Data / ML / AI - IV (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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38 hours 55 minutesof self-paced video lessons -
36 Programscrafting your path to success -
Completion Certificateawarded on course completion
Probability Theory: Understanding Joint, Marginal, & Conditional Probability
Price on Request
1 hour 35 minutes
Probability is all about estimating the likeliness of the occurrence of specific events. Use this course to learn more about defining and measuring joint, marginal, and conditional probabilities of events. Start by exploring the chain rule of probability and then use this rule to compute conditional probabilities of multiple events. You'll also investigate the steps involved in measuring the expected value of a random variable as the weighted sum of all outcomes, with each outcome weighted by its probability. By the time you finish this course, you will be able to compute joint, marginal, and conditional probabilities and the expected value of a random variable, as well as effectively utilize the chain rule of probability.
Perks of Course
Certificate: Yes
CPD Points: 96
Compliance Standards: AICC
Raw Data to Insights: Data Ingestion & Statistical Analysis
Price on Request
55 minutes
Explore how statistical analysis can turn raw data into insights, and then examine how to use the data to improve business intelligence, in this 10-video course. Learn how to scrutinize and perform analytics on the collected data. The course explores several approaches for identifying values and insights from data by using various standard and intuitive principles, including data exploration and data ingestion, along with the practical implementation by using R. First, you will learn how to detect outliers by using R, and how to compare simple linear regression models, with and without outliers, to improve the quality of the data. Because today's data are available in diversified formats, with large volume and high velocity, this course next demonstrates how to use a variety of technologies: Apache Kafka, Apache NiFi, Apache Sqoop, and Wavefront (a program for simulating two-dimensional acoustic systems) to ingest data. Finally, you will learn how these tools can help users in data extraction, scalability, integration support, and security.
Perks of Course
Certificate: Yes
CPD Points: 53
Compliance Standards: AICC
Raw Data to Insights: Data Management & Decision Making
Price on Request
55 minutes
To master data science, it is important to turn raw data into insights. In this 12-video course, you will learn to apply and implement various essential data correction techniques, transformation rules, deductive correction techniques, and predictive modeling using critical data analytical approaches by using R. The key concepts in this course include: the capabilities and advantages of the application of data-driven decision making; loading data from databases using R; preparing data for analysis; and the concept of data correction, using the essential approaches of simple transformation rules and deductive correction, Next, examine implementing data correction using simple transformation rules and deductive correction; the various essential distributed data management frameworks used to handle big data; and the approach of implementing data analytics using machine learning. Finally, learn how to implement exploratory data analysis by using R; to implement predictive modeling by using machine learning; how to correct data with deductive correction; and how to analyze data in R and facilitate predictive modeling with machine learning.
Perks of Course
Certificate: Yes
CPD Points: 56
Compliance Standards: AICC
Scalable Data Architectures: Getting Started
Price on Request
50 minutes
Explore theoretical foundations of the need for and characteristics of scalable data architectures in this 8-video course. Learn to use data warehouses to store, process, and analyze big data. Key concepts covered here include how to recognize the need to scale architectures to keep up with needs for storage and processing of big data; how to identify characteristics of data warehouses ideally suiting them to tasks of big data analysis and processing; and how to distinguish between relational databases and data warehouses. Next, learn to recognize specific characteristics of systems meant for online transaction processing and online analytical processing, and how data warehouses are an example of online analytical processing (OLAP) systems. Then, learn to identify various components of data warehouses enabling them to work with varied sources, extract and transform big data, and generate reports of analysis operations efficiently. Finally, study features of Amazon Redshift enabling big data to be processed at scale; features of data warehouses, contrasted with those of relational databases; and two options available to scale compute capacity.
