Data / ML / AI - II (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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26 hours 15 minutesof self-paced video lessons -
27 Programscrafting your path to success -
Completion Certificateawarded on course completion
Data Pipeline: Using Frameworks for Advanced Data Management
Price on Request
30 minutes
Discover how to implement data pipelines using Python Luigi, integrate Spark and Tableau to manage data pipelines, use Dask arrays, and build data pipeline visualization with Python in this 10-video course. Begin by learning about features of Celery and Luigi that can be used to set up data pipelines, then how to implement Python Luigi to set up data pipelines. Next, turn to working with Dask library, after listing the essential features provided by Dask from the perspective of task scheduling and big data collections. Learn about implementation of Dask arrays to manage NumPy application programming interfaces (APIs). Explore frameworks that can be used to implement data exploration and visualization in data pipelines. Integrate Spark and Tableau to manage data pipelines. Move on to streaming data visualization with Python, using Python to build visualizations for streaming data. Then learn about the data pipeline building capabilities provided by Kafka, Spark, and PySpark. The concluding exercise involves setting up Luigi to implement data pipelines, Spark and Tableau integration, and building pipelines with Python.
Perks of Course
Certificate: Yes
CPD Points: 32
Compliance Standards: AICC
Data Recommendation Engines
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1 hour 5 minutes
This 13-video course explores recommendation engines, systems which provide various users with items or products that they may be interested in by observing their previous purchasing, search, and behavior histories. They are used in many industries to help users find or explore products and content; for example, to find movies, news, insurance, and a myriad of other products and services. Learners will examine the three main types of recommendation systems: item-based, user-based or collaborative, and content-based. The course next examines how to collect data to be used for learning, training, and evaluation. You will learn how to use RStudio, an open-source IDE (integrated development environment) to import, filter, and massage data into data sets. Learners will create an R function that will give a score to an item based on other user ratings and similarity scores. You will learn to use R to create a function called compareUsers, to create an item-to-item similarity or content score. Finally, learn to validate and score by using the built-in R function RMSE (root mean square error).
Perks of Course
Certificate: Yes
CPD Points: 64
Compliance Standards: AICC
Data Research Exploration Techniques
Price on Request
50 minutes
This course explores EDA (exploratory data analysis) and data research techniques necessary to communicate with data management professionals involved in application, implementation, and facilitation of the data research mechanism. You will examine EDA as an important way to analyze extracted data by applying various visual and quantitative methods. In this 10-video course, learners acquire data exploration techniques to derive different data dimensions to derive value from the data. You will learn proper methodologies and principles for various data exploration techniques, analysis, decision-making, and visualizations to gain valuable insights from the data. This course covers how to practically implement data exploration by using R random number generator, Python, linear algebra, and plots. You will use EDA to build learning sets which can be utilized by various machine learning algorithms or even statistical modeling. You will learn to apply univariate visualization, and to use multivariate visualizations to identify the relationship among the variables. Finally, the course explores dimensionality reduction to apply different dimension reduction algorithms to deduce the data in a state which is useful for analytics.
Perks of Course
Certificate: Yes
CPD Points: 49
Compliance Standards: AICC
Data Research Statistical Approaches
Price on Request
40 minutes
This 12-video course explores implementation of statistical data research algorithms using R to generate random numbers from standard distribution, and visualizations using R to graphically represent the outcome of data research. You will learn to apply statistical algorithms like PDF (probability density function), CDF (cumulative distribution function), binomial distribution, and interval estimation for data research. Learners become able to identify the relevance of discrete versus continuous distribution in simplifying data research. This course then demonstrates how to plot visualizations by using R to graphically predict the outcomes of data research. Next, learn to use interval estimation to derive an estimate for an unknown population parameter, and learn to implement point and interval estimation by using R. Learn data integration techniques to aggregate data from different administrative sources. Finally, you will learn to use Python libraries to create histograms, scatter, and box plot; and use Python to implement missing values and outliers. The concluding exercise involves loading data in R, generating a scatter chart, and deleting points outside the limit of x vector and y vector.
