Data / ML / AI - I (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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47 hours 5 minutesof self-paced video lessons -
47 Programscrafting your path to success -
Completion Certificateawarded on course completion
Advanced Functionality of Microsoft Cognitive Toolkit (CNTK)
Price on Request
45 minutes
Microsoft Cognitive Toolkit provides powerful machine learning and deep learning algorithms for developing AI. Knowing which problems are easier to solve using Microsoft CNTK over other frameworks helps AI practitioners decide on the best software stack for a given application. In this course, you'll explore advanced techniques for working with Microsoft CNTK and identify which cases benefit most from MS CNTK. You'll examine how to load and use external data using CNTK and how to use its imperative and declarative APIs. You'll recognize how to carry out common AI development tasks using CNTK, such as working with epochs and batch sizes, model serialization, model visualization, feedforward neural networks, and machine learning model evaluation. Finally, you'll implement a series of practical AI projects using Python and MS CNTK.
Perks of Course
Certificate: Yes
CPD Points: 47
Compliance Standards: AICC
Advanced Operations Using Hadoop MapReduce
Price on Request
50 minutes
In this Skillsoft Aspire course, explore how MapReduce can be used to extract the five most expensive vehicles in a data set, then build an inverted index for the words appearing in a set of text files. Begin by defining a vehicle type that can be used to represent automobiles to be stored in a Java PriorityQueue, then configure a Mapper to use a PriorityQueue to store the five most expensive automobiles it has processed from the dataset. Learn how to use a PriorityQueue in the Reducer of the application to receive the five most expensive automobiles from each mapper and write the top five automobiles overall to the output, then execute the application to verify the results. Next, explore how you can utilize the MapReduce framework in order to generate an inverted index and configure the Reducer and Driver for the inverted index application. This leads on to running the application and examining the inverted index on HDFS (Hadoop Distributed File System). The concluding exercise involves advanced operations using MapReduce.
Perks of Course
Certificate: Yes
CPD Points: 48
Compliance Standards: AICC
Advanced Reinforcement Learning: Implementation
Price on Request
1 hour 35 minutes
In this 11-video course, learners can examine the role of reward and discount factors in reinforcement learning, as well as the multi-armed bandit problem and approaches to solving it for machine learning. You will begin by learning how to install the Markov Decision Policy (MDP) toolbox and implement the Discounted Markov Decision Process using the policy iteration algorithm. Next, examine the role of reward and discount factors in reinforcement learning, and the multi-armed bandit problem and solutions. Learn about dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of Bellman equation. Then learners will explore reinforcement learning agent components and applications; work with reinforcement learning agents using Keras and OpenAI Gym; describe reinforcement learning algorithms and the reinforcement learning taxonomy defined by OpenAI; and implement deep Q-learning with Keras. Finally, observe how to train deep neural networks (DNN) with reinforcement learning for time series forecasting. In the closing exercise, you will recall approaches for resolving the multi-armed bandit problem, list reinforcement learning agent components, and implement deep Q-learning by using Keras and OpenAI Gym.
Perks of Course
Certificate: Yes
CPD Points: 94
Compliance Standards: AICC
Advanced Reinforcement Learning: Principles
Price on Request
1 hour 10 minutes
This 11-video course delves into machine learning reinforcement learning concepts, including terms used to formulate problems and workflows, prominent use cases and implementation examples, and algorithms. Learners begin the course by examining what reinforcement learning is and the terms used to formulate reinforcement learning problems. Next, look at the differences between machine learning and reinforcement learning by using supervised and unsupervised learning. Explore the capabilities of reinforcement learning, by looking at use cases and implementation examples. Then learners will examine reinforcement learning workflow and reinforcement learning terms; reinforcement learning algorithms and their features; and the Markov Decision Process, its variants, and the steps involved in the algorithm. Take a look at the Markov Reward Process, focusing on value functions for implementing the Markov Reward Process, and also the capabilities of the Markov Decision Process toolbox and the algorithms that are implemented within it. The concluding exercise involves recalling reinforcement learning terms, describing implementation approaches, and listing the Markov Decision Process algorithms.
