Data / ML / AI - III (Online Courses)
Elevate your career trajectory with our premier online course, designed to sharpen your competitive edge. Explore our curated selection of top-tier digital programs to hone your skills and propel your professional journey forward. Experience transformative learning tailored to empower your career advancement in today's dynamic landscape.
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This Course Includes
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53 hours 30 minutesof self-paced video lessons -
50 Programscrafting your path to success -
Completion Certificateawarded on course completion
GNNs: An Introduction to Graph Neural Networks
Price on Request
1 hour 20 minutes
Graph neural networks (GNNs) have recently become widely applied graph-analysis tools as they help capture indirect dependencies between data elements. Take this course to learn how to transform graph data for use in GNNs. Explore the use cases for machine learning in analyzing graph data and the challenges around modeling graphs for use in neural networks, including the use of adjacency matrices and node embeddings. Examine how a convolution function captures the properties of a node and those of its neighbors. While doing so explore normalization concepts, including symmetric normalization of adjacency matrices. Moving along, work with the Spektral Python library to model a graph dataset for application in a GNN. Finally, practice defining a convolution function for a GNN and examine how the resultant message propagation works. Upon completion you'll have a clear understanding of the need for and challenges around using graph data for machine learning and recognize the power of graph convolutional networks (GCNs).
Perks of Course
Certificate: Yes
CPD Points: 81
Compliance Standards: AICC
GNNs: Classifying Graph Nodes with the Spektral Library
Price on Request
40 minutes
Machine learning (ML) models can be used to extract insights from your graph data. Use this course to learn how to build, train, and evaluate a multi-label classification model using a graph convolutional network (GCN) constructed using the Spektral Python library. Begin by structuring a Spektral dataset for machine learning and learn how data is modeled using an adjacency matrix and feature vectors. Explore how to assign instances of your data to training, validation, and test sets using masks applied to your dataset instance. Construct a graph neural network (GNN) with input layers for the adjacency matrix and features and a GCN convolutional layer and use it to perform node classification. Discover how node features, the edges of the graph, and the structure of the neural network affect the performance of the classification model. Upon completion, you'll be able to prepare a graph structure for use in an ML model and define the factors which can improve the accuracy of model predictions.
Perks of Course
Certificate: Yes
CPD Points: 42
Compliance Standards: AICC
Graph Modeling on Apache Spark: Working with Apache Spark GraphFrames
Price on Request
1 hour 50 minutes
Apache Spark, which is a widely used analytics engine, also helps anyone modeling graphs to perform powerful graph analytics. GraphFrames, a Spark package, aids this process by providing various graph algorithm implementations. Use this course to learn about GraphFrames and the application of graph algorithms on data to extract insights. Explore how GraphFrames complements the Apache Hadoop ecosystem in processing graph data. Getting hands-on, construct and visualize a GraphFrame. Practice querying nodes and relationships in a graph and finding motifs in it. Moving along, work with the breadth-first search and the shortestPaths functions to find paths between graph nodes. And finally, apply the PageRank algorithm to arrive at the most relevant nodes in a network. Upon completion, you'll be able to use GraphFrames to analyze and generate insights from graph data.
Perks of Course
Certificate: Yes
CPD Points: 111
Compliance Standards: AICC
Hadoop & MapReduce Getting Started
Price on Request
1 hour 5 minutes
In this course, learners will explore the theory behind big data analysis using Hadoop, and how MapReduce enables parallel processing of large data sets distributed on a cluster of machines. Begin with an introduction to big data and the various sources and characteristics of data available today. Look at challenges involved in processing big data and options available to address them. Next, a brief overview of Hadoop, its role in processing big data, and the functions of its components such as the Hadoop Distributed File System (HDFS), MapReduce, and YARN (Yet Another Resource Negotiator). Explore the working of Hadoop's MapReduce framework to process data in parallel on a cluster of machines. Recall steps involved in building a MapReduce application and specifics of the Map phase in processing each row of the input file's data. Recognize the functions of the Shuffle and Reduce phases in sorting and interpreting the output of the Map phase to produce a meaningful output. To conclude, complete an exercise on the fundamentals of Hadoop and MapReduce.
Perks of Course
Certificate: Yes
CPD Points: 63
Compliance Standards: AICC
Hadoop HDFS File Permissions
Price on Request
50 minutes
Explore reasons why not all users should have free reign over all data sets, when managing a data warehouse. In this 9-video Skillsoft Aspire course, learners explore how file permissions can be viewed and configured in HDFS (Hadoop File Management System) and how the NameNode UI is used to monitor and explore HDFS. For this course, you need a good understanding of Hadoop and HDFS, along with familiarity with the HDFS shells, and confidence in working with and manipulating files on HDFS, and exploring it from the command line. The course focuses on different ways to view permissions, which are linked to files and directories, and how these can be modified. Learners explore automating many tasks involving HDFS by simply scripting them, and to use HDFS NameNode UI to monitor the distributed file system, and explore its contents. Review distributed computing and big data. The closing exercise involves writing a command to be used on the HDFS dfs shell to count the number of files within a directory on HDFS, and to perform related tasks.
Perks of Course
Certificate: Yes
CPD Points: 48
Compliance Standards: AICC
Hadoop HDFS Getting Started
Price on Request
1 hour 15 minutes
Explore the concepts of analyzing large data sets in this 12-video Skillsoft Aspire course, which deals with Hadoop and its Hadoop Distributed File System (HDFS), which enables parallel processing of big data efficiently in a distributed cluster. The course assumes a conceptual understanding of Hadoop and its components; purely theoretical, it contains no labs, with just enough information provided to understand how Hadoop and HDFS allow processing big data in parallel. The course opens by explaining the ideas of vertical and horizontal scaling, then discusses functions served by Hadoop to horizontally scale data processing tasks. Learners explore functions of YARN, MapReduce, and HDFS, covering how HDFS keeps track of where all pieces of large files are distributed, replication of data, and how HDFS is used with Zookeeper: a tool maintained by the Apache Software Foundation and used to provide coordination and synchronization in distributed systems, along with other services related to distributed computing-a naming service, configuration management, and so on. Learn about Spark, a data analytics engine for distributed data processing.