Perks of Course
Certificate: Yes
CPD Points: 52
Compliance Standards: AICC
Scalable Data Architectures: Using Amazon Redshift
Price on Request
55 minutes
Using a hands-on lab approach, explore how to use Amazon Redshift to set up and configure a data warehouse on the cloud in this 9-video course. Discover how to interact with Redshift service with both the console and Amazon Web Services (AWS) Command Line Interface (CLI). Key concepts covered here include how to use the Amazon Redshift Quick Launch feature to provision a data warehouse; provisioning a Redshift cluster with the default cluster; and tool configuration options for a Redshift cluster, and metrics available to optimize a cluster configuration. Next, learn how to create Identity and Access Management (IAM) roles on AWS that include necessary permissions to interact with Redshift and S3 services; to provision an IAM user that can connect to and interact with AWS using the CLI; and to install the AWS command-line interface to create and delete Redshift clusters. Then learn to use Redshift Query Editor to create tables, load data, and run queries; and learn features of Amazon Redshift and commands and configurations needed to work with Redshift by using the CLI.
Perks of Course
Certificate: Yes
CPD Points: 54
Compliance Standards: AICC
Scalable Data Architectures: Using Amazon Redshift & QuickSight
Price on Request
1 hour 15 minutes
In this 12-video course, explore the loading of data from an external source such as Amazon S3 into a Redshift cluster, as well as configuration of snapshots and resizing of clusters. Discover how to use Amazon QuickSight to visualize data. Key concepts covered in this course include using the AWS console to load data sets to Amazon S3 and then into a table provisioned on a Redshift cluster; running queries on data in a Redshift cluster with the query evaluation feature; and working with SQL Workbench to connect to and query data in a Redshift cluster. Learn how to disable automated snapshots for a Redshift cluster and configure a table to be excluded from snapshots; recover an individual table from the snapshot of an entire cluster; and create a security group rule enabling access from Amazon's QuickSight servers to a Redshift cluster. Next, configure Amazon QuickSight to load data from a table in a Redshift cluster for analysis; and use the QuickSight dashboard to generate a time series plot to visualize sales at a retailer over time.
Perks of Course
Certificate: Yes
CPD Points: 77
Compliance Standards: AICC
Securing Big Data Streams
Price on Request
1 hour
Learners can explore security risks related to modern data capture, data centers, and processing methods, such as streaming analytics, in this 13-video course. As the value of a company's data increases, the same data have become more and more valuable to hackers and other criminals. You will learn up-to-date techniques and tools employed to mitigate security risks, and best practices related to securing big data, including cloud data, trust, and encryption. Begin with an overview of common security concerns for big data and streaming data, as well as concerns related to NoSQL (non-structured query language), distributed processing frameworks, and flaws related to data mining and analytics. Then explore how to secure big data; explore streaming data and data in motion; and see how end-point devices are secured by using validation and filtering, as well as how to use encryption to secure data at rest. In the concluding exercise, practice what you have learned by describing key big data security concerns, key streaming data security concerns, and how end-point devices are secured.
Perks of Course
Certificate: Yes
CPD Points: 62
Compliance Standards: AICC
Simple Descriptive Statistics
Price on Request
1 hour 10 minutes
Along the career path to Data Science, a fundamental understanding of statistics and modeling is required. The goal of all modeling is generalizing as well as possible from a sample to the population of big data as a whole. In this 10-video Skillsoft Aspire course, learners explore the first step in this process. Key concepts covered here include the objectives of descriptive and inferential statistics, and distinguishing between the two; objectives of population and sample, and distinguishing between the two; and objectives of probability and non-probability sampling and distinguishing between them. Learn to define the average of a data set and its properties; the median and mode of a data set and their properties; and the range of a data set and its properties. Then study the inter-quartile range of a data set and its properties; the variance and standard deviation of a data set and their properties; and how to differentiate between inferential and descriptive statistics, the two most important types of descriptive statistics, and the formula for standard deviation.
