Analytics, and many more. He is a Microsoft-certified data scientist who believes in data literacy.LinkedIn Profile: https://www.linkedin.com/in/siddhanta-bhatta-377880a7/Blog Link: https://joyofunderstanding926957091.wordpress.com/