Perks of Course
Certificate: Yes
CPD Points: 74
Compliance Standards: AICC
Hadoop MapReduce Applications With Combiners
Price on Request
1 hour 25 minutes
In this Skillsoft Aspire course, explore the use of Combiners to make MapReduce applications more efficient by minimizing data transfers. Start by learning about the need for Combiners to optimize the execution of a MapReduce application by minimizing data transfers within a cluster. Recall the steps to process data in a MapReduce application, and look at using a Combiner to perform partial reduction of data output from the Mapper. Then create a new project to calculate average automobile prices using Maven for a MapReduce application. Next, develop the Mapper and Reducer to calculate the average price for automobile makes in the input data set. Create a driver program for the MapReduce application, run it, and check output to get the average price per automobile. Learn how to code up a Combiner for a MapReduce application, fix the bug in the application so it can be used to correctly calculate the average price, then run the fixed application to verify that the prices are being calculated correctly. The concluding exercise concerns optimizing MapReduce with Combiners.
Perks of Course
Certificate: Yes
CPD Points: 83
Compliance Standards: AICC
Harnessing Data Volume & Velocity: Turning Big Data into Smart Data
Price on Request
40 minutes
In this course, you will explore the concept of smart data and its associated lifecycle and benefits and the frameworks and algorithms that can help transition big data to smart data. Begin by comparing big data and smart data from the perspective of volume, variety, velocity, and veracity. Look at smart data capabilities for machine learning and artificial intelligence. Examine how to turn big data into smart data and how to use data volumes; list applications of smart data and smart process, and recall use cases for smart data application. Then explore the lifecycle of smart data and the associated impacts and benefits. Learn steps involved in transforming big data into smart data by using k-NN (K Nearest Neighbor algorithm), and look at various smart data solution implementation frameworks. Recall how to turn smart data into business by using data sharing and algorithms and how to implement clustering on smart data. Finally, learn about integrating smart data and its impact on optimization of data strategy. The exercise concerns transforming big data into smart data.
Perks of Course
Certificate: Yes
CPD Points: 38
Compliance Standards: AICC
Implementing AI Using Cognitive Modeling
Price on Request
45 minutes
Cognitive modeling can provide additional human qualities to AI systems. It is traditionally used in cognitive machines and expert systems. However, with extra computing power, it can be applied to more profound AI approaches like neural networks and reinforcement learning systems. Knowledge of cognitive modeling applications is essential to any AI developer aspiring to design AI architectures and develop large-scale applications. In this course, you'll examine the role of cognitive modeling in AI development and its possible applications in NLP, image recognition, and neural networks. You'll outline core cognitive modeling concepts and significant industry use cases. You'll list open source cognitive modeling frameworks and explore cognitive machines, expert systems, and reinforcement learning in cognitive modeling. Finally, you'll use cognitive models to solve real-world problems.
Perks of Course
Certificate: Yes
CPD Points: 44
Compliance Standards: AICC
Implementing AI With Amazon ML
Price on Request
40 minutes
Amazon offers AI developers a wide variety of tools and frameworks including Amazon Web Services (AWS) and the Amazon Machine Learning (ML) framework. By integrating complex machine and deep learning development with the extensive computing capabilities of Amazon, Amazon ML allows AI developers to adopt big data AI services. With many companies actively using AWS and Amazon ML, a basic knowledge of this framework is beneficial. In this course, you'll learn how to use Amazon ML together with AWS, to work with big data, and to create machine and deep learning models. You'll also examine the basics of automated model deployment with Amazon SageMaker. Next, you'll explore how to use Amazon ML for image and video analysis, text-to-speech translation, and text analytics. Finally, you'll implement a system to analyze movie review sentiment using the Amazon ML framework.
Perks of Course
Certificate: Yes
CPD Points: 38
Compliance Standards: AICC
Implementing Bayesian Model and Computation with PyMC
Price on Request
45 minutes
Learners can examine the concept of Bayesian learning and the different types of Bayesian models in this 12-video course. Discover how to implement Bayesian models and computations by using different approaches and PyMC for your machine learning solutions. Learners start by exploring critical features of and difficulties associated with Bayesian learning methods, and then take a look at defining the Bayesian model and classifying single-parameter, multiparameter, and hierarchical Bayesian models. Examine the features of probabilistic programming and learn to list the popular probabilistic programming languages. You will look at defining Bayesian models with PyMC and arbitrary deterministic function and generating posterior samples with PyMC models. Next, learners recall the fundamental activities involved in the PyMC Bayesian data analysis process, including model checking, evaluation, comparison, and model expansion. Delve into the computation methods of Bayesian, including numerical integration, distributional approximation, and direct simulation. Also, look at computing with Markov chain simulation, and the prominent algorithms that can be used to find posterior modes based on the distribution approximation. The concluding exercise focuses on Bayesian modeling with PyMC.