Perks of Course
Certificate: Yes
CPD Points: 70
Compliance Standards: AICC
Simplifying Regression and Classification with Estimators
Price on Request
35 minutes
This 6-video course focuses on understanding Google's TensorFlow estimators, and showing learners how they simplify the task of building simple linear and logistic regression models for machine learning solutions. As a prerequisite, learners should have a basic understanding of ML (machine learning), and basic experience programming in Python. Though not required, familiarity with the Scikit-learn library and the Keras API will simplify the labs part of this course. First, you will learn how TensorFlow estimators abstract many of the details in creating a neural network, and you will then learn that you no longer need to define the type of neural network model, nor will you need to add definitions to layer. When using an estimator, learners only need to feed in training and validation data. In the course labs, you will build both a linear regression model and a classifier by using TensorFlow estimators. Finally, you will learn how to evaluate your model using the prebuilt methods available in the estimator.
Perks of Course
Certificate: Yes
CPD Points: 35
Compliance Standards: AICC
Spark for High-speed Big Data Analytics
Price on Request
45 minutes
Spark is an open-source, massively parallel, in-memory solution that allows you to run big data analytics pipelines at high speed. Use this course to learn how Apache Spark works and gain an understanding of its architecture. As you progress, investigate the industry-leading examples of Uber and Alibaba to recognize how Spark can add business value to data in many industry types. Moving along, compare the functionality of Spark and Hadoop in relation to use cases, identifying when using Spark is most advantageous. Finally, explore fundamental Spark characteristics, optimization techniques, and best practices. When you've completed this course, you'll have a solid theoretical understanding of how and when to use Apache Spark for specific big data analytics tasks.
Perks of Course
Certificate: Yes
CPD Points: 45
Compliance Standards: AICC
Streaming Data Architectures: An Introduction to Streaming Data in Spark
Price on Request
50 minutes
Learn the fundamentals of streaming data with Apache Spark. During this course, you will discover the differences between batch and streaming data. Observe the types of streaming data sources. Learn about how to process streaming data, transform the stream, and materialize the results. Decouple a streaming application from the data sources with a message transport. Next, learn about techniques used in Spark 1.x to work with streaming data and how it contrasts with processing batch data; how structured streaming in Spark 2.x is able to ease the task of stream processing for the app developer; and how streaming processing works in both Spark 1.x and 2.x. Finally, learn how triggers can be set up to periodically process streaming data; and the key aspects of working with structured streaming in Spark
Perks of Course
Certificate: Yes
CPD Points: 50
Compliance Standards: AICC
Streaming Data Architectures: Processing Streaming Data with Spark
Price on Request
50 minutes
Process streaming data with Spark, the analytic engine built on Hadoop. In this course, you will discover how to develop applications in Spark to work with streaming data and generate output. Topics include the following: Configure a streaming data source; Use Netcat and write applications to process the data stream; Learn the effects of using the Update mode on your stream processing application's output; Write a monitoring application that listens for new files added to a directory; Compare the append output with the update mode; Develop applications to limit files processed in each trigger; Use Spark's Complete mode for output; Perform aggregation operations on streaming data with the DataFrame API; Process streaming data with Spark SQL queries.
Perks of Course
Certificate: Yes
CPD Points: 52
Compliance Standards: AICC
Support Vector Machine (SVM) Math: A Conceptual Look at Support Vector Machines
Price on Request
1 hour
Simple to use yet efficient and reliable, support vector machines (SVMs) are supervised learning methods popularly used for classification tasks. This course uncovers the math behind SVMs, focusing on how an optimum SVM hyperplane for classification is computed. Explore the representation of data in a feature space, finding a hyperplane to separate the data linearly. Then, learn how to separate non-linear data. Investigate the optimization problem for SVM classifiers, looking at how the weights of the model can be adjusted during training to get the best hyperplane separating the data points. Furthermore, apply gradient descent to solve the optimization problem for SVMs. When you're done, you'll have the foundational knowledge you need to start building and applying SVMs for machine learning.