Perks of Course
Certificate: Yes
CPD Points: 42
Compliance Standards: AICC
Data Research Techniques
Price on Request
30 minutes
To master data science, you must learn the techniques surrounding data research. In this 10-video course, learners will discover how to apply essential data research techniques, including JMP measurement, and how to valuate data by using descriptive and inferential methods. Begin by recalling the fundamental concept of data research that can be applied on data inference. Then learners look at steps that can be implemented to draw data hypothesis conclusions. Examine values, variables, and observations that are associated with data from the perspective of quantitative and classification variables. Next, view the different scales of standard measurements with a critical comparison between generic and JMP models. Then learn about the key features of nonexperimental and experimental research approaches when using real-time scenarios. Compare differences between descriptive and inferential statistical analysis and explore the prominent usage of different types of inferential tests. Finally, look at the approaches and steps involved in the implementation of clinical data research and sales data research using real-time scenarios. The concluding exercise involves implementing data research.
Perks of Course
Certificate: Yes
CPD Points: 32
Compliance Standards: AICC
Data Rollbacks: Transaction Management & Rollbacks in NoSQL
Price on Request
30 minutes
During this 7-video course, learners will explore differences between transaction management by using NoSQL and MongoDB and discover how to implement change data capture in databases and NoSQL. The first tutorial compares the transaction management architecture and capabilities of NoSQL and SQL. Then you will learn how to recognize the transaction management capabilities of MongoDB, along with its impact on consistency and availability. Next, learners will explore how to implement multi-document transaction management by using replica set in MongoDB. Then the course moves on to examine change data capture, which is the process of capturing the change, and learn about the essential SQL Server change data capture features. You will examine the features of change stream in MongoDB, which leads on to creating change streams to enable real-time data change streaming for applications using MongoDB. To conclude the course, an exercise on MongoDB transactions and change streams compares the transaction management architecture and capabilities of NoSQL and SQL.
Perks of Course
Certificate: Yes
CPD Points: 28
Compliance Standards: AICC
Data Rollbacks: Transaction Rollbacks & Their Impact
Price on Request
35 minutes
In this 9-video course, you will explore the data concepts of transactions, transaction management policies, and rollbacks. Discover how to implement transaction management and rollbacks by using SQL Server. Begin by learning about the concept and characteristics of the rollback process and its impact on transactions. Then take a look at various states of transactions, and prominent types of transactions along with their essential features (distributed and compensating transactions). Moving on, learn about implementing transaction management, along with certain essential elements like commit savepoint and release savepoint using SQL server. Learners recall the various transaction log operations and their characteristics (transaction recovery and transaction replication). You will learn to recognize the Deadlock Management capabilities and features provided by SQL server by using lock monitors and trace. Examine the essential rollback mechanism adopted by SQL server, then see how the SQL server is used to roll back databases to a specific point in time. A concluding exercise involves implementing transaction management and rollbacks by using SQL server.
Perks of Course
Certificate: Yes
CPD Points: 35
Compliance Standards: AICC
Data Silos, Lakes, & Streams Introduction
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1 hour 20 minutes
This 11-video course discusses the transition of data warehousing to cloud-based solutions using the AWS (Amazon Web Services) cloud platform. You will examine various implications involved in storing different types of data from different sources within an organization. You will need to be familiar with provisioning and working with resources on the cloud, basic big data architecture, distributed systems, using shell commands, and a Linux terminal prompt. You will learn that an organization may have data silos which may prevent access to other teams within an organization. You will learn how to use data lakes, a centralized repository to store data at scale, and as a viable solution to data silos that might exist within an organization. You will learn the difference between a data lake which stores all kinds of raw data in a native format before the data has been processed, and a data warehouse which contains data that can be used so directly to generate business insights. Finally, this course demonstrates storing data with AWS Redshift data warehouse.