Perks of Course
Certificate: Yes
CPD Points: 47
Compliance Standards: AICC
Implementing Deep Learning: Optimized Deep Learning Applications
Price on Request
40 minutes
This 11-video course explores the concepts of computational graphics, interfaces for programming graphics processing units (GPUs), and TensorFlow Extended and its pipeline components. Learners discover features and elements that should be considered for machine learning when building deep learning (DL) models, as well as hyperparameters that can be tuned to optimize DL models. Begin by examining the concept of computational graphs and recognize essential computational graph operations used in implementing DL. Then learn to list prominent processors with specialized purpose and architectures used in implementing DL. Recall prominent interfaces for programming GPUs with focus on Compute Unified Device Architecture (CUDA) and OpenCL, and then take a look at TensorFlow Extended (TFX) and TFX pipeline components for machine learning pipelines. Discover how to setup the TFX environment; use the ExampleGen and StatisticsGen TFX pipeline components to build pipelines; work with TensorFlow Model analysis; and explore the practical considerations for DL build and train. Finally, recall essential hyperparameters of DL algorithms that can be tuned to optimize DL models. The concluding exercise involves optimizing DL applications.
Perks of Course
Certificate: Yes
CPD Points: 42
Compliance Standards: AICC
Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools
Price on Request
1 hour
Explore the concept of deep learning, including a comparison between machine learning and deep learning (ML/DL) in this 12-video course. Learners will examine the various phases of ML/DL workflows involved in building deep learning networks; recall the essential components of building and applying deep learning networks; and take a look at the prominent frameworks that can be used to simplify building ML/DL applications. You will then observe how to use the Caffe2 framework for implementing recurrent convolutional neural networks; write PyTorch code to generate images using autoencoders; and implement deep neural networks by using Python and Keras. Next, compare the prominent platforms and frameworks that can be used to simplify deep learning implementations; identify and select the best fit frameworks for prominent ML/DL use cases; and learn how to recognize challenges and strategies associated with debugging deep learning networks and algorithms. The closing exercise involves identifying the steps of ML workflow, deep learning frameworks, and strategies for debugging deep learning networks.
Perks of Course
Certificate: Yes
CPD Points: 58
Compliance Standards: AICC
Implementing Governance Strategies
Price on Request
45 minutes
This course explores the key concepts behind governance and its relationship with big data. Big data are large and complex, often being represented by massive amounts of very granular data that need to be protected from misuse. This 12-video course examines the five main requirements when an all-encompassing governance strategy is being planned and designed. You will first learn to build a data governance plan by first identifying the most important data domain. Then learn the importance of assembling a data governance body for an organization's big data activities; and how to identify the stakeholders that need to be part of a data governance program. Next, you will learn why the members' governance body should be fairly diverse, well trained, and informed of the policies surrounding the collection of data and the procedures for using the data; and should include compliance professionals who understand the rules and regulations applicable to your corporate structure. Finally, you will explore the issues involved in cloud storage of big data.
Perks of Course
Certificate: Yes
CPD Points: 45
Compliance Standards: AICC
Importing & Exporting Data using R
Price on Request
35 minutes
An essential skill for statistical computing and graphics. The programming language R the tool of choice for data science professionals in every industry and field-both to take advantage of R's great graphic and charting capabilities and to create reproducible high-quality analyses. In this 8-video Skillsoft Aspire course, you will discover how to use R to import and export tabular data in CSV (comma-separated values), Excel, and HTML format. The key concepts covered in this course include how to read data from a CSV formatted text file and from an Excel spreadsheet; how to read tabular data from an HTML file; and how to export tabular data from R to a CSV file and to an Excel spreadsheet. In addition, learners will explore exporting tabular data from R to an HTML table; how to read data from an HTML table and export to CSV; and how to confirm that the contents of the CSV file were written correctly.
Perks of Course
Certificate: Yes
CPD Points: 33
Compliance Standards: AICC
Improving Neural Networks: Data Scaling & Regularization
Price on Request
1 hour 35 minutes
Explore how to create and optimize machine learning neural network models, scaling data, batch normalization, and internal covariate shift. Learners will discover the learning rate adaptation schedule, batch normalization, and using L1 and L2 regularization to manage overfitting problems. Key concepts covered in this 10-video course include the approach of creating deep learning network models, along with steps involved in optimizing networks, including deciding size and budget; how to implement the learning rate adaptation schedule in Keras by using SGD and specifying learning rate, epoch, and decay using Google Colab; and scaling data and the prominent data scaling methods, including data normalization and data standardization. Next, you will learn the concept of batch normalization and internal covariate shift; how to implement batch normalization using Python and TensorFlow; and the steps to implement L1 and L2 regularization to manage overfitting problems. Finally, observe how to implement gradient descent by using Python and the steps related to library import and data creation.
Perks of Course
Certificate: Yes
CPD Points: 97
Compliance Standards: AICC
Improving Neural Networks: Loss Function & Optimization
Price on Request
1 hour 5 minutes
Learners can explore the concept of loss function, the different types of Loss function and their impact on neural networks, and the causes of optimization problems, in this 10-video course. Examine alternatives to optimization, the prominent optimizer algorithms and their associated properties, and the concept of learning rates in neural networks for machine learning solutions. Key concepts in this course include learning loss function and listing various types of loss function; recognizing impacts of the different types of loss function on neural networks models; and learning how to calculate loss function and score by using Python. Next, learners will learn to recognize critical causes of optimization problems and essential alternatives to optimization; recall prominent optimizer algorithms, along with their properties that can be applied for optimization; and how to perform comparative optimizer analysis using Keras. Finally, discover the relevance of learning rates in optimization and various approaches of improving learning rates; and learn the approach of finding learning rate by using RMSProp optimizer.