Perks of Course
Certificate: Yes
CPD Points: 59
Compliance Standards: AICC
Techniques for Big Data Analytics
Price on Request
35 minutes
Big data analytics provides a way to turn the vast amounts of data available in today's digital world into valuable insights. For this reason, big data analytics techniques have taken a central place in many businesses' IT infrastructure. These comprise complex processes and multiple stack layers that allow you to transform raw data into visualizations that demonstrate trends or other phenomena. Use this course to explore the basic principles and techniques of big data analytics in a business context. Go through each step of data processing to fully comprehend the big data analytics pipeline. Furthermore, explore various use cases of big data analytics through real-world examples. When you're done with this course, you'll have a foundational comprehension of some of the technologies behind big data and how these can drive business decisions for the better.
Perks of Course
Certificate: Yes
CPD Points: 34
Compliance Standards: AICC
Technology Landscape & Tools for Data Management
Price on Request
25 minutes
This Skillsoft Aspire course explores various tools you can utilize to get better data analytics for your organization. You will learn the important factors to consider when selecting tools, velocity, the rate of incoming data, volume, the storage capacity or medium, and the diversified nature of data in different formats. This course discusses the various tools available to provide the capability of implementing machine learning, deep learning, and to provide AI capabilities for better data analytics. The following tools are discussed: TensorFlow, Theano, Torch, Caffe, Microsoft cognitive tool, OpenAI, DMTK from Microsoft, Apache SINGA, FeatureFu, DL4J from Java, Neon, and Chainer. You will learn to use SCIKIT-learn, a machine learning library for Python, to implement machine learning, and how to use machine learning in data analytics. This course covers how to recognize the capabilities provided by Python and R in the data management cycle. Learners will explore Python; the libraries NumPy, SciPy, Pandas to manage data structures; and StatsModels. Finally, you will examine the capabilities of machine learning implementation in the cloud.
Perks of Course
Certificate: Yes
CPD Points: 26
Compliance Standards: AICC
The Four Vs of Data
Price on Request
40 minutes
The four Vs (volume, variety, velocity, and veracity) of big data and data science are a popular paradigm used to extract meaning and value from massive data sets. In this course, learners discover the four Vs, their purpose and uses, and how to extract value by using the four Vs. Key concepts covered here include the four Vs, their roles in big data analytics, and the overall principle of the four Vs; and ways in which the four Vs relate to each other. Next, study variety and data structure and how they relate to the four Vs; validity and volatility and how they relate to the four Vs; and how the four Vs should be balanced in order to implement a successful big data strategy. Learners are shown the various use cases of big data analytics and the four Vs of big data, and how the four Vs can be leveraged to extract value from big data. Finally, review the four Vs of big data analytics, their differences, and how balance can be achieved.
Perks of Course
Certificate: Yes
CPD Points: 39
Compliance Standards: AICC
The Math Behind Decision Trees: An Exploration of Decision Trees
Price on Request
1 hour 30 minutes
Decision trees are an effective supervised learning technique for predicting the class or value of a target variable. Unlike other supervised learning methods, they're well-suited to classification and regression tasks. Use this course to learn how to work with decision trees and classification, distinguishing between rule-based and ML-based approaches. As you progress through the course, investigate how to work with entropy, Gini impurity, and information gain. Practice implementing both rule-based and ML-based decision trees and leveraging powerful Python visualization libraries to construct intuitive graphical representations of decision trees. Upon completion, you'll be able to create, use, and share rule-based and ML-based decision trees.
Perks of Course
Certificate: Yes
CPD Points: 119
Compliance Standards: AICC
Training Neural Networks: Advanced Learning Algorithms
Price on Request
1 hour 40 minutes
This 15-video course explores how to design advanced machine learning algorithms by using training patterns, pattern association, the Hebbian learning rule, and competitive learning. First, learners examine the concepts and characteristics of online and offline training techniques in implementing artificial neural networks, and different training patterns in teaching inputs that are used in implementing artificial neural networks. You will learn to manage training samples, and how to use Google Colab to implement overfitting and underfitting scenarios by using baseline models. You will examine regularization techniques to use in training artificial neural networks. This course then demonstrates how to train previously-built neural network models using Python, and the prominent training algorithms to implement pattern associations. Next, learn the architecture and algorithm associated with learning vector quantization; the essential phases involved in implementing Hebbian learning; how to implement Hebbian learning rule using Python; and the steps involved in implementing competitive learning. Finally, you will examine prominent techniques to use to optimize neural networks, and how to debug neural networks.
