Machine Learning in Data Science Using Python (Paperback)

This book is useful for students and IT professionals who want to make their career in the field of Machine Learning and Data Science.Table of Content:Part 1: Python for Machine Learning and Data ScienceChapter 1: Fundamentals of PythonChapter 2: Datatypes in Python 19Chapter 3: Operators in PythonChapter 4: Input and OutputChapter 5: Control StatementsChapter 6: Numpy ArraysChapter 7: Functions in PythonChapter 8: Modules, Packages and LibrariesChapter 9: Introduction to OOPSChapter 10: Classes, Objects and MethodsChapter 11: Data Storage in FilesChapter 12: Data Analysis Using PandasChapter 13 Advanced Data Analysis using PandasChapter 14: Data Visualization using MatplotlibChapter 15: Data Visualization using Seaborn Part 2: Machine Learning in Data Science 747Chapter 16: Introduction to Machine LearningChapter 17: Exploratory Data Analysis (EDA)Chapter 18: OutliersChapter 19: Simple Linear RegressionChapter 20: Multiple Linear RegressionChapter 21: One Hot EncodingChapter 22: Polynomial Linear RegressionChapter 23: Ridge RegressionChapter 24: Lasso RegressionChapter 25: Elasticnet RegressionChapter 26: Logistic RegressionChapter 27: Support Vector Machine (SVM)Chapter 28: Naive Bayes ClassificationChapter 29: KNN ClassifierChapter 30: Decision TreesChapter 31: Random ForestChapter 32: K-Means ClusteringChapter 33: Apriori AlgorithmChapter 34: Principal Component Analysis (PCA)Chapter 35: K-Fold Cross ValidationChapter 36: Model Selection Part 3: Deep Learning and AI in Data ScienceChapter 37: Introduction to Deep LearningChapter 38: Creating Neural Networks in PythonChapter 39: Tensorflow and KerasChapter 40: Creating ANN Using Tensorflow and KerasChapter 41: Convolutional Neural Network (CNN)Chapter 42: Recurrent Neural Network (RNN)Chapter 43: Natural Language Processing (NLP)Chapter 44: Computer Vision Program Index