Perks of Course
Certificate: Yes
CPD Points: 63
Compliance Standards: AICC
Improving Neural Networks: Neural Network Performance Management
Price on Request
1 hour 55 minutes
In this 12-video course, learners can explore machine learning problems that can be addressed with hyperparameters, and prominent hyperparameter tuning methods, along with problems associated with hyperparameter optimization. Key concepts covered here include the iterative workflow for machine learning problems, with a focus on essential measures and evaluation protocols; steps to improve performance of neural networks, along with impacts of data set sizes on neural network models and performance estimates; and impact of the size of training data sets on quality of mapping function and estimated performance of a fit neural network model. Next, you will learn the approaches of identifying overfitting scenarios and preventing overfitting by using regularization techniques; learn the impact of bias and variances on machine learning algorithms, and recall the approaches of fixing high bias and high variance in data sets; and see how to trade off bias variance by building and deriving an ideal learning curve by using Python. Finally, learners will observe how to test multiple models and select the right model by using Scikit-learn.
Perks of Course
Certificate: Yes
CPD Points: 116
Compliance Standards: AICC
Inferential Statistics
Price on Request
1 hour
In this Skillsoft Aspire course on data science, learners can explore hypothesis testing, which finds wide applications in data science. This beginner-level, 10-video course builds upon previous coursework by introducing simple inferential statistics, called the backbone of data science, because they seek to posit and prove or disprove relationships within data. You will start by learning steps in simple hypothesis testing: the null and alternative hypotheses, s-statistic, and p-value, as ach term is introduced and explained. Next, listen to an informative discussion of a specific family of hypothesis tests, the t-test. Then learn to describe their applications, and become familiar with how to use cases including linear regression. Learn about Gaussian distribution and the related concepts of correlation, which measures relationships between any two variables, and autocorrelation, a special form used in the concept of time-series analysis. In the closing exercise, review your knowledge by differentiating between the null and the alternative hypotheses in a hypothesis testing procedure, then enumerating four distinct uses for different types of t-tests.
Perks of Course
Certificate: Yes
CPD Points: 61
Compliance Standards: AICC
Introducing Apache Spark for AI Development
Price on Request
35 minutes
Apache Spark provides a robust framework for implementing machine learning and deep learning. It takes advantage of resilient distributed databases to provide a fault-tolerant platform well-suited to developing big data applications. Because many large companies are actively using this framework, AI developers should be familiar with the basics of implementing AI with Apache Spark and Spark ML. In this course, you'll explore the concept of distributed computing. You'll identify the benefits of using Spark for AI Development, examining the advantages and disadvantages of using Spark over other big data AI platforms. Next, you'll describe how to implement machine learning, deep learning, natural language processing, and computer vision using Spark. Finally, you'll use Spark ML to create a movie recommendation system commonly used by Netflix and YouTube.
Perks of Course
Certificate: Yes
CPD Points: 36
Compliance Standards: AICC
Kubernetes & Automation Testing
Price on Request
1 hour 5 minutes
Learners can explore design principles, architecture, and essential components of Kubernetes, as well as how Kubernetes helps implement end-to-end software testing, in this 16-video course. You will begin with a detailed look at design principles and architecture behind Kubernetes and the essential components of Kubernetes master and Kubernetes worker. Then you will move on to explore Kubernetes cluster objects and controllers; scaling applications on Kubernetes; and Kubernetes cluster application deployment. Learn how to create single-zone clusters with the default features enabled in Google Kubernetes Engine and create volume resources in the clusters. Discover packaging and executing on Kubernetes Engine; Kubernetes and software testing, the various types of testing that can be automated with Kubernetes, and the advantages of deploying test containers in Kubernetes. Delve into test automation with Kubernetes; automation testing with Selenium Grid and Kubernetes; and setting up test environments by using Selenium Grid, Docker, and Kubernetes. Build test container images; discover end-to-end (E2E) testing with Selenium WebDriver, and deploy test containers in Kubernetes clusters and publish the results.
Perks of Course
Certificate: Yes
CPD Points: 67
Compliance Standards: AICC
Leveraging Reusable AI Architecture Patterns
Price on Request
50 minutes
AI architecture patterns, some of which have been known for many years, have been formally identified as such only in the last couple of years. In this course, you'll identify 12 reusable, standard AI architecture patterns, and 3 AI architecture anti-patterns frequently used to architect common AI applications. You'll learn to differentiate between architecture and design patterns and explore how they're used. Next, you'll examine the structure of an AI architecture pattern, and that of an anti-pattern and its different parts. You'll identify when specific patterns should or can be used, when they need to be avoided, and how to avoid using anti-patterns. You will also learn that even good patterns can become anti-patterns when applied to solve a problem they were not intended for.
Perks of Course
Certificate: Yes
CPD Points: 49
Compliance Standards: AICC
Linear Algebra & Probability: Advanced Linear Algebra
Price on Request
1 hour 40 minutes
Learners will discover how to apply advanced linear algebra and its principles to derive machine learning implementations in this 14-video course. Explore PCA, tensors, decomposition, and singular-value decomposition, as well as how to reconstruct a rectangular matrix from singular-value decomposition. Key concepts covered here include how to use Python libraries to implement principal component analysis with matrix multiplication; sparse matrix and its operations; tensors in linear algebra and arithmetic operations that can be applied; and how to implement Hadamard product on tensors by using Python. Next, learn how to calculate singular-value decomposition and reconstruct a rectangular matrix; learn the characteristics of probability applicable in machine learning; and study probability in linear algebra and its role in machine learning. You will learn types of random variables and functions used to manage random numbers in probability; examine the concept and characteristics of central limit theorem and means and learn common usage scenarios; and examine the concept of parameter estimation and Gaussian distribution. Finally, learn the characteristics of binomial distribution with real-time examples.
