Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R

V Kishore Ayyadevara

買這商品的人也買了...

相關主題

商品描述

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.
 

 

商品描述(中文翻譯)

橋接高層次對演算法運作方式的理解與了解調整模型的細節,這本書將讓您在開發主要機器學習模型時獲得信心和技能。在《Pro Machine Learning Algorithms》中,您將首先在Excel中開發演算法,以便實際了解模型中可以調整的所有操縱桿,然後再在Python/R中實現這些模型。

您將涵蓋所有主要的演算法:監督式和非監督式學習,包括線性/邏輯回歸、k-means聚類、主成分分析(PCA)、推薦系統、決策樹、隨機森林、GBM和神經網絡。您還將通過CNN、RNN和word2vec進行深度學習,用於文本挖掘。您將學習不僅是演算法,還有特徵工程的概念,以最大化模型的性能。您將看到理論和案例研究,例如情感分類、詐騙檢測、推薦系統和圖像識別,以獲得工業界使用的絕大多數機器學習演算法的理論和實踐的最佳結合。除了學習演算法外,您還將了解在所有主要雲服務提供商上運行機器學習模型的方法。

您需要具備最基本的統計/軟體程式設計知識,並在閱讀本書結束時能夠自信地參與機器學習項目。

您將學到什麼:
- 深入了解所有主要的機器學習和深度學習演算法
- 充分了解在建立模型時應避免的陷阱
- 在雲端中實現機器學習演算法
- 通過每個演算法的案例研究進行實踐
- 掌握集成學習的技巧,以建立更準確的模型
- 探索R/Python編程和Keras深度學習框架的基礎知識

本書適合對象:
- 想轉職為數據科學家的業務分析師/IT專業人士
- 希望鞏固機器學習知識的數據科學家