000			03338nam a22002057a 4500
999			_c5010 _d5010
005			20230322110524.0
008			230322b ||||| |||| 00| 0 eng d
020			_a9788126579907
082			_a006.31 _bPRA
100			_aPradhan, Manaranjan _911459
245			_aMachine learning using python
260			_bWiley India Pvt. Ltd. _aNew Delhi _c2023
300			_axx, 343 p.
365			_aINR _b609.00
504			_aTable of content 1 Introduction To Machine Learning 1.1 Introduction to Analytics and Machine Learning 1.2 Why Machine Learning? 1.3 Framework for Developing Machine Learning Models 1.4 Why Python? 1.5 Python Stack for Data Science 1.6 Getting Started with Anaconda Platform 1.7 Introduction to Python 2 Descriptive Analytics 2.1 Working with DataFrames in Python 2.2 Handling Missing Values 2.3 Exploration of Data using Visualization 3 Probability Distributions And Hypothesis Tests 3.1 Overview 3.2 Probability Theory – Terminology 3.3 Random Variables 3.4 Binomial Distribution 3.5 Poisson Distribution 3.6 Exponential Distribution 3.7 Normal Distribution 3.7.5 Other Important Distributions 3.8 Central Limit Theorem 3.9 Hypothesis Test 3.10 Analysis of Variance (ANOVA) 4 Linear Regression 4.1 Simple Linear Regression 4.2 Steps in Building a Regression Model 4.3 Building Simple Linear Regression Model 4.4 Model Diagnostics 4.5 Multiple Linear Regression 5 Classification Problems 5.1 Classification Overview 5.2 Binary Logistic Regression 5.3 Credit Classification 5.4 Gain Chart and Lift Chart 5.5 Classification Tree (Decision Tree Learning) 6 Advanced Machine Learning 6.1 Overview 6.2 Gradient Descent Algorithm 6.3 Scikit-Learn Library for Machine Learning 6.4 Advanced Regression Models 6.5 Advanced Machine Learning Algorithms 7 Clustering 7.1 Overview 7.2 How Does Clustering Work? 7.3 K-Means Clustering 7.4 Creating Product Segments Using Clustering 7.5 Hierarchical Clustering 8 Forecasting 8.1 Forecasting Overview 8.2 Components of Time-Series Data 8.3 Moving Average 8.4 Decomposing Time Series 8.5 Auto-Regressive Integrated Moving Average Models 9 Recommender Systems 9.1 Overview 9.2 Association Rules (Association Rule Mining) 9.3 Collaborative Filtering 9.4 Using Surprise Library 9.5 Matrix Factorization 10 Text Analytics 10.1 Overview 10.2 Sentiment Classification 10.3 Naïve-Bayes Model for Sentiment Classification 10.4 Using TF-IDF Vectorizer 10.5 Challenges of Text Analytics Conclusion Exercises References Index
520			_aThis book is written to provide a strong foundation in Machine Learning using Python libraries by providing real-life case studies and examples. It covers topics such as Foundations of Machine Learning, Introduction to Python, Descriptive Analytics and Predictive Analytics. Advanced Machine Learning concepts such as decision tree learning, random forest, boosting, recommender systems, and text analytics are covered. The book takes a balanced approach between theoretical understanding and practical applications. All the topics include real-world examples and provide step-by-step approach on how to explore, build, evaluate, and optimize machine learning models.
650			_aMachine learning _92343
700			_aKumar, U. Dinesh _9836
942			_2ddc _cBK