Perks of Course
Certificate: Yes
CPD Points: 102
Compliance Standards: AICC
Linear Algebra and Probability: Fundamentals of Linear Algebra
Price on Request
1 hour 40 minutes
Explore the fundamentals of linear algebra, including characteristics and its role in machine learning, in this 13-video course. Learners can examine important concepts associated with linear algebra, such as the class of spaces, types of vector space, vector norms, linear product vector and theorems, and various operations that can be performed on matrix. Key concepts examined in this course include important classes of spaces associated with linear algebra; features of vector spaces and the different types of vector spaces and their application in distribution and Fourier analysis; and inner product spaces and the various theorems that are applied on inner product spaces. Next, you will learn how to implement vector arithmetic by using Python; learn how to implement vector scalar multiplication with Python; and learn the concept and different types of vector norms. Finally, learn how to implement matrix-matrix multiplication, matrix-vector multiplication, and matric-scalar multiplication by using Python; and learn about matrix decomposition and the roles of Eigenvectors and Eigenvalues in machine learning.
Perks of Course
Certificate: Yes
CPD Points: 100
Compliance Standards: AICC
Linear Models & Gradient Descent: Gradient Descent and Regularization
Price on Request
55 minutes
Explore the features of simple and multiple regression, implement simple and multiple regression models, and explore concepts of gradient descent and regularization and different types of gradient descent and regularization. Key concepts covered in this 12-video course include characteristics of the prominent types of linear regression; essential features of simple and multiple regressions and how they are used to implement linear models; and how to implement simple regression models by using Python libraries for machine learning solutions. Next, observe how to implement multiple regression models in Python by using Scikit-learn and StatsModels; learn the different types of gradient descent; and see how to classify the prominent gradient descent optimization algorithms from the perspective of their mathematical representation. Learn how to implement a simple representation of gradient descent using Python; how to implement linear regression by using mini-batch gradient descent to compute hypothesis and predictions; and learn the benefits of regularization and the objectives of L1 and L2 regularization. Finally, learn how to implement L1 and L2 regularization of linear models by using Scikit-learn.
Perks of Course
Certificate: Yes
CPD Points: 53
Compliance Standards: AICC
Linear Models & Gradient Descent: Managing Linear Models
Price on Request
45 minutes
Explore the concept of machine learning linear models, classifications of linear models, and prominent statistical approaches used to implement linear models. This 11-video course also explores the concepts of bias, variance, and regularization. Key concepts covered here include learning about linear models and various classifications used in predictive analytics; learning different statistical approaches that are used to implement linear models [single regression, multiple regression and analysis of variance (ANOVA)]; and various essential components of a generalized linear model (random component, linear predictor and link function). Next, discover differences between the ANOVA and analysis of covariance (ANCOVA) approaches of statistical testing; learn about implementation of linear regression models by using Scikit-learn; and learn about the concepts of bias, variance, and regularization and their usages in evaluating predictive models. Learners explore the concept of ensemble techniques and illustrate how bagging and boosting algorithms are used to manage predictions, and learn to implement bagging algorithms with the approach of random forest by using Scikit-learn. Finally, observe how to implement boosting ensemble algorithms by using Adaboost classifier in Python.
Perks of Course
Certificate: Yes
CPD Points: 47
Compliance Standards: AICC
Linear Regression Models: Building Models with Scikit Learn & Keras
Price on Request
40 minutes
Learn how to use the Scikit Learn and Keras libraries to build a linear regression model to predict a house price. This course reviews the steps needed to prepare data and configure regression models. It shows how to prepare a data set to feed a linear regression model; how to use the Pandas library to load a CSV data set file; and how to configure, train, and validate linear regression models. The course also shows how to visualize metrics with Matplotlib; how to prepare data for a Keras model, how to learn the architecture for a Keras sequential model and initialize it; and finally, how train it to use optimal weights and biases for machine learning solutions.
Perks of Course
Certificate: Yes
CPD Points: 41
Compliance Standards: AICC
Linear Regression Models: Introduction
Price on Request
1 hour 20 minutes
Machine learning (ML) is everywhere these days, often invisible to most of us. In this course, you will discover one of the fundamental problems in the world of ML: linear regression. Explore how this is solved with classic ML as well as neural networks. Key concepts covered here include how regression can be used to represent a relationship between two variables; applications of regression, and why it is used to make predictions; and how to evaluate the quality of a regression model by measuring its loss. Next, learn techniques used to make predictions with regression models; compare classic ML and deep learning techniques to perform a regression; and observe various components of a neural network and how they fit together. You will learn the two types of functions used in a neuron and their individual roles; how to calculate the optimal weights and biases of a neural network; and how to find the optimal parameters for a neural network.
Perks of Course
Certificate: Yes
CPD Points: 78
Compliance Standards: AICC
Linear Regression Models: Introduction to Logistic Regression
Price on Request
55 minutes
Logistic regression is a technique used to estimate the probability of an outcome for machine learning solutions. In this 10-video course, learners discover the concepts and explore how logistic regression is used to predict categorical outcomes. Key concepts covered here include the qualities of a logistic regression S-curve and the kind of data it can model; learning how a logistic regression can be used to perform classification tasks; and how to compare logistic regression with linear regression. Next, you will learn how neural networks can be used to perform a logistic regression; how to prepare a data set to build, train, and evaluate a logistic regression model in Scikit Learn; and how to use a logistic regression model to perform a classification task and evaluate the performance of the model. Learners observe how to prepare a data set to build, train, and evaluate a Keras sequential model, and how to build, train, and validate Keras models by defining various components, including activation functions, optimizers and the loss function.
