| Description |
1 online resource (524 p.) |
| Series |
Synthesis Lectures on Engineering, Science, and Technology |
|
Synthesis Lectures on Engineering, Science, and Technology (Springer (Firm))
|
| Contents |
Intro -- Foreword -- Preface -- Key Features -- Acknowledgments -- Essentials of Python for Artificial Intelligence and Machine Learning -- Table of Contents -- 1 Introduction -- 1.1 What is Data Science? -- 1.2 Key Areas of Data Science -- 1.3 Why is Data Science Important? -- 1.4 Data Science Applications -- 1.5 Benefits of Data Science -- 1.6 Operational Life Cycle of Data Science -- 1.7 Significance of Data Science Process -- 1.9 Data Science Technologies, Techniques, and Methods -- 1.10 Data Science Tools and Platforms -- 1.11 Why Use Python for ML and AI |
|
1.12 Installation Considerations -- 1.13 Python Machine Learning Ecosystem -- 1.14 Magic Commands -- 1.15 References -- 2 Statistical Methods and Models -- 2.1 Introduction -- 2.2 Statistical terms and definitions -- 2.3 Statistical distributions -- 2.4 Non-parametric Statistical Data -- 2.5 Statistical Tests -- 2.6 Bayes Theorem and its Applications -- 2.7 Regression Models for Prediction and Classification -- 2.8 References: -- 3 Python Language Basics -- 3.1 Numbers -- 3.2 Variables -- 3.3 Strings -- 3.4 Control Flow -- 3.5 Data Structures -- 3.6 Functions -- 3.7 References |
|
4 Introduction to NumPy -- 4.1 Why Use NumPy? -- 4.2 What is the Relationship Between NumPy, SciPy, Scikit-learn, and Pandas? -- 4.3 The NumPy ndarrays: A Multidimensional Array -- 4.4 Reshaping Arrays -- 4.5 Flattening an Array -- 4.6 Expanding and Squeezing an Array -- 4.7 Array Indexing and Slicing -- 4.8 Stacking and Concatenating Arrays -- 4.9 Array Math and Universal Functions -- 4.10 NumPy Broadcasting -- 4.11 Linear Algebra with NumPy Arrays -- 4.12 What is Missing in NumPy? -- 4.13 References -- 5 Introduction to Pandas -- 5.1 Key Features of Pandas -- 5.2 Benefits of Pandas |
|
5.3 The Rise in Popularity of Pandas -- 5.4 Difference between NumPy and Pandas -- 5.5 Pandas Data Objects -- 5.6 Difference between NumPy array and Pandas Series -- 5.7 Accessing Data from Series -- 5.8 Series object attributes -- 5.9 Dealing with missing or null values -- 5.10 Arithmetic Operations on Series -- 5.11Pandas DataFrame -- 5.12 DataFrame metadata -- 5.13 Get the statistics from DataFrame -- 5.14 DataFrame Attributes -- 5.15 DataFrame Selection -- 5.16 Subset of the columns of a data frame based on dtype -- 5.17 Data Frame modification -- 5.18 How to Use where function |
|
5.19 Rename the Columns -- 5.20 Reverse the row order -- 5.21 How to split a text column into separate columns -- 5.22 Sorting -- 5.23 Refrences -- 6 Data Manipulation with Pandas -- 6.1 Data Preparation -- 6.2 Challenges in Data Preparation -- 6.3 When to Use Data Preprocessing? -- 6.4 Feature Aggregation -- 6.5 Pivot Tables -- 6.6 Combining and Merging datasets -- 6.7 Data Cleaning -- 6.8 What is the difference between data cleaning and data transformation? -- 6.9 Missing Values -- 6.10Duplicated Values -- 6.11Discretization and Binning -- 6.12 Detecting Outliers |
| Summary |
This book introduces the essentials of Python for the emerging fields of Machine Learning (ML) and Artificial Intelligence (AI). The authors explore the use of Python's advanced module features and apply them in probability, statistical testing, signal processing, financial forecasting, and various other applications. This includes mathematical operations with array data structures, Data Manipulation, Data Cleaning, machine learning, Data pipeline, probability density functions, interpolation, visualization, and other high-performance benefits using the core scientific packages NumPy, Pandas, SciPy, Sklearn/Scikit learn and Matplotlib. Readers will gain a deep understanding with problem-solving experience on these powerful platforms when dealing with engineering and scientific problems related to Machine Learning and Artificial Intelligence. Several examples of real problems using these techniques are provided along with examples. The authors also focus on the best practices in the industry on using Python for AI and ML. Deployment on a cloud infrastructure is described in detail (with code) to emphasize real scenarios. Includes several real examples of how to write and deploy code, including on a cloud infrastructure Provides single-source on Python for machine learning and artificial intelligence, from basics to real implementation Includes sufficient coverage of Python libraries, frameworks, and tools to develop complex data science applications |
| Notes |
Description based upon print version of record |
|
6.13 Computing Dummy Variables |
| Bibliography |
Includes bibliographical references |
| Subject |
Machine learning.
|
|
Artificial intelligence.
|
|
Python (Computer program language)
|
|
artificial intelligence.
|
| Form |
Electronic book
|
| Author |
Bagchi, Anupam.
|
| ISBN |
9783031437250 |
|
303143725X |
|
