Data Science and Machine Learning with Python: Learn and Practice Series

✍ Scribed by NIBEDITA Sahu

Publisher: NIBEDITA Sahu
Year: 2023
Tongue: English
Leaves: 314
Series: Learn and Practice Series
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Unlock your potential as an AI and ML professional! This book covers basic to advanced level topics required to master the Machine Learning concepts. There are lot of programs implemented which goes with the explaination - thats why we call it Learn and Practice. Book uses Scikit-learn (formerly scikits.learn and also known as sklearn) is the most popular package and also a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.Happy Coding in Python

✦ Table of Contents

Title Page
Data Science Fusion: Integrating Maths, Python, and Machine Learning
Chapter 1: Understanding Data Science
Chapter 2: The Data Science Workflow
Chapter 3: Tools and Technologies in Data Science
Chapter 4: Foundations of Mathematics for Data Science
Chapter 5: Linear Algebra for Data Scientists
Chapter 6: Multivariable Calculus: A Data Science Perspective
Chapter 7: Probability and Statistics for Data Analysis
Chapter 8: Python Fundamentals
Chapter 9: Essential Python Libraries for Data Science
Chapter 10: Data Wrangling and Preprocessing with Python
Chapter 11: Data Visualization Techniques with Matplotlib and Seaborn
Chapter 12: Introduction to Machine Learning
Chapter 13: Supervised Learning: Regression and Classification
Chapter 14: Unsupervised Learning: Clustering and Dimensionality Reduction
Chapter 15: Evaluation Metrics for Machine Learning Models
Chapter 16: Ensembles and Boosting Algorithms
Chapter 17: Deep Learning Fundamentals
Chapter 18: Convolutional Neural Networks (CNNs) for Image Analysis
Chapter 19: Recurrent Neural Networks (RNNs) for Sequence Data
Chapter 20: Natural Language Processing (NLP) with Machine Learning
Chapter 1: Understanding Data Science
Chapter 2: The Data Science Workflow
Chapter 3: Tools and Technologies in Data Science
Chapter 4: Foundations of Mathematics for Data Science
Chapter 5: Linear Algebra for Data Scientists
Chapter 6: Multivariable Calculus: A Data Science Perspective
Chapter 7: Probability and Statistics for Data Analysis
Chapter 8: Python Fundamentals
Chapter 9: Essential Python Libraries for Data Science
Chapter 10: Data Wrangling and Preprocessing with Python
Chapter 11: Data Visualization Techniques with Matplotlib and Seaborn
Chapter 12: Introduction to Machine Learning
Chapter 13: Supervised Learning: Regression and Classification
Chapter 14: Unsupervised Learning: Clustering and Dimensionality Reduction
Chapter 15: Evaluation Metrics for Machine Learning Models
Chapter 16: Ensembles and Boosting Algorithms
Chapter 17: Deep Learning Fundamentals
Chapter 18: Convolutional Neural Networks (CNNs) for Image Analysis
Chapter 19: Recurrent Neural Networks (RNNs) for Sequence Data
Chapter 20: Natural Language Processing (NLP) with Machine Learning
Appendix

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