Best Machine learning books

        

                  Machine learning and artificial intelligence are growing fields and growing topics of study.  In this article, we’ll review some of the most popular resources for machine learning beginners. Some of these books will require familiarity with some coding languages and math.

1. Machine Learning For Dummies” by John Paul Mueller and Luca Massaron

Topics Covered :

• Introducing How Machines Learn : the Real Story about AI, Learning in the Age of Big Data, Glance at the Future

• Preparing Your Learning Tools : Installing an R, Python , Coding in R Using RStudio, Python Using Anaconda and exploring other machine learning tools

• Getting Started with the Math Basics : Math Behind Machine Learning, Probabilities, Statistics,Cost Functions, Error Curve , Greedy Classification Trees, Incredible Perceptron, Greedy Classification Trees. Validating Machine Learning : Training, Validating, and Testing

• Learning from Smart and Big Data : Preprocessing Data, Leveraging Similarity , Working with Linear Models, Neural Networks, Support Vector Machines

• Applying Learning to Real Problems : Classifying Images, Scoring Opinions and Sentiments, Recommending Products and Movies

Where to buy: Amazon

2. Programming Collective Intelligence” by Toby Segaran

Topics Covered :

• Introduction to Collective Intelligence, Making Recommendations

• Discovering Groups : Supervised versus Unsupervised Learning, Word Vectors, Hierarchical Clustering, K-Means Clustering, Viewing Data in Two Dimensions

• Searching and Ranking, Optimization

• Document Filtering : Filtering Spam, A Naïve Classifier, The Fisher Method, Modeling with Decision Trees

• Building Price Models :k-Nearest Neighbors ,Weighted Neighbors

• Advanced Classification: Understanding Kernel Methods and SVMs , LIBSVM , Finding Independent Features

• Algorithm Summary : Bayesian Classifier, Decision Tree Classifier, Neural Networks, Support-Vector Machines, k-Nearest Neighbors, Clustering, Multidimensional Scaling, Non-Negative Matrix Factorization, Optimization

• Different third party libraries and Mathematical Formulas

Where to buy: Amazon

3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Topics Covered :

• What Machine Learning is, what problems it tries to solve, and the main categories and fundamental concepts of its systems, Optimizing a cost function, Handling, cleaning, and preparing data, Selecting a model, The challenges of Machine Learning

• The most common learning algorithms: Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods

• unsupervised learning techniques, including clustering, density estimation, and anomaly detection

• What neural nets are and what they’re good for, Building and training neural nets using TensorFlow and Keras

• Neural net architectures: feedforward neural nets for tabular data, convolutional nets for computer vision, recurrent nets and long short-term memory (LSTM) nets for sequence processing, encoder/decoders and Transformers for natural language processing, autoencoders and generative adversarial networks (GANs) for generative learning

• Techniques for training deep neural nets, Training and deploying TensorFlow models at scale 

Where to buy: Amazon

4. Natural Language Processing with Python

Topics Covered :

• Natural Language Processing with Python and spaCy will show you how to create NLP applications like chatbots, text-condensing scripts, and order-processing tools quickly and easily

• Using Python for Natural Language Processing

• The spaCy Library, How Can Computers Understand Language?, Using Machine Learning for Natural Language Processing

• Neural Network Models, Convolutional Neural Networks for NLP,

• Extracting and Generating Text with Part-of-Speech Tags,, Finding Patterns Based on Linguistic Features

• Implementing and Deploying a Chatbot Works

Where to buy: Amazon

5. Machine Learning in Action

Topics Covered :

• Machine learning basics, How to choose the right algorithm, python, NumPy library, Classifying with k-Nearest Neighbors

• Decision trees, probability theory: naïve Bayes

• Logistic regression, Support vector machines, AdaBoost meta-algorithm, linear regression

• Tree-based regression, Unsupervised learning

• Big data and MapReduce Hadoop Streaming, Machine learning in MapReduce, The Pegasos algorithm

Where to buy: Amazon

6. Machine Learning with TensorFlow

Topics Covered :

• Machine-learning fundamentals, Types of learning : Supervised learning, Unsupervised learning, Reinforcement learning, TensorFlow essentials

• Core learning algorithms Linear regression,Polynomial model, Regularization, Using logistic regression, Multiclass classifier

• Automatically clustering data : K-means clustering, Clustering using a self-organizing map

• Hidden Markov models, The neural network paradigm : Reinforcement learning, Convolutional neural networks, Recurrent neural networks

• Sequence-to-sequence models for chatbots

Where to buy: Amazon

7. Introduction to Machine Learning with Python: A Guide for Data Scientists

Topics Covered :

• Scikit-learn, Jupyter Notebook, NumPy, SciPy, matplotlib, pandas, mglearn

• Supervised Machine Learning Algorithms : k-Nearest Neighbors, Linear Models, Naive Bayes Classifiers, Decision Trees, Kernelized Support Vector Machines, Neural Networks (Deep Learning)

• Unsupervised Learning and Preprocessing :  Dimensionality Reduction, Feature Extraction, and Manifold Learning, Clustering

• Representing Data and Engineering Features, Model Evaluation and Improvement, Algorithm Chains and Pipelines, Working with Text Data

Where to buy: Amazon

Did you find our blog helpful ? Share in comment section. Feel free to share on Twitter or Facebook by using the share buttons