Perks of Course
Certificate: Yes
CPD Points: 72
Compliance Standards: AICC
Apache Spark Getting Started
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1 hour 5 minutes
Explore the basics of Apache Spark, an analytics engine used for big data processing. It's an open source, cluster computing framework built on top of Hadoop. Discover how it allows operations on data with both its own library methods and with SQL, while delivering great performance. Learn the characteristics, components, and functions of Spark, Hadoop, RDDS, the spark session, and master and worker notes. Install PySpark. Then, initialize a Spark Context and Spark DataFrame from the contents of an RDD and a DataFrame. Configure a DataFrame with a map function. Retrieve and transform data. Finally, convert Spark and Pandas DataFrames and vice versa.
Perks of Course
Certificate: Yes
CPD Points: 66
Compliance Standards: AICC
Applied Data Analysis
Price on Request
1 hour 25 minutes
In this 14-video course, learners discover how to perform data analysis by using Anaconda Python R, and related analytical libraries and tools. Begin by learning how to install and configure Python with Anaconda, and how R is installed by using Anaconda. Jupyter Notebook will be launched to explore data. Next, learn about the import and export of data in Python, and how to read data from, write data to files with Python Pandas library, and import and export data in R. Learn to recognize and handle missing data in R and to use the Dplyr package to transform data in R. Then learners examine Python data analysis libraries NumPy and Pandas. Next, perform exploratory data analysis in R by using mean, median, and mode. Discover how to use the Python data analysis library Pandas to analyze data and how to use the ggplot2 library to visualize data with R. Learn about Pandas built-in data visualization tools to visualize data by using Python. The closing exercise deals with performing data analysis with R and Python.
Perks of Course
Certificate: Yes
CPD Points: 84
Compliance Standards: AICC
Applied Deep Learning: Generative Adversarial Networks and Q-Learning
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45 minutes
Learners will explore variations of generative adversarial network (GAN) and the challenges associated with its models, as well as the concept of deep reinforcement learning, its application for machine learning, and how it differs from deep learning, in this 11-video course. Begin by implementing autoencoders with Keras and Python; implement GAN and the role of Generator and Discriminator; and implement GAN Discriminator and Generator with Python and Keras and build Discriminator for training models. Discover the challenges of working with GAN models and explore the concept of deep reinforcement learning and its application in the areas of robotics, finance, and health care. Compare deep reinforcement learning with deep learning, and examine challenges associated with their implementations. Learn about the basic concepts of reinforcement learning, as well as the concept of deep Q-learning and implementing deep Q-learning. Then implement deep Q-learning in Python by using Keras and OpenAI Gym. The concluding exercise involves recalling variations of GAN, implementing GAN Discriminator and Generator using Python, and implementing deep Q-learning in Python by using Keras and OpenAI Gym.
Perks of Course
Certificate: Yes
CPD Points: 44
Compliance Standards: AICC
Applied Deep Learning: Unsupervised Data
Price on Request
1 hour 25 minutes
This 11-video course explores the concept of deep learning and implementation of deep learning-based frameworks for natural language processing (NLP) and audio data analysis. Discover the architectures of recurrent neural network (RNN) that can be used in modeling NLP, and the challenges of unsupervised learning and the approach of using deep learning from the perspective of common unsupervised feature machine learning. First, examine the prominent statistical classification models and compare generative classifiers with discriminative classifiers; then recall different types of generative models, with focus on generative adversarial network, variational autoencoders, and flow-based generative model. Learn about setting up and working with PixelCNN; explore differences between multilayer perception (MLP), convolutional neural network (CNN), and RNN. Explore the essential capabilities and variants of ResNet that can be used for computer vision and deep learning. Finally, take a look at encoders in neural networks and compare the capabilities of standard autoencoders and variational autoencoders. The concluding exercise involves recalling RNN architecture that can be used in modeling NLP, variants of ResNet, and setting up PixelCNN.
Perks of Course
Certificate: Yes
CPD Points: 87
Compliance Standards: AICC
Applied Predictive Modeling
Price on Request
1 hour 5 minutes
In this course, you will explore machine learning predictive modeling and commonly used models like regressions, clustering, and Decision Trees that are applied in Python with the scikit-learn package. Begin this 13-video course with an overview of predictive modeling and recognize its characteristics. You will then use Python and related data analysis libraries including NumPy, Pandas, Matplotlib, and Seaborn, to perform exploratory data analysis. Next, you will examine regression methods, recognizing the key features of Linear and Logistic regressions, then apply both a linear and a logistic regression with Python. Learn about clustering methods, including the key features of hierarchical clustering and K-Means clustering, then learn how to apply hierarchical clustering and K-Means clustering with Python. Examine the key features of Decision Trees and Random Forests, then apply a Decision Tree and a Random Forest with Python. In the concluding exercise, learners will be asked to apply linear regression, logistic regression, hierarchical clustering, Decision Trees, and Random Forests with Python.
