4 Books with Jupyter Notebooks for Data Science

The emergence of open-source programming languages and toolsets in the recent times have introduced techniques to implement machine learning algorithms leveraging number of programming languages applying statistical classification and statistical inference on Jupyter notebooks. It’s critical for the beginners in the field of data science to get acquainted with supervised learning and unsupervised learning, representation of data through several data engineering and data science techniques. In supervised learning, machine learning can solve problems, taking the pairs of inputs and producing the desired outputs without the intervention of the human. The convergence of computer science, machine learning, and statistics can solve many problems. A number of solutions can be offered with supervised learning such as classification of email as SPAM, identifying the ZIP code from the handwritten digits on an envelope. Identifying the anomalies in credit card transactions and detecting the fraudulent activity. Customer segmentation of book readers, detecting the abnormal access such as phishing, ransomware, and any other anomalies that occur while accessing the websites.

Introduction to machine learning with Python

The book introduction to machine learning will offer a number of machine learning algorithms with an objective to solve practical problems. Scikit-learn machine learning library has been implemented in this book for the Python programming language to solve most of the supervised and unsupervised machine learning problems. The book covers the steps needed for pre-processing the data, identifying the machine learning problems and solutions, selection of machine learning algorithms, and applying appropriate algorithm parameters.

Introduction to Machine Learning with Python

Though, the book does not cover the mathematical details of the machine learning algorithms, also not large on the mathematical formulas on this book. There’s not a lot of pre-requisites needed for probability theory in statistics or linear algebra. This book is available for purchase on Amazon This book primarily focuses on leveraging large number of existing machine learning models built on scikit-learn and few other libraries. This book also covers Gaussian processes covering probabilistic models that are complex in nature.

Python Open-source library revolution

The open-source libraries of Python have revolutionized the field last year, such as Pipenv, Pytest, Poetry, Loguru, Faust, Pampy, Pyre-check, Delorean, Cirq, Python-nubia, Requests-HTML, Bokeh, Vibora, Pywebview 2.0, Whatwaf, Molten, Teamtosvg, Termgraph, Algojammer, Bowler, Py-spy, Birdseye, Ice cream, Transcrypt, Pyodide, Botflow, Fast-pandas, Chart, Chartify, and Hypertools. A number of corporations were able to implement these open-source libraries including students and beginners to the data science field. However, libraries such as scikit-learn have several github repositories where the machine learning algorithms code is available. Scikit-learn depends primarily on NumPy and SciPy libraries that can run in Jupyter environment coupled displaying the plots through matplotlib libbrary.

This book also covers the implementation of classification and regression, generalization, overfitting, and underfitting, supervised machine learning algorithms, k-Nearest neighbor, linear regression, Naive Bayes classifiers, decision trees, ensembles of decision trees, kernelled support vector machines, neural networks for deep learning, unsupervised learning with clustering, and dimensionality reduction. It would benefit the beginners hugely who want to get started on fundamental supervised and unsupervised machine learning algorithms. If they’re looking for reinforcement learning environments or coverage on the topic of RL, this is not the book. The code can be found at github repository.

The elements of statistical learning

In order to understand the mathematics behind the machine learning algorithms in greater detail, the book, the elements of statistical learning is recommended. It’s a free book that can be downloaded as a PDF at Stanford website.

Elements of statistical learning

This books covers the introduction to statistics, supervised learning, linear methods for regression (LAR algorithm and generalizations of the lasso), linear methods for regression, logistic regression, basis expansions and regularizations, kernel smoothing methods, model assessment and selection, cross-validation, model inference and averaging, additive models, decision trees, boosting and additive trees, neural networks, bayesian neural networks, support vector machines and flexible discriminants, prototype methods and nearest-neighbors, unsupervised learning, random forests, ensemble learning, undirected graphical models, and high-dimensional problems.

Make your own neural network: An In-depth visual introduction for beginners 

For beginners, who would like to understand the mathematics behind the neural networks, can refer to the book make your own neural network: An in-depth visual introduction for beginners.

This book introduces neural networks to the beginners for implementations through Python and TensorFlow. Especially, beginners who have not implemented complex neural networks who want to gain a deeper understanding of how to build a simple neural network from scratch, this book helps. The step-by-step examples in Python in this book helps the beginners to embark upon a journey from being a citizen data scientist, or a student by harnessing the transformative power of data science and neural networks. The 20th century and 21st century have brought the neural networks into every corporation and all the products. The rise of the public datasets and seismic increase of neural networks market trajectory shows the rise will be significant 249% in the next few years for the enterprise adoption. In the wake of such unprecedented demand in the next few years. The brains are wired to think and learn a process by trial and error. Similarly, the neural networks run through trial and error technique with mathematics regardless if the neural network is implemented through surpervised, unsupervised, or semi-supervised. The book dives into the handful of mathematical functions that play the major role with the trial and error method. The forward propagation, backward propagation, calculating the total error from a neural network, calculation of the gradients, updating the weights of the neural network are all part of hands-on examples provided in this book with eight chapters. The book is available at Amazon.

Hands-on machine learning with Scikit-Learn, Keras & Tensorflow

This book hands-on machine learning with scikit-learn, Keras, and Tensorflow is more geared towards hands-on approach that makes an assumption that the reader knows nothing closer to implementing a machine learning algorithm. The book covers simple linear regression algorithm to complex neural networks with the aid of scikit-learn and Tensorflow libraries of Python. However, the book makes the assumption that the reader has some experience or exposure to Python programming and familiarity with the libraries such as NumPy, Pandas, and Matplotlib.

The book can be primarily be broken down to few parts such as the fundamentals of machine learning covering various classification of problems and solutions by creating a machine learning project and learning by fitting data models, pre-processing of the data, data cleansing, data loading, data engineering features, tuning the hyperparmeters through cross-validation, reducing the dimensionality of the training the data to battle the curse of the dimensionality, and a number of commonly known and implemented algorithms such as linear and polynomial regression, clustering algorithms, support vector machines, k-Nearest Neighbors, random forests, ensemble methods, and decision trees heavily leveraging the skit-learn library. The other section of the book deals with neural networks and deep learning with TensorFlow and Keras implementation. These chapters cover the long short-term memory (LSTM) nets, auto encoders, recurrent neural networks, feedforward neural networks, with large datasets and some introduction to reinforcement learning. However, I would not recommend this book for reinforcement learning. The book is available at Amazon.

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