Perks of Course
Certificate: Yes
CPD Points: 100
Compliance Standards: AICC
Training Neural Networks: Implementing the Learning Process
Price on Request
1 hour 40 minutes
In this 13-video course, learners can explore how to work with machine learning frameworks and Python to implement training algorithms for neural networks. You will learn the concept and characteristics of perceptrons, a single layer neural network that aggregates the weighted sum of inputs, and returns either zero or one, and neural networks. You will then explore some of the prominent learning rules that to apply in neural networks, and the concept of supervised and unsupervised learning. Learn several types of neural network algorithms, and several training methods. Next, you will learn how to prepare and curate data by using Amazon SageMaker, and how to implement an artificial neural network training process using Python, and other prominent and essential learning algorithms to train neural networks. You will learn to use Python to train artificial neural networks, and how to use Backpropagation in Keras to implement multilayer perceptrons or neural networks. Finally, this course demonstrates how to implement regularization in multilayer perceptrons by using Keras.
Perks of Course
Certificate: Yes
CPD Points: 98
Compliance Standards: AICC
Using Apache Spark for AI Development
Price on Request
35 minutes
Spark is a leading open-source cluster-computing framework that is used for distributed databases and machine learning. Although not primarily designed for AI, Spark allows you to take advantage of data parallelism and the large distributed systems used in AI development. AI practitioners should recognize when to use Spark for a particular application. In this course, you'll explore advanced techniques for working with Apache Spark and identify the key advantages of using Spark over other platforms. You'll define the meaning of resilient distributed databases (RDDs) and explore several workflows related to them. You'll move on to recognize how to work with a Spark DataFrame, identifying its features and use cases. Finally, you'll learn how to create a machine learning pipeline using Spark ML Pipelines.
Perks of Course
Certificate: Yes
CPD Points: 36
Compliance Standards: AICC
Using BigML: An Introduction to Machine Learning & BigML
Price on Request
1 hour 10 minutes
From self-driving cars to predicting stock prices, machine learning has an exciting range of applications. BigML, due to its ease of use, makes these algorithms widely accessible. This course outlines machine learning fundamentals and how these are applied in BigML. You'll start by examining various machine learning algorithm categories and the kinds of problems they're used to solve. You'll then investigate the classification problem and the process involved in training and evaluating such models. Next, you'll examine linear regression and how this can help predict a continuous value. Moving on, you'll explore the concept of unsupervised learning and its application in clustering, Principal Component Analysis (PCA), and generating associations. Finally, you'll recognize how all of this comes together when using BigML to significantly simplify the building and maintenance of your machine learning models.
Perks of Course
Certificate: Yes
CPD Points: 70
Compliance Standards: AICC
Using BigML: Building Supervised Learning Models
Price on Request
1 hour 30 minutes
The versatility of BigML allows you to build supervised learning models without much complexity. In this course, you'll practice constructing a selection of supervised learning models using BigML. You'll start by building an ensemble of decision trees to perform binary classification. Next, you'll build a linear regression model to predict the values of homes in a particular region. You'll then train and evaluate a logistic regression model to illustrate how it can be used to solve similar problems to those solved using ensemble methods. Another BigML capability you'll explore is building a time series plot to make various forecasts. In each demonstration, you'll delve into some optional configurations for the model being trained. Lastly, you'll use the OptiML feature to find the optimal model for your data.