Perks of Course
Certificate: Yes
CPD Points: 67
Compliance Standards: AICC
Applying AI to Robotics
Price on Request
55 minutes
Robots can utilize machine learning, deep learning, reinforcement learning, as well as probabilistic techniques to achieve intelligent behavior. This application of AI to robotic systems is found in the automotive, healthcare, logistics, and military industries. With increasing computing power and sophistication in small robots, more industry use cases are likely to emerge, making AI development for robotics a useful AI developer skill. In this course, you'll explore the main concepts, frameworks, and approaches needed to work with robotics and apply AI to robots. You'll examine how AI and robotics are used across multiple industries. You'll learn how to work with commonly used algorithms and strategies to develop simple AI systems that improve the performance of robots. Finally, you'll learn how to control a robot in a simulated environment using deep Q-networks.
Perks of Course
Certificate: Yes
CPD Points: 57
Compliance Standards: AICC
Applying Predictive Analytics
Price on Request
1 hour 30 minutes
This 13-video course explores machine learning predictive analytics, and how its application can drive revenues, reduce costs, and provide a competitive advantage to businesses. Learners will observe the predictive modeling process and how to apply tools and techniques for performing predictive analytics, and how to use historical data to identify trends and patterns to forecast future events. First, you will learn about the predicative modeling process, the statistical concepts for predictive modeling, and regression techniques. This course uses two examples to demonstrate commonly used methods of predictive analytics, by examining decision trees and SVMs (support vector machines). Next, you will learn about survival analysis, market basket analysis, and how to apply data for cluster models. You will learn about random forests in predictive analytics, and you will examine probabilistic graphical models. Learn about classification models, and how to organize data into groups based on predicting the class of the data points. Finally, you will explore some best practices for predictive modeling.
Perks of Course
Certificate: Yes
CPD Points: 85
Compliance Standards: AICC
Architecting Balance: Designing Hybrid Cloud Solutions
Price on Request
55 minutes
In this 12-video course, learners can explore differences between on-premises and hybrid cloud deployment and the challenges and benefits afforded by hybrid cloud architecture for machine learning solutions. Discover how to derive goals and design process for a successful hybrid cloud implementation. Begin by examining essential features afforded by cloud and cloud deployment models, and compare characteristics of on-premises and cloud deployment from perspectives of cost, hardware, software, mobility, and reliability. View factors influencing on-premises and cloud architecture; compare hybrid versus private versus public cloud; and use hybrid cloud assessment to identify appropriate scenarios for adopting a hybrid cloud architecture. Learners will be able to list essential factors to consider when deriving a hybrid cloud implementation strategy and architecture, and benefits and challenges of implementing hybrid cloud. Look at key considerations to determine whether to move and deploy applications to the cloud or retain them on on-premises environment, and demonstrate the steps involved in setting up hybrid cloud architecture. The concluding exercise involves recalling benefits of hybrid cloud and the differences between on-premises and hybrid cloud deployment models.
Perks of Course
Certificate: Yes
CPD Points: 56
Compliance Standards: AICC
Architecting Balance: Hybrid Cloud Implementation with AWS & Azure
Price on Request
1 hour 5 minutes
Explore the various services and components provided by Amazon Web Services (AWS) and Azure that can be used to implement hybrid cloud machine learning solution in this 13-video course. You will discover how to implement hybrid cloud environments by using AWS and Azure Stack. Learners begin by examining the critical use cases of implementing hybrid cloud using AWS, and the prominent AWS services that can be used to implement hybrid cloud solutions. Explore the cloudbursting application hosting model from the perspective of AWS, and recall the essential AWS services that provide integrated resource deployment management capabilities for hybrid cloud solutions. Implement a hybrid cloud environment that integrates on-premises Hadoop clusters with data lakes on AWS; recall the recommended principles and best practices of AWS hybrid cloud implementation, and essential Azure components for hybrid solutions. Explore Azure tooling for hybrid cloud; hybrid cloud implementation with Azure Stack; Azure DevOps for hybrid cloud; implementing hybrid cloud with Azure; and Azure services for hybrid cloud. The concluding exercise involves implementing a hybrid cloud with Azure.
