# Pro Machine Learning Algorithms

## A Hands-On Approach to Implementing Algorithms in Python and R

Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models
better. This book will give you the confidence and skills when developing all the major machine learning models. Les mer

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Paperback

Legg i
Vår pris:

**466,-**
(Paperback)
**Fri frakt!**

Leveringstid: Sendes innen 21 dager

You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers.

You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence.

What You Will Learn

Get an in-depth understanding of all the major machine learning and deep learning algorithms

Fully appreciate the pitfalls to avoid while building models

Implement machine learning algorithms in the cloud

Follow a hands-on approach through case studies for each algorithm

Gain the tricks of ensemble learning to build more accurate models

Discover the basics of programming in R/Python and the Keras framework for deep learning

Who This Book Is For

Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.

Chapter 1: Basic statisticsChapter Goal: Build the statistical foundation for machine learning No of pages : 20Sub -Topics1.
Introduction to various statistical functions1. Introduction to distributions2. Hypothesis testing3. Case classes

Chapter 2: Linear regression Chapter Goal: Help the reader master linear regression with the theory & practical conceptsNo of pages: 25Sub - Topics 1. Introduction to regression 2. Least squared error3. Implementing linear regression in Excel & R & Python4. Measuring error

Chapter 3: Logistic regressionChapter Goal: Help the reader master logistic regression with the theory & practical concepts No of pages: 25Sub - Topics: 1. Introduction to logistic regression 2. Cross entropy error3. Implementing logistic regression in Excel & R & Python4. Area under the curve calculation

Chapter 4: Decision treeChapter Goal: Help the reader master decision tree with the theory & practical concepts No of pages: 40Sub - Topics: 1. Introduction to decision tree 2. Information gain3. Decision tree for classification & regression4. Implementing decision tree in Excel & R & Python5. Measuring errorChapter 5: Random forestChapter Goal: Help the reader master random forests with the theory & practical concepts No of pages: 15Sub - Topics: 1. Moving from decision tree to random forests2. Implement random forest in R & Python using decision tree functionalities Chapter 6: GBMChapter Goal: Help the reader master GBM with the theory & practical concepts No of pages: 20Sub - Topics: 1. Understanding gradient boosting process2. Difference between gradient boost & adaboost3. Implement GBM in R & Python using decision tree functionalities Chapter 7: Neural networkChapter Goal: Help the reader master neural network with the theory & practical conceptsNo of pages: 30Sub - Topics: 1. Forward propagation2. Backward propagation3. Impact of epochs and learning rate4. Implement Neural network in Excel, R & Python Chapter 8: Convolutional neural networkChapter Goal: Help the reader master CNN with the theory & practical conceptsNo of pages: 30Sub - Topics: 1. Moving from NN to CNN2. Key parameters within CNN3. Implement CNN in Excel & Python

Chapter 9: RNNChapter Goal: Help the reader master RNN with the theory & practical conceptsNo of pages: 25Sub - Topics: 1. Need for RNN2. Key variations of RNN3. Implementing RNN in Excel & Python Chapter 10: word2vecChapter Goal: Help the reader master word2vec with the theory & practical conceptsNo of pages: 201. Need for word2vec2. Implementing word2vec in Excel & Python

Chapter 11: Unsupervised learning - clusteringChapter Goal: Help the reader master clustering with the theory & practical conceptsNo of pages: 15Sub - Topics: 1. k-Means clustering2. Hierarchical clustering3. Implement clustering in Excel, R & Python

Chapter 12: PCAChapter Goal: Help the reader master PCA with the theory & practical conceptsNo of pages: 15Sub - Topics: 1. Dimensionality reduction using PCA2. Implement PCA in Excel, R & Python

Chapter 13: Recommender systemsChapter Goal: Help the reader master recommender systems with the theory & practical conceptsNo of pages: 25Sub - Topics: 1. user based collaborative filtering2. Item based collaborative filtering3. Matrix factorization4. Implementing the above algorithms in Excel, R & Python

