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Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTo
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Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTo

Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTo

Ashwin / Joshi Pajankar

335 pages, parution le 05/03/2022

Résumé

Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios.
The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.
After completing Hands-on Machine Learning with Python , you will be able to implement machine learning and neural network solutions and extend them to your advantage.
What You'll Learn
  • Review data structures in NumPy and Pandas
  • Demonstrate machine learning techniques and algorithm
  • Understand supervised learning and unsupervised learning
  • Examine convolutional neural networks and Recurrent neural networks
  • Get acquainted with scikit-learn and PyTorch
  • Predict sequences in recurrent neural networks and long short term memory

Who This Book Is For
Data scientists, machine learning engineers, and software professionals with basic skills in Python programming.
Chapter 1: Getting Started with Python 3 and Jupyter NotebookChapter Goal: Introduce the reader to the basics of Python Programming language, philosophy, and installation. We will also learn how to install it on various platforms. This chapter also introduces the readers to Python programming with Jupyter Notebook. In the end, we will also have a brief overview of the constituent libraries of sciPy stack.No of pages - 30Sub -Topics1. Introduction to the Python programming language2. History of Python3. Python enhancement proposals (PEPs)4. Philosophy of Python5. Real life applications of Python6. Installing Python on various platforms (Windows and Debian Linux Flavors)7. Python modes (Interactive and Script)8. Pip (pip installs python)9. Introduction to the scientific Python ecosystem10. Overview of Jupyter Notebook11. Installation of Jupyter Notebook12. Running code in Jupyter Notebook Chapter 2: Getting Started with NumPyChapter Goal: Get started with NumPy Ndarrays and the basics of NumPy library. The chapter covers the instructions for installation and basic usage of NumPy.No of pages: 10Sub - Topics:1. Introduction to NumPy2. Install NumPy with pip33. Indexing and Slicing of ndarrays4. Properties of ndarrays5. Constants in NumPy6. Datatypes in datatypes Chapter 3 : Introduction to Data VisualizationChapter goal - In this chapter, we will discuss the various ndarray creation routines available in NumPy. We will also get started with Visualizations with Matplotlib. We will learn how to visualize the various numerical ranges with Matplotlib.No of pages: 15Sub - Topics:1. Ones and zeros2. Matrices3. Introduction to Matplotlib4. Running Matplotlib programs in Jupyter Notebook and the script mode5. Numerical ranges and visualizations Chapter 4 : Introduction to Pandas Chapter goal - Get started with Pandas data structuresNo of pages: 10Sub - Topics:1. Install Pandas2. What is Pandas3. Introduction to series4. Introduction to dataframesa) Plain Text Fileb) CSVc) Handling excel filed) NumPy file formate) NumPy CSV file readingf) Matplotlib Cbookg) Read CSVh) Read Exceli) Read JSONj) Picklek) Pandas and webl) Read SQLm) Clipboard Chapter 5: Introduction to Machine Learning with Scikit-LearnChapter goal - Get acquainted with machine learning basics and scikit-Learn libraryNo of pages: 101. What is machine learning, offline and online processes2. Supervised/unsupervised methods3. Overview of scikit learn library, APIs4. Dataset loading, generated datasets Chapter 6: Preparing Data for Machine LearningChapter Goal: Clean, vectorize and transform dataNo of Pages: 151. Type of data variables2. Vectorization3. Normalization4. Processing text and images Chapter 7: Supervised Learning Methods - 1Chapter Goal: Learn and implement classification and regression algorithmsNo of Pages: 301. Regression and classification, multiclass, multilabel classification2. K-nearest neighbors3. Linear regression, understanding parameters4. Logistic regression5. Decision trees Chapter 8: Tuning Supervised LearnersChapter Goal: Analyzing and improving the performance of supervised learning modelsNo of Pages: 201. Training methodology, evaluation methodology2. Hyperparameter tuning3. Regularization in linear regression4. Regularization in logistic regression5. Regularization in decision trees6. Crossvalidation, K-fold cross validation7. ROC Curve Chapter 9: Supervised Learning Methods - 2Chapter Goal: Learn more algorithmsNo of Pages: 151. Naive bayes2. Support vector machines3. Visualization of decision boundaries Chapter 10: Ensemble Learning MethodsChapter Goal: Learn the in-depth background of ensemble learning methodsNo of Pages: 101. Bagging vs boosting2. Random forest3. Adaboost4. Gradient boosting Chapter 11: Unsupervised Learning MethodsChapter Goal: Detailed theory and practically oriented introduction to dimensionality reduction and clustering algorithmsNo of Pages: 201. Dimensionality reduction2. Principle components analysis3. Clustering4. K-Means method5. Density-based method Chapter 12: Neural Networks and Pytorch BasicsChapter Goal: Understand the basics of neural networks, deep learning, and PytorchNo of Pages: 101. Introduction to Pytorch, tensors2. Tensor operations3. Exercises Chapter 13: Feedforward Neural NetworksChapter Goal: In-depth introduction to basic dense neural networks along with necessary mathematical background and implementation. (chapter might split into two while writing)No of Pages: 201. Perceptron model2. Neural network and activation functions3. Multiclass classification4. Cost functions and gradient descent5. Backpropagation6. Pytorch gradients7. Linear regression with PyTorch8. Basic dense network with PyTorch for regression9. Basic dense network with Pytorch for classification Chapter 14: Convolutional Neural NetworkChapter Goal: Explore details behind CNNs and implement two solutions for image classificationNo of Pages: 201. Dense network for digits classification2. Image filters and kernels3. Convolutional layers4. Pooling layers5. CNN for digits classification6. CNN for image classification Chapter 15: Recurrent Neural Network Chapter Goal: Understand sequence networks and implement them for forecasting values (or text classification)No of Pages: 151. Introduction to recurrent neural networks2. Vanishing gradient problem3. LSTM4. RNN batches, LSTM5. Text classification Problem (or forecasting problem) Chapter 16: Bringing It All TogetherChapter Goal: Discuss, conceptualize, design, and develop end to endNo of Pages: 201. Project 12. Project 2Ashwin Pajankar holds a Master of Technology from IIIT Hyderabad, and has over 25 years of programming experience. He started his journey in programming and electronics with BASIC programming language and is now proficient in Assembly programming, C, C++, Java, Shell Scripting, and Python. Other technical experience includes single board computers such as Raspberry Pi and Banana Pro, and Arduino. He is currently a freelance online instructor teaching programming bootcamps to more than 60,000 students from tech companies and colleges. His Youtube channel has an audience of 10000 subscribers and he has published more than 15 books on programming and electronics with many international publications.
Aditya Joshi has worked in data science and machine learning engineering roles since the completion of his MS (By Research) from IIIT Hyderabad. He has conducted tutorials, workshops, invited lectures, and full courses for students and professionals who want to move to the field of data science. His past academic research publications include works on natural language processing, specifically fine grain sentiment analysis and code mixed text. He has been the organizing committee member and program committee member of academic conferences on data science and natural language processing.

Caractéristiques techniques

  PAPIER
Éditeur(s) Apress
Auteur(s) Ashwin / Joshi Pajankar
Parution 05/03/2022
Nb. de pages 335
EAN13 9781484279205

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