000			03074nam a22001577a 4500
020			_a9788126579907
082			_a006.31 _bPRA-M
100			_aPradhan, Manaranjan & Kumar, U Dinesh
245			_aMachine learning using Python _cBy Manaranjan Pradhan & U Dinesh Kumar.
250			_a1
260			_aNew Delhi: _bWiley, _cc2019.
300			_ai-xx+343P.
505			_a 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
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.
942			_cBK
999			_c297576 _d297576