Perks of Course
Certificate: Yes
CPD Points: 79
Compliance Standards: AICC
Data Sources: Implementing Edge Data on the Cloud
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1 hour 30 minutes
To become proficient in data science, users have to understand edge computing. This is where data is processed near the source or at the edge of the network while in a typical cloud environment, data processing happens in a centralized data storage location. In this 7-video course, learners will explore the implementation of IoT (Internet of Things) on prominent cloud platforms like AWS (Amazon Web Services) and GCP (Google Cloud Platform). Discover how to work with IoT Device Simulator and generate data streams with MQTT (Message Queuing Telemetry Transport). You will next examine the approaches and steps involved in setting up AWS IoT Greengrass, and the essential components of GCP IoT Edge. Then learn how to connect a web application to AWS IoT by using MQTT over WebSockets. The next tutorial demonstrates the essential approach of using IoT Device Simulator, then on to generating streams of data by using the MQTT messaging protocol. The concluding exercise involves creating a device type, a user, and a device by using IoT Device Simulator.
Perks of Course
Certificate: Yes
CPD Points: 30
Compliance Standards: AICC
Data Sources: Integration from the Edge
Price on Request
40 minutes
In this 11-video course, you will examine the architecture of IoT (Internet of Things) solutions and the essential approaches of integrating data sources. Begin by examining the required elements for deploying IoT solutions and its prominent service categories. Take a look at the capabilities provided and the maturity models of IoT solutions. Explore the critical design principles that need to be implemented when building IoT solutions and the cloud architectures of IoT from the perspective of Microsoft Azure, Amazon Web Services, and GCP (Google Cloud Platform). Compare the features and capabilities provided by the MQTT (Message Queuing Telemetry Transport) and XMPP (Extensible Messaging and Presence Protocol) protocols for IoT solutions. Identify key features and applications that can be implemented by using IoT controllers; learn to recognize the concept of IoT data management and the applied lifecycle of IoT data. Examine the list of essential security techniques that can be implemented to secure IoT solutions. The concluding exercise focuses on generating data streams.
Perks of Course
Certificate: Yes
CPD Points: 39
Compliance Standards: AICC
Database-as-a-Service with Neo4j: The AuraDB Cloud Database Service
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1 hour
The Neo4j AuraDB cloud database service enables developers creating graph applications to focus on the same and not bother about database administration. Take this course to create and load an AuraDB database and integrate it with various tools in the Neo4j ecosystem. Practice setting up a free AuraDB account and provisioning an empty Neo4j cloud database. Learn how to migrate data to the database. Then, analyze data in an AuraDB database with the web-based Neo4j Bloom application. Moving along, practice writing a Python application to query an AuraDB database. Work on connecting with and running queries on the database using Cypher Shell. Upon completion, you'll be able to work with the Neo4j AuraDB cloud tool in order to simplify database administration for graph applications.
Perks of Course
Certificate: Yes
CPD Points: 59
Compliance Standards: AICC
Datasets in R: Joining & Visualizing Data
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45 minutes
Data for the same entity is often stored in multiple locations. Your analysis may require bringing this data together in a single location. Doing this forms a core part of data preprocessing. Another core task is recognizing the relationships in your data. In this course, you'll practice methods to merge data to prepare for statistical and predictive modeling and identify relationships in your data using charts and graphs. You'll combine data in different data frames (or tibbles) based on the values in common columns. You'll use the merge() function to perform join operations and implement joins using functions from the tidyverse. You'll also examine the plotting systems available in R and use the plot() functionality and the ggplot2 package to visualize and explore your data. Upon completion of this course, you'll be able to combine your data in a meaningful way and uncover data relationships.
Perks of Course
Certificate: Yes
CPD Points: 46
Compliance Standards: AICC
Datasets in R: Loading & Saving Data
Price on Request
1 hour 45 minutes
Transforming and manipulating massive amounts of data is crucial for all organizations. The R programming language offers a plethora of packages to load, explore, manipulate, and transform data. R is ideal for data analysis, mutation, and cleaning, making it a choice language for statisticians and data scientists. In this course, you'll learn how to write R script files using the RStudio environment. You'll use different panes to debug and evaluate your R program, import data in various file formats, and access files embedded in an R package and stored on your machine. Additionally, you'll learn how to export data to different file formats. Once you've completed this course, you'll know how to work R using RStudio, import and export data in R, and perform simple data transformation and exploration operations.