Perks of Course
Certificate: Yes
CPD Points: 57
Compliance Standards: AICC
Linear Regression Models: Multiple & Parsimonious
Price on Request
1 hour 10 minutes
Several factors usually influence an outcome, and users need to consider all of those by using regression. Regression models help us mathematically evaluate our hunches. This course explores machine learning techniques and the risks involved with multiple factor linear regression. Key concepts covered here include reasons to use multiple features in a regression, and how to configure, train, and evaluate the linear regression model. Next, learn to create a data set with multiple features in a form that can be fed to a neural network for training and validation. Review Keras sequential model architecture, its training parameters, and ways to test its predictions. Learn how to use Pandas and Seaborn to view correlations and enumerate risks. Conclude by applying parsimonious regression to rebuild linear regression models.
Perks of Course
Certificate: Yes
CPD Points: 70
Compliance Standards: AICC
Loading & Querying Data with Hive
Price on Request
1 hour 20 minutes
Among the market's most popular data warehouses used for data science, Apache Hive simplifies working with large data sets in files by representing them as tables. In this 12-video Skillsoft Aspire course, learners explore how to create, load, and query Hive tables. For this hands-on course, learners should have a conceptual understanding of Hive and its basic components, and prior experience with querying data from tables using SQL (structured query language) and with using the command line. Key concepts covered include cluster, joining tables, and modifying tables. Demonstrations covered include using the Beeline client for Hive for simple operations; creating tables, loading them with data, and then running queries against them. Only tables with primitive data types are used here, with data loaded into these tables from HDFS (Hadoop Distributed File System) file system and local machines. Learners will work with Hive metastore and temporary tables, and how they can be used. You will become familiar with basics of using the Hive query language and quite comfortable working with HDFS.
Perks of Course
Certificate: Yes
CPD Points: 79
Compliance Standards: AICC
Math & Optimizations: Introducing Graphs & Graph Operations
Price on Request
1 hour 30 minutes
The graph data structure plays a significant role in modeling entities in the real world. A graph comprises nodes and edges that are used to represent entities and relationships, respectively. A graph can be used to model a social network or a professional network, roads and rail infrastructure, and telecommunication and telephone networks. Through this course, you'll explore graph data structure, graph components, and different types of graphs and their use cases. Start by discovering how to represent directed, undirected, weighted, and unweighted graphs in NetworkX. You'll then learn more about visualizing nodes and connections in graphs using Matplotlib. This course will also help you examine how to implement graph algorithms on all graph types using NetworkX. Upon completing this course, you will have the skills and knowledge to create and work with graphs using NetworkX in Python.
Perks of Course
Certificate: Yes
CPD Points: 88
Compliance Standards: AICC
Math & Optimizations: Introducing Sets & Set Operations
Price on Request
1 hour 30 minutes
Discrete mathematics is the study of objects that take on distinct, separated values. The study of discrete mathematics is important in the field of Computer Science as computers can only understand discrete binary numbers. Use this course to learn more about the use and importance of discrete mathematics in the world of computer science. Examine the use of sets and perform common operations on them in Python. These operations include union, intersection, difference, and symmetric difference. When you are finished with this course, you will have the skills to use and work with sets in the real world using Python.
Perks of Course
Certificate: Yes
CPD Points: 56
Compliance Standards: AICC
Math & Optimizations: Solving Optimization Problems Using Integer Programming
Price on Request
55 minutes
Integer programming is a mathematical optimization model that helps find optimal solutions to our problems. Integer programming problems find more applications than linear programming and are an important tool in a developer's toolkit. Examine how to solve optimizations problems using integer programming through this course. Start by comparing the integer programming optimization model and linear programming. You'll then move on to the LP relaxation technique and how it can be used to obtain the starting point of an integer programming solution. You'll also explore the Pulp Python library through different case studies consisting of integer programming problems. Upon completing this course, you'll be able to apply integer programming to solve optimization problems.
Perks of Course
Certificate: Yes
CPD Points: 53
Compliance Standards: AICC
Math & Optimizations: Solving Optimization Problems Using Linear Programming
Price on Request
1 hour 25 minutes
Mathematical optimization models allow us to represent our objectives, decision variables, and constraints in mathematical terms, and solving these models gives us the optimal solution to our problems. Linear programming is an optimization model that can be used when our objective function and constraints can be represented using linear terms. Use this course to learn how decision-making can be represented using mathematical optimization models. Begin by examining how optimization problems can be formulated using objective functions, decision variables, and constraints. You'll then recognize how to find an optimal solution to a problem from amongst feasible solutions through a case study. This course will also help you investigate the pros and cons of the assumptions made by linear programming and the steps involved in solving linear programming problems graphically as well as by using the Simplex method. When you are done with this course, you will have the skills and knowledge to apply linear programming to solve optimization problems.
Perks of Course
Certificate: Yes
CPD Points: 87
Compliance Standards: AICC
Matrix Decomposition: Getting Started with Matrix Decomposition
Price on Request
1 hour 15 minutes
Matrix decomposition refers to the process of expressing a matrix as the product of other matrices. These factorized matrices are a lot easier to work with than the original matrix, as they usually possess specific properties desirable in the contexts of various mathematical procedures. Use this course to learn how to use matrix decomposition. Explore precisely what matrices and vectors are and how they're used. Then, study various matrix operations, such as computing the transpose and the inverse of a matrix. Moving on, identify why matrices are great for expressing linear transformations of points in a coordinate space. Work with important transformations, such as shearing, reflection, and rotation. Implement the LU, QR, and Cholesky decompositions and examine their applicability and restrictions. Upon completion, you'll know when and how to implement various matrix decompositions.