Perks of Course
Certificate: Yes
CPD Points: 90
Compliance Standards: AICC
Using BigML: Getting Hands-on with BigML
Price on Request
1 hour 15 minutes
BigML not only provides ease-of-use, but it also offers flexibility in how you work with your data. This course serves as a hands-on introduction to BigML and its vast array of features. You'll start by exploring the different ways data can be loaded into the platform and how these can be transformed into datasets to train and test a machine learning model. You'll gain practical experience with some of the tools available to help you better understand your data - from histograms and scatterplots to visualizations of value distribution. Moving on, you'll build a fundamental classification model, a decision tree, which takes employee details and predicts whether they'll stay or leave in the next year. Finally, you'll investigate some possible configurations for this model.
Perks of Course
Certificate: Yes
CPD Points: 76
Compliance Standards: AICC
Using BigML: Unsupervised Learning
Price on Request
1 hour
BigML includes various unsupervised learning models used to gain insights into your data. These insights can help make pivotal business decisions or act as a starting point to build supervised learning models. In this course, you'll build several unsupervised learning models and analyze the results they produce. You'll start by creating clusters from a dataset and examining how data points within a cluster share similarities. You'll move on to uncover associations in a dataset about items purchased on an e-commerce platform. Next, you'll apply topic modeling to extract the topics discussed in a collection of texts. Following this, you'll transform a dataset containing multiple fields into a handful of principal components using Principal Component Analysis, or PCA. Finally, you'll explore the detection of anomalies in your dataset.
Perks of Course
Certificate: Yes
CPD Points: 60
Compliance Standards: AICC
Using Hive to Optimize Query Executions with Partitioning
Price on Request
1 hour
Continue to explore the versatility of Apache Hive, among today's most popular data warehouses, in this 10-video Skillsoft Aspire course. Learners are shown ways to optimize query executions, including the powerful technique of partitioning data sets. The hands-on course assumes previous work with Hive tables using the Hive query language and in processing complex data types, along with theoretical understanding of improving query performance by partitioning very large data sets. Demonstrations focus on basics of partitioning and how to create partitions and load data into them. Learners work with both Hive-managed tables and external tables to see how partitioning works for each; then watch navigating to the shell of the Hadoop master node, and creating new directories in the Hadoop file system. Observe dynamic partitioning of tables and how this simplifies loading of data into partitions. Finally, you explore how using multiple columns in a table can partition data within it. During this course, learners will acquire a sound understanding of how exactly large data sets can be partitioned into smaller chunks, improving query performance.
Perks of Course
Certificate: Yes
CPD Points: 60
Compliance Standards: AICC
Using Intelligent Information Systems in AI
Price on Request
50 minutes
The world of technology continues to transform at a rapid pace, with intelligent technology incorporated at every stage of the business process. Intelligent information systems (IIS) reduce the need for routine human labor and allow companies to focus instead on hiring creative professionals. In this course, you'll explore the present and future roles of intelligent informational systems in AI development, recognizing the current demand for IIS specialists. You'll list several possible IIS applications and learn about the roles AI and ML play in creating them. Next, you'll identify significant components of IIS and the purpose of these components. You'll examine how you would go about creating a self-driving vehicle using IIS components. Finally, you'll work with Python libraries to build high-level components of a Markov decision process.
Perks of Course
Certificate: Yes
CPD Points: 51
Compliance Standards: AICC
Using R Programming Structures: Object Systems
Price on Request
1 hour
R supports not one but multiple alternative object-oriented programming paradigms. These are known as object systems and constitute a relatively underutilized but incredibly powerful feature of the R language. This course will show you how to work effectively with object systems in R. You'll begin by identifying different object systems. You'll then examine how the S3 object system allows some features of object-oriented programming, albeit in a very different form from other OOP languages. You'll move to leverage the R5 object system, also known as the system of reference classes, to create classes and instantiate objects, specify member variables and methods, and initialize values of member functions. You'll also implement inheritance using the system of reference classes. When you're done with this course, you'll be able to utilize different object systems in your R programming projects.
