Applied Machine Learning Engineer Career Guide

"Learning Pandas 2.0" is an essential guide for anyone looking to harness the power of Python's premier data manipulation library. With this comprehensive resource, you will not only master core Pandas 2.0 concepts but also learn how to employ its advanced features to perform efficient data manipulation and analysis.

Throughout the book, you will acquire a deep understanding of Pandas 2.0's data structures, indexing, and selection techniques. Gain expertise in loading, storing, and cleaning data from various file formats and sources, ensuring data integrity and consistency. As you progress, you will delve into advanced data transformation, merging, and aggregation methods to extract meaningful insights and generate insightful reports.

"Learning Pandas 2.0" also covers specialized data processing needs like time series data, DateTime operations, and geospatial analysis. Furthermore, this book demonstrates how to integrate Pandas 2.0 with machine learning libraries like Scikit-learn, TensorFlow, and PyTorch for predictive analytics. This will empower you to build powerful data-driven models to solve complex problems and enhance your decision-making capabilities.

• Master core Pandas 2.0 concepts, including data structures, indexing, and selection for efficient data manipulation.
• Load, store, and clean data from various file formats and sources, ensuring data integrity and consistency.
• Perform advanced data transformation, merging, and aggregation techniques for insightful analysis and reporting.
• Harness time series data, DateTime operations, and geospatial analysis for specialized data processing needs.
• Visualize data effectively using Seaborn, Plotly, and advanced geospatial visualization tools.
• Integrate Pandas 2.0 with machine learning libraries like Scikit-learn, TensorFlow, and PyTorch for predictive analytics.

1. Introduction to Pandas 2.0
2. Data Read, Storage, and File Formats
3. Indexing and Selecting Data
4. Data Manipulation and Transformation
5. Time Series and DateTime Operations
6. Performance Optimization and Scaling
7. Machine Learning with Pandas 2.0
8. Text Data and Natural Language Processing
9. Geospatial Data Analysis

"Neural Networks with Python" serves as an introductory guide for those taking their first steps into neural network development with Python. It's tailored to assist beginners in understanding the foundational elements of neural networks and to provide them with the confidence to delve deeper into this intriguing area of machine learning.

In this book, readers will embark on a learning journey, starting from the very basics of Python programming, progressing through essential concepts, and gradually building up to more complex neural network architectures. The book simplifies the learning process by using relatable examples and datasets, making the concepts accessible to everyone. You will be introduced to various neural network architectures such as Feedforward, Convolutional, and Recurrent Neural Networks, among others. Each type is explained in a clear and concise manner, with practical examples to illustrate their applications.

The book emphasizes the real-world applications and practical aspects of neural network development, rather than just theoretical knowledge. Readers will also find guidance on how to troubleshoot and refine their neural network models. The goal is to equip you with a solid understanding of how to create efficient and effective neural networks, while also being mindful of the common challenges that may arise. By the end of your journey with this book, you will have a foundational understanding of neural networks within the Python ecosystem and be prepared to apply this knowledge to real-world scenarios.

"Neural Networks with Python" aims to be your stepping stone into the vast world of machine learning, empowering you to build upon this knowledge and explore more advanced topics in the future.

Key Learnings

• Master Python for machine learning, from setup to complex models.
• Gain flexibility with diverse neural network architectures for various problems.
• Hands-on experience in building, training, and fine-tuning neural networks.
• Learn strategic approaches for troubleshooting and optimizing neural models.
• Grasp advanced topics like autoencoders, capsule networks, and attention mechanisms.
• Acquire skills in crucial data preprocessing and augmentation techniques.
• Understand and apply optimization techniques and hyperparameter tuning.
• Implement an end-to-end machine learning project, from data to deployment.

Table of Content

1. Python, TensorFlow, and your First Neural Network
2. Deep Dive into Feedforward Networks
3. Convolutional Networks for Visual Tasks
4. Recurrent Networks for Sequence Data
5. Data Generation with GANs
6. Transformers for Complex Tasks
7. Autoencoders for Data Compression and Generation
8. Capsule Networks

"Learning PyTorch 2.0, Second Edition" is a fast-learning, hands-on book that emphasizes practical PyTorch scripting and efficient model development using PyTorch 2.3 and CUDA 12. This edition is centered on practical applications and presents a concise methodology for attaining proficiency in the most recent features of PyTorch.

The book presents a practical program based on the fish dataset which provides step-by-step guidance through the processes of building, training and deploying neural networks, with each example prepared for immediate implementation. Given your familiarity with machine learning and neural networks, this book offers concise explanations of foundational topics, allowing you to proceed directly to the practical, advanced aspects of PyTorch programming. The key learnings include the design of various types of neural networks, the use of torch.compile() for performance optimization, the deployment of models using TorchServe, and the implementation of quantization for efficient inference.

