Explore the advantages of the programming language R in this 8-video Skillsoft Aspire course. An essential skill for statistical computing and graphics, R is the tool of choice for data science professionals in every industry and field. It both creates reproducible high-quality analyses, and offers unparalleled graphic and charting capabilities. Learners will examine how to apply classification and clustering methods to data science problems by using R. Key concepts covered in this course include performing the preparatory steps needed to create a classification and decision tree; using the rpart library and ctree library to build a decision tree; and how to perform the preparatory steps needed to carry out clustering. Next, explore use of the k-means clustering method; using hierarchical clustering with the hclust and cutree methods; and applying a decision tree method to a classification problem. Finally, learn to train a decision tree classifier by using the data and a relationship inside of those data.

R is a programming language that is an essential skill for statistical computing and graphics. It is the tool of choice for data science professionals in every industry and field-not only to create reproducible high-quality analyses, but to take advantage of R's great graphic and charting capabilities. In this 11-video Skillsoft Aspire course, you will explore the fundamental data structures used in R, including working with vectors, lists, matrices, factors, and data frames. The key concepts in this course include: creating vectors in R and manipulating and performing operations on vectors in R; how to sort vectors in R; and how to use lists in R and explore example code line by line executing each line using the run current line command along the way. You will also examine creating matrices and performing matrix operations in R; creating factors and data frames in R; performing data frame operations in R; and how to create and use a data frame.

Continue exploring the advantageous aspects of the programming language R in this 8-video Skillsoft Aspire course. An essential skill for statistical computing and graphics, R has become the tool of choice for data science professionals in every industry and field. Learn how to create reproducible high-quality analyses, while taking advantage of R's great graphic and charting capabilities. Learners will explore how to use R to create plots and charts of data. Key concepts covered in this course include creating a scatter plot by using the built-in R method; creating a line graph on a time series data set; and creating a bar chart with the built-in R function bar plot. You will learn how to create a box and whisker plot by using the built in mtcars data set; to create a histogram with the built-in R function hist, and the equivalent by using the ggplot2 library functions; and how to create a bubble plot with the ggplot2 library. Finally, learn how to use an appropriate plot to visualize data.

Vectors are the easiest type of data structures in R. However, to use them successfully, it's important to appreciate their restrictions, recognize the types available, and identify their members - or components as they're officially called in R. This course shows you how to create and generate vectors using the c() and vector() functions, respectively. You'll perform vectorized operations on elements in vectors. Practice filtering and slicing vectors. And use the which(), any(), and all() functions on vectors. Furthermore, you'll perform naming and indexing operations on vectors and work with different length vectors using vector recycling. On completing this course, you'll have the knowledge and know-how to utilize vectors for their intended purpose.

The free and robust statistical package R has been decades in the making and is worth learning for serious statistical operations, such as conducting new medical data analysis. This course teaches you everything you need to know to get started with R, from installing R to running R from the command line. You'll grasp how to invoke basic functions and view the documentation on those. You'll create variables in R and explore various reserved words and the = and <- operators. You'll then perform basic arithmetic operations on variables, invoke built-in functions, and work with various atomic data types, such as character, integer, double, logical, complex, real, and raw. By the end of this course, you'll have the skills you need to get working with R.

The programming language has become an essential skill for statistical computing and graphics, the tool of choice for data science professionals in every industry and field. R creates reproducible high-quality analyses, and allows users to take advantage of its great graphic and charting capabilities. In this 8-video Skillsoft Aspire course, you will discover how to apply regression methods to data science problems by using R. Key concepts covered in this course include preparing a data set before creating a linear regression model how to create a linear regression model with the lm method in R; and extracting statistical results of a linear regression problem. You will also learn how to test the predict method on perform the preparatory steps needed to create a logistic model; and how to apply the generalized linear model (glm) method on a logistic regression problem. Finally, learners see how to create a linear regression model and use the predict method on a linear model.

Users marvel at a system's ability to recommend items they're likely to appreciate. As someone working with machine learning, implementing these recommendation systems (also called recommender systems) can dramatically increase user engagement and goodwill towards your products or brand. Use this course to comprehend the math behind recommendation systems and how to apply latent factor analysis to make recommendations to users. Examine the intuition behind recommender systems before investigating two of the main techniques used to build them: content-based filtering and collaborative filtering. Moving on, explore latent factor analysis by decomposing a ratings matrix into its latent factors using the gradient descent algorithm and implementing this technique to decompose a ratings matrix using the Python programming language. By the end of this course, you'll be able to build a recommendation system model that best suits your products and users.