Perks of Course
Certificate: Yes
CPD Points: 104
Compliance Standards: AICC
Datasets in R: Selecting, Filtering, Ordering, & Grouping Data
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1 hour 35 minutes
Data analysis often requires performing a series of complex transformations. R makes this hassle-free via the forward pipe operator for chaining operations, data selection and filtering based on conditional operations, and grouping and aggregating options to compute summaries. Learn how to carry out all these operations in this course. Task you'll carry out include using logical and relational operators to perform conditional filtering, sampling records at random, and computing the top N records based on values in a variable. You'll also learn to use the forward pipe operator in the magrittr package and tibbles, the next-generation data frame, to store and transform your data. You'll round this course off by performing ordering, grouping, and aggregations on your data. When you're finished, you'll have a solid grasp of complex operations on data frames and be able to apply these concepts using the R programming language.
Perks of Course
Certificate: Yes
CPD Points: 94
Compliance Standards: AICC
Datasets in R: Transforming Data
Price on Request
2 hour
Organizations store data in various ways. The R programming language offers a straightforward interface to work with data in relational databases and transform it to the format you need for analysis. In this course, you'll learn how to connect to relational databases using the APIs provided in the Database Interface package (DBI) in R. You'll connect to SQLite data and perform create, read, update, and delete (CRUD) operations on your database tables. You'll also use R functions to mutate and transform data. You'll practice renaming columns, changing variable types, and creating new columns from derived data. You'll examine the tidyverse universe of data science packages and work with data in the wide and long formats. Once you've completed this course, you'll have a strong foundation in basic data manipulation and transformation using the R programming language.
Perks of Course
Certificate: Yes
CPD Points: 118
Compliance Standards: AICC
Developing a Basic MapReduce Hadoop Application
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1 hour 15 minutes
In this Skillsoft Aspire course, discover how to use Hadoop's MapReduce; provision a Hadoop cluster on the cloud; and build an application with MapReduce to calculate word frequencies in a text document. To start, create a Hadoop cluster on the Google Cloud Platform using its Cloud Dataproc service; then work with the YARN Cluster Manager and HDFS (Hadoop Distributed File System) NameNode web applications that come packaged with Hadoop. Use Maven to create a new Java project for the MapReduce application, and develop a mapper for word frequency application. Create a Reducer for the application that will collect Mapper output and calculate word frequencies in input text files, and identify configurations of MapReduce applications in the Driver program and the project's pom.xml file. Next, build the MapReduce word frequency application with Maven to produce a jar file and prepare for execution from the master node of the Hadoop cluster. Finally, run the application and examine outputs generated to get word frequencies in the input text document. The exercise involves developing a basic MapReduce application.
Perks of Course
Certificate: Yes
CPD Points: 73
Compliance Standards: AICC
Distance-based Models: Implementing Distance-based Algorithms
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1 hour 10 minutes
Knowing the math behind machine learning (ML) opens up many exciting avenues. There are vast amounts of ML algorithms you could learn. However, the distance-based algorithms K Nearest Neighbors and K-means clustering are arguably the most popular due to their simplicity and efficacy. In this course, practice building a classification model using the K Nearest Neighbors algorithm. Build upon this algorithm to perform regression. Then, perform a clustering operation by implementing the K-means algorithm. And in doing so, explore the techniques involved in converging the centroids towards their optimal positions. Upon completion, you'll be able to perform classification, regression, and clustering using the KNN and K-means algorithms.
Perks of Course
Certificate: Yes
CPD Points: 69
Compliance Standards: AICC
Distance-based Models: Overview of Distance-based Metrics & Algorithms
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1 hour 10 minutes
Machine learning (ML) is widely used across all industries, meaning engineers need to be confident in using it. Pre-built libraries are available to start using ML with little knowledge. However, to get the most out of ML, it's worth taking the time to learn the math behind it. Use this course to learn how distances are measured in ML. Investigate the types of ML problems distance-based models can solve. Examine different distance measures, such as Euclidean, Manhattan, and Cosine. Learn how the distance-based ML algorithms K Nearest Neighbors (KNN) and K-means work. Lastly, use Python libraries and various metrics to compute the distance between a pair of points. Upon completion, you'll have a solid foundational knowledge of the mechanisms behind distance-based machine learning algorithms.
