Machine learning in data science using Python
Material type:
TextPublication details: Dreamtech Publisher New Delhi 2022Description: xxx, 926 pISBN: - 9789391540463
- 006.31 RAO
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|---|
Book
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Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 006.31 RAO (Browse shelf(Opens below)) | 1 | Available | 004868 |
Table of content
Part 1: Python for Machine Learning and Data Science
Chapter 1: Fundamentals of Python
Chapter 2: Datatypes in Python 19
Chapter 3: Operators in Python
Chapter 4: Input and Output
Chapter 5: Control Statements
Chapter 6: Numpy Arrays
Chapter 7: Functions in Python
Chapter 8: Modules, Packages and Libraries
Chapter 9: Introduction to OOPS
Chapter 10: Classes, Objects and Methods
Chapter 11: Data Storage in Files
Chapter 12: Data Analysis Using Pandas
Chapter 13 Advanced Data Analysis using Pandas
Chapter 14: Data Visualization using Matplotlib
Chapter 15: Data Visualization using Seaborn
Part 2: Machine Learning in Data Science 747
Chapter 16: Introduction to Machine Learning
Chapter 17: Exploratory Data Analysis (EDA)
Chapter 18: Outliers
Chapter 19: Simple Linear Regression
Chapter 20: Multiple Linear Regression
Chapter 21: One Hot Encoding
Chapter 22: Polynomial Linear Regression
Chapter 23: Ridge Regression
Chapter 24: Lasso Regression
Chapter 25: Elasticnet Regression
Chapter 26: Logistic Regression
Chapter 27: Support Vector Machine (SVM)
Chapter 28: Naive Bayes Classification
Chapter 29: KNN Classifier
Chapter 30: Decision Trees
Chapter 31: Random Forest
Chapter 32: K-Means Clustering
Chapter 33: Apriori Algorithm
Chapter 34: Principal Component Analysis (PCA)
Chapter 35: K-Fold Cross Validation
Chapter 36: Model Selection
Part 3: Deep Learning and AI in Data Science
Chapter 37: Introduction to Deep Learning
Chapter 38: Creating Neural Networks in Python
Chapter 39: Tensorflow and Keras
Chapter 40: Creating ANN Using Tensorflow and Keras
Chapter 41: Convolutional Neural Network (CNN)
Chapter 42: Recurrent Neural Network (RNN)
Chapter 43: Natural Language Processing (NLP)
Chapter 44: Computer Vision
This book is useful for students and IT professionals who want to make their career in the field of Machine Learning and Data Science.
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