Ebooks Data Analytics I: Statistics and Machine Learning in Python with Examples and Applications (Series 1)
Description Data Analytics I: Statistics and Machine Learning in Python with Examples and Applications (Series 1)
Data Analytics, Statistics and Machine Learning in Python with many Examples and Real World Applications for Financial Data Analytics, Text and Image Processing, Stock Return Prediction with News, House Price Modeling, Face Recognition and more Series 1 contains: Fundamentals of Data - Exploratory Data Analytics - Equality and Performance Measurement - Text and Image Processing - Stochastic and Probability Foundations - Statistical Learning - Parameter Estimation - Statistical Tests - Bayesian Statistics - Regression - Regression Diagnostics - Analysis of Variance (ANOVA) - Generalized Linear ModelsSeries 2 contains: Machine Learning Principles – Workflow – Feature Engineering - Learning, Validation and Prediction – Under- and Overfit – Train-Test-Split - Crossvalidation – Hyperparameter tuning - Supervised Machine Learning for Regression and Classification– K-Nearest Neighbors – Decision Trees – Bootstrapping – Bagging – Random Forests - Boosting – Neural Networks – Unsupervised Learning – Clustering – Principal Components Analysis - Fallacies
Data Analytics I: Statistics and Machine Learning in Python with Examples and Applications (Series 1) PDF ePub
Top 13 Python Libraries / Python Libraries For Data science ~ In this article, we discussed 13 libraries that will help you achieve your data science goals like maths, data mining, data exploration, and visualization, machine learning. From a data science perspective, you get to master all of these libraries and many more as part of Analytics Vidhya’s AI and ML Blackbelt+ program .
Advanced Data Analytics Using Python - With Machine ~ You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis.
Machine Learning For Dummies®, IBM Limited Edition ~ Descriptive analytics . The Roles of Statistics and Data Mining with Machine Learning... 11 Putting Machine Learning in Context ... 12 Approaches to . About This Book Machine Learning For Dummies, IBM Limited Edition, gives you
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Statistics with Python / Coursera ~ Offered by University of Michigan. This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization.
Machine Learning in Python (Data Science and Deep Learning) ~ This comprehensive machine learning tutorial includes over 100 lectures spanning 14 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at and IMDb to guide you through what matters, and what doesn’t.
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Python for Analytics - Statistics ~ Data scientists, statisticians, software engineers who need to use Python for data analytics, including web scraping, pulling data, data cleaning, data prep and data analysis. Instructors Dr. David Masad
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Statistics for Machine Learning (7-Day Mini-Course) ~ Statistics for Machine Learning Crash Course. Get on top of the statistics used in machine learning in 7 Days. Statistics is a field of mathematics that is universally agreed to be a prerequisite for a deeper understanding of machine learning. Although statistics is a large field with many esoteric theories and findings, the nuts and bolts tools and notations taken from the field
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How to Learn Statistics for Data Science, The Self-Starter Way ~ After completing these 3 steps, you'll be ready to attack more difficult machine learning problems and common real-world applications of data science. Step 1: Core Statistics Concepts. To know how to learn statistics for data science, it's helpful to start by looking at how it will be used.
Predictive Analytics using Python MicroMasters® Program / edX ~ This MicroMasters program is designed for data analysts and data scientists and will teach you how to prepare data for predictive modelling, data mining, and advanced analytics using a range of statistical and machine learning methodologies on real-life datasets.
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Statistics and Machine Learning Toolbox - MATLAB ~ Statistics and Machine Learning Toolboxâ„¢ provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis; fit probability distributions to data; generate random numbers for Monte Carlo simulations, and perform hypothesis tests.
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10 Free Data Science Books You Must Read in 2019 / by ~ There are so many great resources out there to learn data science and analysis for free. Over the last year I have read quite a few data science books and I wanted to share some of the best here.
Data Mining for Business Analytics: Concepts, Techniques ~ Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration. Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.
Python Machine Learning - W3Schools ~ Ordinal data are like categorical data, but can be measured up against each other. Example: school grades where A is better than B and so on. By knowing the data type of your data source, you will be able to know what technique to use when analyzing them. You will learn more about statistics and analyzing data in the next chapters.
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Learn Data Analysis - Free Curriculum / Springboard ~ The path is divided into three parts. In part 1, we learn general programming practices (software design, version control) and tools (Python, SQL, Unix, and Git). In part 2, we learn R and focus more narrowly on data analysis, studying statistical techniques, machine learning, and presentation of findings.
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