Perks of Course
Certificate: Yes
CPD Points: 67
Compliance Standards: AICC
Automation Design & Robotics
Price on Request
1 hour 30 minutes
In this 12-video course, you will examine the different uses of data science tools and the overall platform, as well as the benefits and challenges of machine learning deployment. The first tutorial explores what automation is and how it is implemented. This is followed by a look at the tasks and processes best suited for automation. This leads learners into exploring automation design, including what Display Status is, and also the Human-Computer Collaboration automation design principle. Next, you will examine the Human Intervention automation design principle; automated testing in software design and development; and also the role of task runners in software design and development. Task runners are used to automate repeatable tasks in the build process. Delve into DevOps and automated deployment in software design, development, and deployment. Finally, you will examine process automation using robotics, and in the last tutorial in the course, recognize how modern robotics and AI designs are applied. The concluding exercise involves recognizing automation and robotics design application.
Perks of Course
Certificate: Yes
CPD Points: 34
Compliance Standards: AICC
Build & Train RNNs: Implementing Recurrent Neural Networks
Price on Request
50 minutes
Learners will examine the concepts of perception, layers of perception, and backpropagation, and discover how to implement recurrent neural network by using Python, TensorFlow, and Caffe2 in this 10-video course. Begin by taking a look at the essential features and processes of implementing perception and backpropagation in machine learning neural networks. Next, you will compare single-layer perception and multilayer perception and describe the need for layer management. You will learn about the steps involved in building recurrent neural network models; building recurrent neural networks with Python and TensorFlow; implementing long short-term memory (LSTM) by using TensorFlow, and building recurrent neural networks with Caffe2. Caffe is a deep learning framework. Building deep learning language models using Keras-an open source neural network library-will be explored in the final tutorial of the course. The concluding exercise entails implementing recurrent neural networks by using TensorFlow and Caffe2 and building deep learning language models by using Keras.
Perks of Course
Certificate: Yes
CPD Points: 48
Compliance Standards: AICC
Build & Train RNNs: Neural Network Components
Price on Request
35 minutes
Explore the concept of artificial neural networks (ANNs) and components of neural networks, and examine the concept of learning and training samples used in supervised, unsupervised, and reinforcement learning in this 10-video course. Other topics covered in this course include network topologies, neuron activation mechanism, training sets, pattern recognition, and the need for gradient optimization procedure for machine learning. You will begin the course with an overview of ANN and its components, then examine the artificial network topologies that implement feedforward, recurrent, and linked networks. Take a look at the activation mechanism for neural networks, and the prominent learning samples that can be applied in neural networks. Next, compare supervised learning samples, unsupervised learning samples, and reinforcement learning samples, and then view training samples and the approaches to building them. Explore training sets and pattern recognition and, in the final tutorial, examine the need for gradient optimization in neural networks. The exercise involves listing neural network components, activation functions, learning samples, and gradient descent optimization algorithms.
Perks of Course
Certificate: Yes
CPD Points: 36
Compliance Standards: AICC
Building Data Pipelines
Price on Request
1 hour 10 minutes
Explore data pipelines and methods of processing them with and without ETL (extract, transform, load). In this course, you will learn to create data pipelines by using the Apache Airflow workflow management program. Key concepts covered here include data pipelines as an application that sits between raw data and a transformed data set, between a data source and a data target; how to build a traditional ETL pipeline with batch processing; and how to build an ETL pipeline with stream processing. Next, learn how to set up and install Apache Airflow; the key concepts of Apache Airflow; how to instantiate a directed acyclic graph in Airflow. Learners are shown how to use tasks and include arguments in Airflow; how to use dependencies in Airflow; how to build an ETL pipeline with Airflow; and how to build an automated pipeline without using ETL. Finally, learn how to test Airflow tasks by using the airflow command line utility, and how to use Apache Airflow to create a data pipeline.
