A problem-focused guide for tackling industrial machine learning issues with methods and frameworks chosen by experts.

Key Features

● Popular techniques for problem formulation, data collection, and data cleaning in machine learning.

● Comprehensive and useful machine learning tools such as MLFlow, Streamlit, and many more.

● Covers numerous machine learning libraries, including Tensorflow, FastAI, Scikit-Learn, Pandas, and Numpy.

Description

This book discusses how to apply machine learning 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 learning 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 learn how to formulate a problem, collect data, build a model, and tune it. You will learn 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 learning profile, various machine learning libraries, Statistics, and FAST API.

Throughout the book, you will use Python to experiment with machine learning libraries such as Tensorflow, Scikit-learn, Spacy, and FastAI. The book will help train our models on both Kaggle and our datasets.

What you will learn

● Construct a machine learning problem, evaluate the feasibility, and gather and clean data.

● Learn to explore data first, select, and train machine learning 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 for

This book caters to beginners in machine learning, software engineers, and students who want to gain a good understanding of machine learning concepts and create production-ready ML systems. This book assumes you have a beginner-level understanding of Python.

Table of Contents

1. Introduction to Machine Learning

2. Problem Formulation in Machine Learning

3. Data Acquisition and Cleaning

4. Exploratory Data Analysis

5. Model Building and Tuning

6. Taking Our Model into Production

7. Data Analytics Use Case

8. Building a Custom Image Classifier from Scratch

9. Building a News Summarization App Using Transformers

10. Multiple Inputs and Multiple Output Models

11. Contributing to the Community

12. Creating Your Project

13. Crash Course in Numpy, Matplotlib, and Pandas

14. Crash Course in Linear Algebra and Statistics

15. Crash Course in FastAPI