Perks of Course
Certificate: Yes
CPD Points: 69
Compliance Standards: AICC
Elements of an Artificial Intelligence Architect
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25 minutes
An Artificial Intelligence (AI) Architect works and interacts with various groups in an organization, including IT Architects and IT Developers. It is important to differentiate between the work activities performed by these groups and how they work together. This course will introduce you to the AI Architect role. You'll discover what the role is, why it's important, and who the architect interacts with on a daily basis. We will also examine and categorize their daily work activities and will compare those activities with those of an IT Architect and an IT Developer. The AI Architect helps many groups within the organization, and we will examine their activities within those groups as well. Finally, we will highlight the roles the AI Architect plays in the organizations which they are a member of.
Perks of Course
Certificate: Yes
CPD Points: 26
Compliance Standards: AICC
Essential Maths: Exploring Linear Algebra
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1 hour 45 minutes
Linear algebra comes in handy when we need to work with a set of points represented in multi-dimensional space. Use this course to explore how systems of linear functions and equations can be represented using linear algebra. Examine how to define and compute the addition, scalar multiplication, dot product, and cross product operations on vectors, and discover how these operations are required while working with matrices. This course will also help you explore matrix multiplication, the inverse and transpose of a matrix, and computing the determinant of a matrix. By the time you finish this course, you will be able to express a system of linear functions as a matrix and perform fundamental operations on matrices, including matrix multiplication and the computation of inverses and determinants.
Perks of Course
Certificate: Yes
CPD Points: 105
Compliance Standards: AICC
Evaluating Current and Future AI Technologies and Frameworks
Price on Request
40 minutes
Solid knowledge of the AI technology landscape is fundamental in choosing the right tools to use as an AI Architect. In this course, you'll explore the current and future AI technology landscape, comparing the advantages and disadvantages of common AI platforms and frameworks. You'll move on to examine AI libraries and pre-trained models, distinguishing their advantages and disadvantages. You'll then classify AI datasets and see a list of dataset topics. Finally, You'll learn how to make informed decisions about which AI technology is best suited to your projects.
Perks of Course
Certificate: Yes
CPD Points: 39
Compliance Standards: AICC
Filtering Data Using Hadoop MapReduce
Price on Request
1 hour
Extracting meaningful information from a very large dataset can be painstaking. In this Skillsoft Aspire course, learners examine how Hadoop's MapReduce can be used to speed up this operation. In a new project, code the Mapper for an application to count the number of passengers in each Titanic class in the input data set. Then develop a Reducer and Driver to generate final passenger counts in each Titanic class. Build the project by using Maven and run on Hadoop master node to check that output correctly shows passenger class numbers. Apply MapReduce to filter only surviving Titanic passengers from the input data set. Execute the application and verify that filtering has worked correctly; examine job and output files with YARN cluster manager and HDFS (Hadoop Distributed File System) NameNode web User interfaces. Using a restaurant app's data set, use MapReduce to obtain the distinct set of cuisines offered. Build and run the application and confirm output with HDFS from both command line and web application. The exercise involves filtering data by using MapReduce.
Perks of Course
Certificate: Yes
CPD Points: 58
Compliance Standards: AICC
Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling
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35 minutes
Explore artificial neural networks (ANNs), their essential components, tools, and frameworks for their implementation in machine learning solutions. In this 9-video course, you will discover recurrent neural networks (RNNs) and how they are implemented. Key concepts covered here include perceptrons and the computational role they play in ANNs; learning features and characteristics of ANNs and how components are used to build a model; and learning prominent tools and frameworks used to implement sequence models and ANNs. Next, you will learn about sequence modeling as it pertains to language models; RNNs and their capabilities and components; and how to specify RNN types and their implementation features. Learners will then explore the concept of linear and nonlinear functions and classify how they are used with perceptrons; explore the concept of backpropagation and usage of backpropagation algorithm in neural networks; and examine the concept of activation functions and how linear and nonlinear activations are utilized in neural networks. Finally, you will see how to implement perceptrons with Python, and how to use modeling tools and architectures and applications of sequence models.
