Pro Machine Learning Algorithms
Apress (Verlag)
978-1-4842-3563-8 (ISBN)
- Exposes readers to running a large-scale model in a cloud environment
- Covers all major machine learning algorithms with theory along with case studies including the vast majority of algorithms used in industry
- Algorithm models are implemented both 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.
In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R.
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.
- 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
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.
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.
Chapter 1: Basic statistics
Chapter Goal: Build the statistical foundation for machine learning No of pages : 20
Sub -Topics
1. Introduction to various statistical functions
1. Introduction to distributions
2. Hypothesis testing
3. 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 error
3. Implementing linear regression in Excel & R & Python
4. 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 error
3. Implementing logistic regression in Excel & R & Python
4. 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 gain
3. Decision tree for classification & regression
4. Implementing decision tree in Excel & R & Python
5. 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 forests
2. Implement random forest in R & Python using decision tree functionalities Chapter
6: GBMChapter Goal: Help the reader master GB
M with the theory & practical concepts No of pages: 20Sub - Topics:
1. Understanding gradient boosting process
2. Difference between gradient boost & adaboost
3. Implement GB
M 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 propagation
2. Backward propagation
3. Impact of epochs and learning rate
4. 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 CNN
2. Key parameters within CNN
3. 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 RNN
2. Key variations of RNN
3. Implementing RNN in Excel & Python Chapter
10: word2vecChapter Goal: Help the reader master word2vec with the theory & practical conceptsNo of pages:
201. Need for word2vec
2. 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 clustering
2. Hierarchical clustering
3. 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 PCA
2. 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 filtering
2. Item based collaborative filtering
3. Matrix factorization
4. 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 AWS
2. Implementing machine learning algorithms in Azure
3. Implementing machine learning algorithms in GCP
| Erscheinungsdatum | 21.07.2018 |
|---|---|
| Zusatzinfo | 467 Illustrations, black and white |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 7409 g |
| Einbandart | kartoniert |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Programmiersprachen / -werkzeuge ► Python | |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Wirtschaft ► Betriebswirtschaft / Management ► Wirtschaftsinformatik | |
| Schlagworte | Decision Tree • linear regression • Logistic Regression • machine learning • neural network • Python • R |
| ISBN-10 | 1-4842-3563-0 / 1484235630 |
| ISBN-13 | 978-1-4842-3563-8 / 9781484235638 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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