Chapter 14: Implement algorithms in the cloudChapter Goal: Help the reader understand the ways to implement algorithms in the cloudNo of pages: 30Sub - Topics: 1. Implementing machine learning algorithms in AWS2. Implementing machine learning algorithms in Azure3. Implementing machine learning algorithms in GCP

Chapter 2: Linear regression Chapter Goal: Help the reader master linear regression with the theory & practical conceptsNo of pages: 25Sub - Topics 1. Introduction to regression 2. Least squared error3. Implementing linear regression in Excel & R & Python4. Measuring error

Chapter 3: Logistic regressionChapter Goal: Help the reader master logistic regression with the theory & practical concepts No of pages: 25Sub - Topics: 1. Introduction to logistic regression 2. Cross entropy error3. Implementing logistic regression in Excel & R & Python4. Area under the curve calculation

Chapter 4: Decision treeChapter Goal: Help the reader master decision tree with the theory & practical concepts No of pages: 40Sub - Topics: 1. Introduction to decision tree 2. Information gain3. Decision tree for classification & regression4. Implementing decision tree in Excel & R & Python5. Measuring errorChapter 5: Random forestChapter Goal: Help the reader master random forests with the theory & practical concepts No of pages: 15Sub - Topics: 1. Moving from decision tree to random forests2. Implement random forest in R & Python using decision tree functionalities Chapter 6: GBMChapter Goal: Help the reader master GBM with the theory & practical concepts No of pages: 20Sub - Topics: 1. Understanding gradient boosting process2. Difference between gradient boost & adaboost3. Implement GBM in R & Python using decision tree functionalities Chapter 7: Neural networkChapter Goal: Help the reader master neural network with the theory & practical conceptsNo of pages: 30Sub - Topics: 1. Forward propagation2. Backward propagation3. Impact of epochs and learning rate4. Implement Neural network in Excel, R & Python Chapter 8: Convolutional neural networkChapter Goal: Help the reader master CNN with the theory & practical conceptsNo of pages: 30Sub - Topics: 1. Moving from NN to CNN2. Key parameters within CNN3. Implement CNN in Excel & Python

Chapter 9: RNNChapter Goal: Help the reader master RNN with the theory & practical conceptsNo of pages: 25Sub - Topics: 1. Need for RNN2. Key variations of RNN3. Implementing RNN in Excel & Python Chapter 10: word2vecChapter Goal: Help the reader master word2vec with the theory & practical conceptsNo of pages: 201. Need for word2vec2. Implementing word2vec in Excel & Python

Chapter 11: Unsupervised learning - clusteringChapter Goal: Help the reader master clustering with the theory & practical conceptsNo of pages: 15Sub - Topics: 1. k-Means clustering2. Hierarchical clustering3. Implement clustering in Excel, R & Python

Chapter 12: PCAChapter Goal: Help the reader master PCA with the theory & practical conceptsNo of pages: 15Sub - Topics: 1. Dimensionality reduction using PCA2. Implement PCA in Excel, R & Python

Chapter 13: Recommender systemsChapter Goal: Help the reader master recommender systems with the theory & practical conceptsNo of pages: 25Sub - Topics: 1. user based collaborative filtering2. Item based collaborative filtering3. Matrix factorization4. Implementing the above algorithms in Excel, R & Python

Chapter 14: Implement algorithms in the cloudChapter Goal: Help the reader understand the ways to implement algorithms in the cloudNo of pages: 30Sub - Topics: 1. Implementing machine learning algorithms in AWS2. Implementing machine learning algorithms in Azure3. Implementing machine learning algorithms in GCP

V Kishore Ayyadevara currently leads retail analytics consulting in a start-up. He received his MBA from IIM Calcutta. Following
that, he worked for American Express in risk management and in Amazon's supply chain analytics teams. He is passionate about
leveraging data to make informed decisions - faster and more accurately. Kishore's interests include identifying business
problems that can be solved using data, simplifying the complexity within data science and applying data science to achieve
quantifiable business results.