Perks of Course
Certificate: Yes
CPD Points: 58
Compliance Standards: AICC
VBA: Building User Interfaces with Forms in VBA & Excel
Price on Request
1 hour 20 minutes
One of the many capabilities of Excel's VBA is building complex user interfaces within Excel. Part of this is the creation of complex forms for gathering input data. In this course, you'll learn how to create user forms using VBA in Excel and, in doing so, achieve user interfaces with complex controls like combo boxes and spinners. You'll also learn how to send emails from VBA while being mindful of security risks and the configuration required if the email provider is Gmail. You'll learn how to validate form input data, insert it into an Excel spreadsheet, and illustrate how such user input can be accepted using complex controls, such as radio buttons and checkboxes, and buttons that, when clicked, trigger the invocation of VBA subroutines.
Perks of Course
Certificate: Yes
CPD Points: 82
Compliance Standards: AICC
VBA: Getting Started with VBA in Excel
Price on Request
1 hour 55 minutes
Excel's VBA can be a powerful tool useful for a multitude of purposes if you know how to leverage its capabilities, debug issues, and mitigate for specific limitations. In this introductory course, you'll begin by using subroutines in VBA to perform operations. You'll then define functions and reference and edit cell ranges and Excel sheets with VBA. After that, you'll invoke subroutines with relative cell references, record macros in Excel, and debug macros in VBA. You'll insert columns and sheets from VBA and format cells based on a condition in VBA both manually and using a FormatConditions object. Finally, you'll illustrate how clearing formatting using a FormatConditions object will only clear formatting created using a FormatConditions object, not by using if-else conditionals.
Perks of Course
Certificate: Yes
CPD Points: 115
Compliance Standards: AICC
VBA: Leveraging VBA to Work with Charts, Stocks, & MS Access
Price on Request
1 hour 55 minutes
Leveraging VBA with Excel has many useful capabilities, including the creation and management of pivot tables and charts, and the automation of tasks in MS Access, the lightweight relational database. In this course, you'll learn how to create and edit pivot tables and different chart types, such as bar charts and line charts, using VBA in Excel. You'll then save these Excel charts as images, again using VBA. Next, you'll use Excel's stock datatype to access financial data. Moving on, you'll learn how to use VBA to automate tasks in MS Access, accept user input, validate it, and insert it into an MS Access database. You'll also learn how to run simple SQL queries against that database. Finally, you'll set up event handlers in Excel using VBA and illustrate their purpose.
Perks of Course
Certificate: Yes
CPD Points: 117
Compliance Standards: AICC
Viewing & Querying Complex Data with Hive
Price on Request
1 hour 10 minutes
Learners explore working with complex data types in Apache Hive in this Skillsoft Aspire course, which assumes previous work with Hive tables using the Hive query language, and comfort using a command-line interface or Hive client to run queries. Learners begin this 12-video, hands-on course by working with Hive tables whose columns are of complex data types (arrays, maps, and structs). Watch demonstrations of set operations and transforming complex types into tabular form with explode operation. Then use lateral views to add more data to exploded outputs. Course labs use the Beeline client; the instructor's Beeline terminal runs on the master node of a Hadoop cluster, provisioned on Google Cloud platform using its Dataproc service, and learner access is assumed to a Hadoop cluster and Beeline, on-premises or in the cloud. Finally, learners observe how to use views to aggregate contents of multiple columns. As the course concludes, you should be comfortable working with all types of data in Hive and performing analysis tasks on tables with both parameter types as well as complex data.