Perks of Course
Certificate: Yes
CPD Points: 36
Compliance Standards: AICC
Fundamentals of Sequence Model: Language Model & Modeling Algorithms
Price on Request
20 minutes
In this 7-video course, learners can explore the concepts of language modeling, natural language processing (NLP), and sequence generation for NLP. Prominent machine learning modeling algorithms such as vanishing gradient problem, gated recurrent units (GRUs), and long short-term memory (LSTM) network are also covered. Key concepts studied in this course include language models, one of the most important parts of NLP. and how to implement NLP along with its essential components; learning the process and approach of generating sequence for NLP; and vanishing gradient problem implementation approaches to overcome the problem of taking longer times to achieve convergence. Then, learn about features and characteristics of GRUs used to resolve issues with vanishing gradient problems, and learn the problems and drawbacks of implementing short-term memory and LSTM as modeling solutions. In the concluding exercise, learners will review the essential components and prominent applications of language modeling and specify some of the solutions for vanishing gradient problems.
Perks of Course
Certificate: Yes
CPD Points: 18
Compliance Standards: AICC
Getting Started with Hive
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55 minutes
This 9-video Skillsoft Aspire course focuses solely on theory and involves no programming or query execution. Learners begin by examining what a data warehouse is, and how it differs from a relational database, important because Apache Hive is primarily a data warehouse, despite giving a SQL-like interface to query data. Hive facilitates work on very large data sets, stored as files in the Hadoop Distributed File System, and lets users perform operations in parallel on data in these files by effectively transforming Hive queries into MapReduce operations. Next, you will hear about types of data and operations which data warehouses and relational databases handle, before moving on to basic components of the Hadoop architecture. Finally, the course discusses features of Hive making it popular among data analysts. The concluding exercise recalls differences between online transaction processing and online analytical processing systems, asking learners to identify Hadoop's three major components; list Hadoop offerings on three major cloud platforms (AWS, Microsoft Azure, and Google Cloud Platform); and list benefits of Hive for data analysts.
Perks of Course
Certificate: Yes
CPD Points: 55
Compliance Standards: AICC
Getting Started with Neural Networks: Biological & Artificial Neural Networks
Price on Request
1 hour
Learners can explore fundamental concepts of biological and artificial neural networks, computational models that can be implemented with neural networks, and how to implement neural networks with Python, in this 12-video course. Begin with a look at characteristics of machine learning biological neural networks that inspired artificial neural networks. Then explore components of biological neural networks and the signal processing mechanism. Next, take a look at the essential components of the structure of artificial neural networks; learn to recognize the layered architecture of neural networks; and observe how to classify various computational models that can be implemented by using neural networks paradigm. Examine neurons connectivity, by describing the interconnection between neurons involving weights and fixed weights. This leads on to threshold functions in neural networks and the basic logic gates of AND, OR, and XNOR. Implement neural networks by using Python and the core libraries provided by Python for neural networks; create a neural network model using Python, Keras, and TensorFlow, and finally, view prominent neural network use cases. The concluding exercise involves implementing neural networks.
Perks of Course
Certificate: Yes
CPD Points: 58
Compliance Standards: AICC
Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms
Price on Request
45 minutes
Discover the basics of perceptrons, including single- layer and multilayer, and the roles of linear and nonlinear functions in this 10-video course. Learners will explore how to implement perceptrons and perceptron classifiers by using Python for machine learning solutions. Key concepts covered in this course include perceptrons, single-layer and multilayer perceptrons, and the computational role they play in artificial neural networks; learning the algorithms that can be used to implement single-layer perceptron training models; and exploring multilayer perceptrons and illustrating the algorithmic difference from single-layer perceptrons. Next, you will learn to classify the role of linear and nonlinear functions in perceptrons; learn how to implement perceptrons by using Python; and learn approaches and benefits of using the backpropagation algorithm in neural networks. Then learn the uses of linear and nonlinear activation functions in artificial neural networks; learn to implement a simple perceptron classifier using Python; and learn the benefits of using the backpropagation algorithm in neural networks and implement perceptrons and perceptron classifiers by using Python.
Perks of Course
Certificate: Yes
CPD Points: 44
Compliance Standards: AICC