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

**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.

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

**Python Open-source library revolution**

The open-source libraries of Python have revolutionized the field last

This book also covers the

**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

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 b eginners **

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

**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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