Perks of Course
Certificate: Yes
CPD Points: 72
Compliance Standards: AICC
Working with Files in Hadoop HDFS
Price on Request
45 minutes
In this Skillsoft Aspire course, learners will encounter basic Hadoop file system operations such as viewing the contents of directories and creating new ones. This 8-video course assumes good understanding of what Hadoop is, and how HDFS enables processing of big data in parallel by distributing large data sets across a cluster; learners should also be familiar with running commands from the Linux shell, with some fluency in basic Linux file system commands. Begin by working with files in various ways, including transferring files between a local file system and HDFS (Hadoop Distributed File System) and explore ways to create and delete files on HDFS. Then examine different ways to modify files on HDFS. After exploring the distributed computing concept, prepare to begin working with HDFS in a production setting. In the closing exercise, write a command to create a directory/data/products/files on HDFS, for which data/products may not exist; list two commands for two copy operations-one from local file system to HDFS, and another for reverse transfer, from HDFS to local host.
Perks of Course
Certificate: Yes
CPD Points: 47
Compliance Standards: AICC
Working with Google BERT: Elements of BERT
Price on Request
1 hour 10 minutes
Adopting the foundational techniques of natural language processing (NLP), together with the Bidirectional Encoder Representations from Transformers (BERT) technique developed by Google, allows developers to integrate NLP pipelines into their projects efficiently and without the need for large-scale data collection and processing. In this course, you'll explore the concepts and techniques that pave the foundation for working with Google BERT. You'll start by examining various aspects of NLP techniques useful in developing advanced NLP pipelines, namely, those related to supervised and unsupervised learning, language models, transfer learning, and transformer models. You'll then identify how BERT relates to NLP, its architecture and variants, and some real-world applications of this technique. Finally, you'll work with BERT and both Amazon review and Twitter datasets to develop sentiment predictors and create classifiers.
Perks of Course
Certificate: Yes
CPD Points: 68
Compliance Standards: AICC
Working With Microsoft Cognitive Toolkit (CNTK)
Price on Request
50 minutes
Microsoft Cognitive Toolkit (CNTK) is an open source framework for distributed deep learning suitable for commercial applications. It's primarily used to develop neural networks but can also be used for machine learning and cognitive computing. It supports multiple languages and can easily be used in the cloud. These factors make CNTK a good fit for various AI projects. In this course, you'll explore the basic concepts required to work with Microsoft CNTK. You'll compare other frameworks with CNTK, examine the process of creating machine learning and deep learning models with CNTK, and learn how it can be used with several cloud services. You'll move on to learn where to access CNTK documentation, community, and installation guidelines. Finally, you'll use CNTK to predict diabetes using retina scans.
Perks of Course
Certificate: Yes
CPD Points: 51
Compliance Standards: AICC
Working with Neo4j Bloom: Analyzing Graphs
Price on Request
1 hour 35 minutes
The Neo4j Bloom application allows those working with graphs to query graph databases sans the need to write code or a Cypher query. Use this course to learn how to use Neo4j Bloom to easily query a Neo4j graph database and achieve rich visualizations of query results. Practice loading data onto a Neo4j database from CSV files. Work with Neo4j Bloom to run simple search queries on patterns in the nodes and relationships. Moving along, explore some of the key features of the Bloom interface, including creating scenes showcasing the results of search queries and working with scenes using card lists. Learn to edit graphs using Bloom. And finally, work with the Bloom interface to find connections between nodes. Upon completion, you'll be able to use Neo4j Bloom to analyze a Neo4j database and give a rich visual representation to this analysis.
Perks of Course
Certificate: Yes
CPD Points: 94
Compliance Standards: AICC
Working With the Keras Framework
Price on Request
50 minutes
Keras provides a quick way to implement, train, and evaluate robust neural networks in Python. Using Keras for AI development for prototyping AI is standard practice and AI practitioners need to know why and how to use Keras for particular AI implementations. In this course, you'll explore advanced techniques for working with the Keras framework. You'll recognize how Keras is different from other AI frameworks and identify cases in which it is advantageous to use Keras. You'll examine the functionality of the Keras Sequential model and Functional API and the role of multiple deep learning layers present in Keras. Finally, you will work with practical AI projects developed using Keras and troubleshoot common problems related to model training and evaluation.
Perks of Course
Certificate: Yes
CPD Points: 50
Compliance Standards: AICC