Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learningBuild strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide Key Features
This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. |
Contents
| 1 | |
| 6 | |
| 20 | |
Feature Selection and Feature Engineering | 44 |
Linear Regression | 72 |
Logistic Regression | 94 |
Naive Bayes | 120 |
Support Vector Machines | 133 |
Clustering Fundamentals | 181 |
Hierarchical Clustering | 208 |
Introduction to Recommendation Systems | 222 |
Introduction to Natural Language Processing | 242 |
Topic Modeling and Sentiment Analysis in NLP | 261 |
A Brief Introduction to Deep Learning and TensorFlow | 288 |
Creating a Machine Learning Architecture | 320 |
| 332 | |
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Common terms and phrases
accuracy algorithm approach binary chapter class_weight=None classifier collaborative filtering common complex computational concept consider corpus decision tree deep learning default value defined determine dimensionality distance distribution document dummy dataset elements example filtering following figure Gaussian going to discuss gradient graph grid search hyperplane import numpy input k-means kernel latent latent semantic analysis layers linear regression Log-Likelihood logistic regression loss function machine learning matrix methods metric minimize naive Bayes nb_samples NLTK non-linear normally number of clusters number of samples numpy numpy as np optimal output overfitting Packt parameter perceptron performances pipeline plot possible prediction probability problem Python random forest random_state=None result ROC curve scikit-learn provides SciPy score scoring='accuracy shown sklearn.datasets import sklearn.linear_model import sklearn.metrics import sklearn.model_selection import split step stochastic gradient descent strategy support vector machines support vectors techniques TensorFlow there's threshold tokens topic variables variance verbose=0 Y_test Y_train