- Manipulate data in R (filter and sort data sets, recode and compute variables)
- Compute statistical indicators (mean, median, mode etc.)
- Determine skewness and kurtosis
- Get statistical indicators by subgroups of the population
- Create histograms and cumulative frequency charts
- Build column charts, mean plot charts and scatterplot charts
- Detect the outliers in a data series
- Perform univariate analyses (one-sample t test, binomial test, chi-square test for goodness-of-fit)

If you want to learn how to perform the basic statistical analyses in the R program, you have come to the right place.

Now you don’t have to scour the web endlessly in order to find how to compute the statistical indicators in R, how to build a cross-table, how to build a scatterplot chart or how to compute a simple statistical test like the one-sample t test. Everything is here, in this course, explained visually, step by step.

So, what will you learn in this course?

First of all, you will learn how to manipulate data in R, to prepare it for the analysis: how to filter your data frame, how to recode variables and compute new variables.

Afterwards, we will take care about computing the main statistical figures in R: mean, median, standard deviation, skewness, kurtosis etc., both in the whole population and in subgroups of the population.

Then you will learn how to visualize data using tables and charts. So we will build tables and cross-tables, as well as histograms, cumulative frequency charts, column and mean plot charts, scatterplot charts and boxplot charts.

Since assumption checking is a very important part of any statistical analysis, we could not elude this topic. So we’ll learn how to check for normality and for the presence of outliers.

Finally, we will perform some basic, one-sample statistical tests and interpret the results. I’m talking about the one-sample t test, the binomial test and the chi-square test for goodness-of-fit.

So after graduating this course, you will know how to perform the essential statistical procedures in the R program. So… enroll today!

Now you don’t have to scour the web endlessly in order to find how to compute the statistical indicators in R, how to build a cross-table, how to build a scatterplot chart or how to compute a simple statistical test like the one-sample t test. Everything is here, in this course, explained visually, step by step.

So, what will you learn in this course?

First of all, you will learn how to manipulate data in R, to prepare it for the analysis: how to filter your data frame, how to recode variables and compute new variables.

Afterwards, we will take care about computing the main statistical figures in R: mean, median, standard deviation, skewness, kurtosis etc., both in the whole population and in subgroups of the population.

Then you will learn how to visualize data using tables and charts. So we will build tables and cross-tables, as well as histograms, cumulative frequency charts, column and mean plot charts, scatterplot charts and boxplot charts.

Since assumption checking is a very important part of any statistical analysis, we could not elude this topic. So we’ll learn how to check for normality and for the presence of outliers.

Finally, we will perform some basic, one-sample statistical tests and interpret the results. I’m talking about the one-sample t test, the binomial test and the chi-square test for goodness-of-fit.

So after graduating this course, you will know how to perform the essential statistical procedures in the R program. So… enroll today!

Statistics with R - Beginner Level

- Use R on stock market data for insight and ideas
- Apply basic technical analysis on stock market data
- Plot great looking financial charts
- Gain additional insights by comparing similar stocks

What this course will cover:

- Easily access free, stock-market data using R and the quantmod package
- Build great looking stock charts with quantmod
- Use R to manipulate time-series data
- Create a moving average from scratch
- Access technical indicators with the TTR package
- Create a simple trading systems by shifting time series using the binhf package
- A look at trend-following trading systems using moving averages
- A look at counter-trend trading systems using moving averages
- Using more sophisticated indicators (ROC, RSI, CCI, VWAP, Chaikin Volatility)
- Grouping stocks by theme to better understand them
- Finding coupling and decoupling stocks within an index

Analyzing Stock Market Data with R :Practical Data Science

- How Google does Machine Learning
- Launching into Machine Learning
- Intro to TensorFlow
- Feature Engineering
- Art and Science of Machine Learning

What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.

Machine Learning with TensorFlow on Google Cloud Platform

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:

With this book, you’ll learn:

- Fundamental concepts and applications of machine learning
- Advantages and shortcomings of widely used machine learning algorithms
- How to represent data processed by machine learning, including which data aspects to focus on
- Advanced methods for model evaluation and parameter tuning
- The concept of pipelines for chaining models and encapsulating your workflow
- Methods for working with text data, including text-specific processing techniques
- Suggestions for improving your machine learning and data science skills

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics.

Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.

You’ll find recipes for:

Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.

You’ll find recipes for:

- Vectors, matrices, and arrays
- Handling numerical and categorical data, text, images, and dates and times
- Dimensionality reduction using feature extraction or feature selection
- Model evaluation and selection
- Linear and logical regression, trees and forests, and k-nearest neighbors
- Support vector machines (SVM), naïve Bayes, clustering, and neural networks
- Saving and loading trained models

- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Semi-supervised Learning Algorithms
- Reinforcement Learning Algorithms
- Overfitting and underfitting
- correctness
- The Bias-Variance Trade-off
- Feature Extraction and Selection
- A Regression Example: Predicting Boston Housing Prices
- Import Libraries:
- How to forecast and Predict
- Popular Classification Algorithms
- Introduction to K Nearest Neighbors
- Introduction to Support Vector Machine
- Example of Clustering
- Running K-means with Scikit-Learn
- Introduction to Deep Learning using TensorFlow
- Deep Learning Compared to Other Machine Learning Approaches
- Applications of Deep Learning
- How to run the Neural Network using TensorFlow
- Cases of Study with Real Data
- Sources & References

This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks.

- Realize different classification and regression techniques
- Understand the concept of clustering and how to use it to automatically segment data
- See how to build an intelligent recommender system
- Understand logic programming and how to use it
- Build automatic speech recognition systems
- Understand the basics of heuristic search and genetic programming
- Develop games using Artificial Intelligence
- Learn how reinforcement learning works
- Discover how to build intelligent applications centered on images, text, and time series data
- See how to use deep learning algorithms and build applications based on it

You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own.

By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop your business vision.

**What you will learn**

By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop your business vision.

- Use adaptive thinking to solve real-life AI case studies
- Rise beyond being a modern-day factory code worker
- Acquire advanced AI, machine learning, and deep learning designing skills
- Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology
- Understand future AI solutions and adapt quickly to them
- Develop out-of-the-box thinking to face any challenge the market presents

By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

]]>- Explore the machine learning landscape, particularly neural nets
- Use scikit-learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the TensorFlow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets
- Apply practical code examples without acquiring excessive machine learning theory or algorithm details