This course explores how to select the appropriate algorithm for machine learning (ML), the principles of designing machine learning algorithms, and how to refactor machine ML code. In 11 videos, you will learn the steps involved in designing ML algorithms. The complexity in the algorithm is huge, and learners will observe how to write iterative and incremental code, and how to apply refactoring to it. This course next examines the types of ML problems, and classifies it into four categories, and how to classify machine learning algorithms. You will learn how to refactor existing ML code written in Python, and to launch and use PyCharm IDE. This course also demonstrates how to use PyCharm IDE on a specific project learners will create. You will examine the problems associated with technical debt in ML implementation, and how to manage it. Then you will learn to use SonarQube to build code coverage for machine learning code that are written in Python. Finally, this course examines automatic clone recommendations for refactoring, based on the present and the past.

Explore techniques of refactoring code, the process of changing a computer program source code without modifying its external functional behavior, in this 14-video course exploring design patterns and challenges in refactoring code. First, learn the essential machine learning principles when planning code, including how to identify what instead of how, and to look for consistencies. You will then learn to recognize the causes of technical debts that contribute to challenges in existing code. Next, you will learn code refactoring techniques and types of processes that you can use to eliminate deficiencies in the code. This course demonstrates the refactoring capabilities provided by PyCharm to refactor Python code, and the steps involved in optimizing Python code. You will learn static code analysis of Python by using Prospector, refactoring code to ensure backward compatibility, and the role of design patterns in code refactoring, and how to use rope to refactor Python code. Finally, you will learn to recall the prominent antipatterns that potentially complicate code and code refactoring.

Linear Regression analysis is a simple yet powerful technique for quantifying cause and effect relationships. Use this course to get your head around linear regression as the process of fitting a straight line through a set of points. Learn how to define residuals and use the least square error. Define and measure the R-squared, implement regression analysis, visualize your data by computing a correlation matrix and plotting it in the form of a correlation heatmap, and use scatter plots as a prelude to performing the regression analysis. Finish by implementing the regression analysis first using functions that you write yourself and then using the scikit-learn python library. By the end of the course, you'll be able to identify the need for linear regression and implement it effectively.

Gradient descent is an extremely powerful numerical optimization technique widely used to find optimal values of model parameters during the model training phase of machine learning. Use this course as an introduction to gradient descent, examining how it can be used in a wide variety of optimization problems. Explore how it can be used to perform linear regression, carefully studying the matrix equations used to compute the gradients and updating the model parameters using the gradients as well as the learning rate hyperparameter. Finally, apply a form of gradient descent known as stochastic gradient descent to fit an S-curve, thus implementing logistic regression on a data set. By the end of the course, you'll be able to assuredly implement logistic regression using gradient descent.

Explore machine learning reinforcement learning, along with the essential components of reinforcement learning that will assist in the development of critical algorithms for decisionmaking, in this 10-video course. You will examine how to achieve continuous improvement in performance of machines or programs over time, along with key differences between reinforcement learning and machine learning paradigm. Learners will observe how to depict the flow of reinforcement learning by using agent, action, and environment. Next, you will examine different scenarios of state changes and transition processes applied in reinforcement learning. Then examine the reward hypothesis, and learn to recognize the role of rewards in reinforcement learning. You will learn that all goals can be described by maximization of the expected cumulative rewards. Continue by learning the essential steps applied by agents in reinforcement learning to make decisions. You will explore the types of reinforcement learning environments, including deterministic, observable, discrete or continuous, and single-agent or multi-agent. Finally, you will learn how to install OpenAI Gym and OpenAl Universe.

This 9-video course explores how to implement machine learning reinforcement learning by examining the terminology, including agents, the environment, state, and policy. This course demonstrates how to implement reinforcement learning by using Keras and Python; how to ensure that you can build a model; and how to launch and use Ubuntu, and VI editor to do score calculations. First, learn the role of the Markov decision process in which the agent observes the environment, with output consisting of a reward and the next state, and then acts upon it. You will explore Q-learning, a model-free reinforcement learning technique, an asynchronous dynamic programming approach, and will learn about the Q-learning rule, and Deep Q-learning. Next, learn the steps to install TensorFlow for reinforcement learning, as well as framework, which is used for reinforcement learning provided by OpenAI. Then learn how to implement TensorFlow for reinforcement learning. Finally, you will learn to implement Q-learning using Python, and then utilize capabilities of OpenAl Gym and FrozenLake.

This course explores research being done in machine learning and deep learning. Topics covered include neural networks and deep neural networks. First, learners examine how to prevent neural networks from overfitting. You will explore research on multilabel learning algorithms, multilabel classification, and multiple-output classifications, which are variants of the standard classification problem. Then examine deep learning algorithms, the enhanced performance of deeper neural networks that are more adept at automatic feature extraction. Next, ut facial alignment, regression tree ensembles, and deep features for scene recognition. Review ELM (Extreme Learning Machine), and how it is used to perform regression and multiclass classification.