Perks of Course
Certificate: Yes
CPD Points: 75
Compliance Standards: AICC
Matrix Decomposition: Using Eigendecomposition & Singular Value Decomposition
Price on Request
1 hour 25 minutes
Eigenvalues, eigenvectors, and the Singular Value Decomposition (SVD) are the foundation of many important techniques, including the widely used method of Principal Components Analysis (PCA). Use this course to learn when and how to use these methods in your work. To start, investigate precisely what eigenvectors and eigenvalues are. Then, explore various examples of eigendecomposition in practice. Moving on, use eigenvalues and eigenvectors to diagonalize a matrix, noting why diagonalizing matrices is extremely efficient in computing matrix higher powers. By the end of the course, you'll be able to apply eigendecomposition and Singular Value Decomposition to diagonalize different types of matrices and efficiently compute higher powers of matrices in this manner.
Perks of Course
Certificate: Yes
CPD Points: 84
Compliance Standards: AICC
Natural Language Processing: Getting Started with NLP
Price on Request
40 minutes
Enterprises across the world are creating large amounts of language data. There are many different kinds of data with language components including reports, word documents, operational data, emails, reviews, sops, and legal documents. This course will help you develop the skills to analyze this data and extract valuable and actionable insights. Learn about the various building blocks of natural language processing to help in understanding the different approaches used for solving NLP problems. Examine machine learning and deep learning approaches to handling NLP issues. Finally, explore common use cases that companies are approaching with NLP solutions. Upon completion of this course, you will have a strong foundation in the fundamentals of natural language processing, its building blocks, and the various approaches that can be used to architect solutions for enterprises in NLP domains.
Perks of Course
Certificate: Yes
CPD Points: 40
Compliance Standards: AICC
Natural Language Processing: Linguistic Features Using NLTK & spaCy
Price on Request
1 hour 10 minutes
Without fundamental building blocks and industry-accepted tools, it is difficult to achieve state-of-art analysis in NLP. In this course, you will learn about linguistic features such as word corpora, tokenization, stemming, lemmatization, and stop words and understand their value in natural language processing. Begin by exploring NLTK and spaCy, two of the most widely used NLP tools, and understand what they can help you achieve. Learn to recognize the difference between these tools and understand the pros and cons of each. Discover how to implement concepts like part of speech tagging, named entity recognition, dependency parsing, n-grams, spell correction, segmenting sentences, and finding similar sentences. Upon completion of this course, you will be able to build basic NLP applications on any raw language data and explore the NLP features that can help businesses take actionable steps with this data.
Perks of Course
Certificate: Yes
CPD Points: 70
Compliance Standards: AICC
Neural Network Mathematics: Exploring the Math behind Gradient Descent
Price on Request
1 hour 55 minutes
Because neural networks comprise thousands of neurons and interconnections, one can assume training a neural network involves millions of computations. This is where a general-purpose optimization algorithm called gradient descent comes in. Use this course to gain an intuitive and visual understanding of how gradient descent and the gradient vector work. As you advance, examine three neural network activation functions, ReLU, sigmoid, and hyperbolic tangent functions, and two variants of the ReLU function, Leaky ReLU and ELU. In examining variants of the ReLU activation function, learn how to use them to deal with deep neural network training issues. Finally, implement a neural network from scratch using TensorFlow and basic Python. When you're done, you'll be able to illustrate the mathematical intuition behind neural networks and be prepared to tackle more complex machine learning problems.
Perks of Course
Certificate: Yes
CPD Points: 113
Compliance Standards: AICC
Neural Network Mathematics: Understanding the Mathematics of a Neuron
Price on Request
50 minutes
First conceived in the 1940s, it wasn't until the early 2010s that artificial neurons showed their true potential as layered entities in the form of neural networks. When big data processing using distributed computing became mainstream, the computational capacity was now available to train these neural networks on huge datasets. Knowing this is one thing, but understanding how it all works is where the true potential lies. Use this course to gain an intuitive understanding of how neural networks work. Explore the mathematical operations performed by a single neuron. Recognize the potential of thousands of neurons connected together in a well-architected design. Finally, implement code to mathematically perform the operations in a single layer of neurons working on batch input. When you're finished, you'll have a solid grasp of the mechanisms behind neural networks and the math behind neurons.
Perks of Course
Certificate: Yes
CPD Points: 50
Compliance Standards: AICC
NLP Case Studies: Article Text Comprehension & Question Answering
Price on Request
30 minutes
Most current question answering datasets will frame the task as reading comprehension, where the question is about a paragraph or document and the answer often is a span in the document. Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. This course focuses on the architecture of the Q&A pipeline. First, install the Transformers library and import a text comprehension model to create your Q&S pipeline. Then, use Gradio to develop a user interface for answering questions about a given article. Upon completion, you'll be able to develop an application that can answer questions asked by a user about a given article.
Perks of Course
Certificate: Yes
CPD Points: 28
Compliance Standards: AICC
NLP Case Studies: News Scraping Translation & Summarization
Price on Request
45 minutes
Keeping up with current events can be challenging, especially when you live or work in a country where you do not speak the language. Learning a new language can be difficult and time-consuming when you have a busy schedule. In this course, you will learn how to scrape news articles written in Arabic from websites, translate them into English, and then summarize them. First, focus on the overall architecture of your summarization application. Next, discover the Transformers library and explore its role in translation and summarization tasks. Then, create a user interface for the application using Gradio. Upon completion of this course, you'll be able to use an application to scrape data written in Arabic from any URL, translate it into English, and summarize it
Perks of Course
Certificate: Yes
CPD Points: 43
Compliance Standards: AICC
Non-relational Data: Non-relational Databases
Price on Request
1 hour 30 minutes
Non-relational (NoSQL) databases are attractive for working with Big Data because they provide a way to store data from different sources in the same document and organize large amounts of diverse and complex data. Use this course to discover the principles behind non-relational databases and NoSQL, explore their benefits, and examine different types of non-relational architectures, such as document, key-value, graph, columnar, and multi-model databases. You'll also get familiar with HBase and NewSQL. After finishing this course, you will be able to identify the suitable NoSQL database required for any given business problem.
