Pro Machine Learning Algorithms , 1st ed. A Hands-On Approach to Implementing Algorithms in Python and R
Langue : Anglais
Auteur : Ayyadevara V Kishore
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.
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 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 concepts
No of pages: 25
Sub - Topics
1. Introduction to regression
2. Least squared error
3. Implementing linear regression in Excel & R & Python
4. Measuring error
Chapter 3: Logistic regression
Chapter Goal: Help the reader master logistic regression with the theory & practical concepts
No of pages: 25
Sub - 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 tree
Chapter Goal: Help the reader master decision tree with the theory & practical concepts
No of pages: 40
Sub - 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 error
Chapter 5: Random forest
Chapter Goal: Help the reader master random forests with the theory & practical concepts
No of pages: 15
Sub - Topics:
1. Moving from decision tree to random forests
2. Implement random forest in R & Python using decision tree functionalities
Chapter 6: GBM
Chapter Goal: Help the reader master GBM with the theory & practical concepts
No of pages: 20
Sub - Topics:
1. Understanding gradient boosting process
2. Difference between gradient boost & adaboost
3. Implement GBM in R & Python using decision tree functionalities
Chapter 7: Neural network
Chapter Goal: Help the reader master neural network with the theory & practical concepts
No of pages: 30
Sub - 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 network
Chapter Goal: Help the reader master CNN with the theory & practical concepts
No of pages: 30
Sub - Topics:
1. Moving from NN to CNN
2. Key parameters within CNN
3. Implement CNN in Excel & Python
Chapter 9: RNN
Chapter Goal: Help the reader master RNN with the theory & practical concepts
No of pages: 25
Sub - Topics:
1. Need for RNN
2. Key variations of RNN
3. Implementing RNN in Excel & Python
Chapter 10: word2vec
Chapter Goal: Help the reader master word2vec with the theory & practical concepts
No of pages: 20
1. Need for word2vec
2. Implementing word2vec in Excel & Python
Chapter 11: Unsupervised learning - clustering
Chapter Goal: Help the reader master clustering with the theory & practical concepts
No of pages: 15
Sub - Topics:
1. k-Means clustering
2. Hierarchical clustering
3. Implement clustering in Excel, R & Python
Chapter 12: PCA
Chapter Goal: Help the reader master PCA with the theory & practical concepts
No of pages: 15
Sub - Topics:
1. Dimensionality reduction using PCA
2. Implement PCA in Excel, R & Python
Chapter 13: Recommender systems
Chapter Goal: Help the reader master recommender systems with the theory & practical concepts
No of pages: 25
Sub - 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 cloud
Chapter Goal: Help the reader understand the ways to implement algorithms in the cloud
No of pages: 30
Sub - Topics:
1. Implementing machine learning algorithms in AWS
2. Implementing machine learning algorithms in Azure
3. 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.
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
Date de parution : 07-2018
Ouvrage de 372 p.
17.8x25.4 cm
Thèmes de Pro Machine Learning Algorithms :
Mots-clés :
Machine Learning; Python; R; Linear regression; Logistic regression; Decision tree; Neural Network
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