Furthermore, you will also learn to migrate TensorFlow models to PyTorch using the ONNX format. The book employs essential libraries, including torchvision, torchserve, tf2onnx, onnxruntime, and requests, to facilitate seamless integration of PyTorch with production environments. Regardless of whether the objective is to fine-tune models or to deploy them on a large scale, this second edition is designed to ensure maximum efficiency and speed, with practical PyTorch scripting at the forefront of each chapter.

Key Learnings

• Master tensor manipulations and advanced operations using PyTorch's efficient tensor libraries.
• Build feedforward, convolutional, and recurrent neural networks from scratch.
• Implement transformer models for modern natural language processing tasks.
• Use CUDA 12 and mixed precision training (AMP) to accelerate model training and inference.
• Deploy PyTorch models in production using TorchServe, including multi-model serving and versioning.
• Migrate TensorFlow models to PyTorch using ONNX format for seamless cross-framework compatibility.
• Optimize neural network architectures using torch.compile() for improved speed and efficiency.
• Utilize PyTorch's Quantization API to reduce model size and speed up inference.
• Setup custom layers and architectures for neural networks to tackle domain-specific problems. 
• Monitor and log model performance in real-time using TorchServe's built-in tools and configurations.

Table of Content

1. Introduction To PyTorch 2.3 and CUDA 12
2. Getting Started with Tensors
3. Building Neural Networks with PyTorch
4. Training Neural Networks
5. Advanced Neural Network Architectures
6. Quantization and Model Optimization
7. Migrating TensorFlow to PyTorch
8. Deploying PyTorch Models with TorchServe

This book's got a bunch of handy recipes for data science pros to get them through the most common challenges they face when using Python tools and libraries. Each recipe shows you exactly how to do something step-by-step. You can load CSVs directly from a URL, flatten nested JSON, query SQL and NoSQL databases, import Excel sheets, or stream large files in memory-safe batches.

Once the data's loaded, you'll find simple ways to spot and fill in missing values, standardize categories that are off, clip outliers, normalize features, get rid of duplicates, and extract the year, month, or weekday from timestamps. You'll learn how to run quick analyses, like generating descriptive statistics, plotting histograms and correlation heatmaps, building pivot tables, creating scatter-matrix plots, and drawing time-series line charts to spot trends. You'll learn how to build polynomial features, compare MinMax, Standard, and Robust scaling, smooth data with rolling averages, apply PCA to reduce dimensions, and encode high-cardinality fields with sparse one-hot encoding using feature engineering recipes.

As for machine learning, you'll learn to put together end-to-end pipelines that handle imputation, scaling, feature selection, and modeling in one object, create custom transformers, automate hyperparameter searches with GridSearchCV, save and load your pipelines, and let SelectKBest pick the top features automatically. You'll learn how to test hypotheses with t-tests and chi-square tests, build linear and Ridge regressions, work with decision trees and random forests, segment countries using clustering, and evaluate models using MSE, classification reports, and ROC curves. And you'll finally get a handle on debugging and integration: fixing pandas merge errors, correcting NumPy broadcasting mismatches, and making sure your plots are consistent.

Key Learnings

• You can load remote CSVs directly into pandas using read_csv, so you don't have to deal with manual downloads and file clutter.
• Use json_normalize to convert nested JSON responses into simple tables, making it a breeze to analyze.
• You can query relational and NoSQL databases directly from Python, and the results will merge seamlessly into Pandas.
• Find and fill in missing values using IGNSA(), forward-fill, and median strategies for all of your data over time.
• You can free up a lot of memory by turning string columns into Pandas' Categorical dtype.
• You can speed up computations with NumPy vectorization and chunked CSV reading to prevent RAM exhaustion.
• You can build feature pipelines using custom transformers, scaling, and automated hyperparameter tuning with GridSearchCV.
• Use regression, tree-based, and clustering algorithms to show linear, nonlinear, and group-specific vaccination patterns.
• Evaluate models using MSE, R², precision, recall, and ROC curves to assess their performance.
• Set up automated data retrieval with scheduled API pulls, cloud storage, Kafka streams, and GraphQL queries.

Table of Content

1. Data Ingestion from Multiple Sources
2. Preprocessing and Cleaning Complex Datasets
3. Performing Quick Exploratory Analysis
4. Optimizing Data Structures and Performance
5. Feature Engineering and Transformation
6. Building Machine Learning Pipelines
7. Implementing Statistical and Machine Learning Techniques
8. Debugging and Troubleshooting
9. Advanced Data Retrieval and Integration