Perks of Course
Certificate: Yes
CPD Points: 46
Compliance Standards: AICC
Optimizing Query Executions with Hive
Price on Request
40 minutes
In this 7-video Skillsoft Aspire course, learners can explore optimizations allowing Apache Hive to handle parallel processing of data, while users can still contribute to improving query performance. For this course, learners should have previous experience with Hive and familiarity with querying big data for analysis purposes. The course focuses only on concepts; no queries are run. Learners begin to understand how to optimize query executions in Hive, beginning with exploring different options available in Hive to query data in an optimal manner. Discuss how to split data into smaller chunks, specifically, partitioning and bucketing, so that queries need not scan full data sets each time. Hive truly democratizes access to data stored in a Hadoop cluster, eliminating the need to know MapReduce to process cluster data, and makes data accessible using the Hive query language. All files in Hadoop are exposed in the form of tables. Watch demonstrations of structuring queries to reduce numbers of map reduce operations generated by Hive, and speeding up query executions. Other concepts covered include partitioning, bucketing, and joins.
Perks of Course
Certificate: Yes
CPD Points: 42
Compliance Standards: AICC
Planning AI Implementation
Price on Request
45 minutes
This 13-video course explores how artificial intelligence (AI) can be leveraged, how to plan an AI implementation from setup to architecture, and the issues surrounding incorporating it into an enterprise for machine learning. Learners will explore the three legs of AI: how it applies intelligence-like behavior to machines. You will then examine how machine learning adds to this intelligence-like behavior, and the next generation with deep learning. This course discusses strategies for implementation of AI, organizational challenges surrounding the adoption of AI, and the need for training of both personnel and machines. Next, learn the role of data and algorithms in AI implementation. Learners continue by examining several ways in which an organization can plan and develop AI capability; the elements organizations need to understand how to assess AI needs and tools; management challenges; and the impact on personnel. You will learn about pitfalls in using AI, and what to avoid. Finally, you will learn about data issues, data quality, training concepts, overfitting, and bias.
Perks of Course
Certificate: Yes
CPD Points: 44
Compliance Standards: AICC
Predictive Modeling: Implementing Predictive Models Using Visualizations
Price on Request
40 minutes
Explore how to work with machine learning feature selection, general classes of feature selection algorithms, and predictive modeling best practices. In this 12-video course, learners discover how to implement predictive models with scatter plots, boxplots, and crosstabs by using Python. Key concepts examined here include the benefits of feature selection and the general classes of feature selection algorithms; the different types of predictive models that can be implemented and associated features; and how to implement scatterplots and the capability of scatterplots in facilitating predictions. Next, you will learn about Pearson's correlation measures and the possible ranges for Pearson's correlation; learn to recognize the anatomy of a boxplot, a visual representation of the statistical five-number summary of a given data set; and observe how to create and interpret boxplots with Python. Then see how to implement crosstabs to visualize categorical variables; learn statistical concepts that are used for predictive modeling; and learn tree-based methods used to implement regression and classification. Finally, you will learn best practices for implementing predictive modeling.
Perks of Course
Certificate: Yes
CPD Points: 41
Compliance Standards: AICC
Predictive Modeling: Predictive Analytics & Exploratory Data Analysis
Price on Request
40 minutes
Explore the machine learning predictive analytics, exploratory data analytics, and different types of data sets and variables in this 9-video course. Discover how to implement predictive models and manage missing values and outliers by using Python frameworks. Key concepts covered in this course include predictive analytics, a branch of advanced analytics, and its process flow, and learning how analytical base tables can be used to build and score analytical models. Next, you will discover business problems that can be resolved by using predictive modeling; how to build predictive models with the Python framework; and learn the essential features of exploratory data analysis. Then learn about data sets, collections of data corresponding to the content of a single database or a single statistical data matrix, and then learn the variables of the different types of data sets including univariate, bivariate, and multivariate data and analytical approaches that can be implemented with them. Finally, you will learn about methods that can be used to manage missing values and outliers in data sets.
Perks of Course
Certificate: Yes
CPD Points: 40
Compliance Standards: AICC
Probability Theory: Creating Bayesian Models
Price on Request
1 hour 45 minutes
Bayesian models are the perfect tool for use-cases where there are multiple easily observable outcomes and hard-to-diagnose underlying causes, using a combination of graph theory and Bayesian statistics. Use this course to learn more bout stating and interpreting the Bayes theorem for conditional probabilities. Discover how to use Python to create a Bayesian network and calculate several complex conditional probabilities using a Bayesian machine learning model. You'll also examine and use naive Bayes models, which are a category of Bayesian models that assume that the explanatory variables are all independent of each other. Once you have completed this course, you will be able to identify use cases for Bayesian models and construct and effectively employ such models.
Perks of Course
Certificate: Yes
CPD Points: 104
Compliance Standards: AICC
Probability Theory: Getting Started with Probability
Price on Request
55 minutes
Probability is a branch of mathematics that deals with uncertainty, specifically with numerical estimates of how likely an event is to occur and what might happen if that event does or does not occur. Probability has many applications in statistics, engineering, finance, machine learning, and computer science. Get acquainted with the basic constructs of probability through this course. Start by examining different types of events, outcomes, and the complement of an event. You will then simulate various probabilistic experiments in Python and note how the outcomes of these experiments tend to converge towards theoretically expected outcomes as the number of trials increases. By the time you finish this course, you will be able to define and measure probabilities of common events and simulate probabilistic experiments using Python.
Perks of Course
Certificate: Yes
CPD Points: 54
Compliance Standards: AICC