Perks of Course
Certificate: Yes
CPD Points: 69
Compliance Standards: AICC
Building ML Training Sets: Introduction
Price on Request
1 hour 10 minutes
There are numerous options available to scale and encode features and labels in data sets to get the best out of machine learning (ML) algorithms. In this 10-video course, explore techniques such as standardizing, nomalizing, and one-hot encoding. Learners begin by learning how to use Pandas library to load a data set in the form of a CSV file and perform exploratory analysis on its features. Then use scikit-learn's Binarizer to transform the continuous data in a series to binary values; apply the MiniMaxScaler on a data set to get two similar columns to have the same range of values; and standardize multiple columns in data sets with scikit-learn's StandardScaler. Examine differences between the Normalizer and other scaling techniques, and learn how to represent values in a column as a proportion of the maximum absolute value by using the MaxAbScaler. Finally, discover how to use Pandas library to one-hot encode one or more features of your data set and distinguish between this technique and label encoding. The concluding exercise involves building ML training sets.
Perks of Course
Certificate: Yes
CPD Points: 69
Compliance Standards: AICC
Building ML Training Sets: Preprocessing Datasets for Classification
Price on Request
45 minutes
In this course, learners can explore how to implement machine learning scaling techniques such as standardizing and normalizing on continuous data and label encoding on the target, in order to get the best out of machine learning algorithms. Examine dimensionality reduction by using Principal Component Analysis (PCA). Start this 6-video course by using Pandas library to load a CSV data set into a data frame and scale continuous features by using a standard scaler. You will then learn how to build and evaluate a support vector classifier in scikit-learn; use Pandas and Seaborn to generate a heat map; and spot the correlations between features in a data set. Discover how to apply the technique of PCA to reduce the number of dimensions in your input data and obtain the explained variance of each principal component. In the course's final tutorial, you will explore how to apply normalization and PCA on data sets and build a classification model with the principal components of scaled data. The concluding exercise involves processing data for classification.
Perks of Course
Certificate: Yes
CPD Points: 43
Compliance Standards: AICC
Building ML Training Sets: Preprocessing Datasets for Linear Regression
Price on Request
50 minutes
This 7-video course helps learners discover how to implement machine learning scaling techniques such as standardizing and min-max scaling on continuous data and one-hot encoding on categorical features to improve performance of linear regression models. In the first tutorial, you will use Pandas library to load a CSV file into a data frame and analyze its contents by using Pandas and Matplotlib. You will then learn how to create a linear regression model with scikit-learn to predict the sale price of a house and evaluate this model by using metrics such as mean squared error and r-square. Next, learners will examine the application of min-max scaling on continuous fields and one-hot encoding on the categorical columns of a data set. Then analyze effects of preprocessing by recognizing benefits of scaling and encoding data sets by evaluating the performance of a regression model built with preprocessed data. Also, learn how to use scikit-learn's StandardScaler on a data set's continuous features and compare its effects with that of min-max scaling. The concluding exercise involves preprocessing data for regression.
Perks of Course
Certificate: Yes
CPD Points: 50
Compliance Standards: AICC
Building Neural Networks: Artificial Neural Networks Using Frameworks
Price on Request
1 hour 55 minutes
This 13-video course helps learners discover how to implement various neural networks scenarios by using Python, Keras, and TensorFlow for machine learning. Learn how to optimize, tune, and speed up the processes of artificial neural networks (ANN) and how to implement predictions with ANN is also covered. You will begin with a look at prominent building blocks involved in building a neural network, then recalling the concept and characteristics of evolutionary algorithms, gradient descent, and genetic algorithms. Learn how to build neural networks with Python and Keras for classification with Tensorflow as the backend. Discover how to build neural networks by using PyTorch; implement object image classification using neural network algorithms; and define and illustrate the use of learning rates to optimize deep learning. Examine various parameters and approaches of optimizing neural network speed; learn how to select hyperparameters and tune for dense networks by using Hyperas; and build linear models with estimators by using the capabilities of TensorFlow. Explore predicting with neural networks, temporal prediction optimization, and heterogenous prediction optimization. The concluding exercise involves building neural networks.
Perks of Course
Certificate: Yes
CPD Points: 114
Compliance Standards: AICC
Building Neural Networks: Development Principles
Price on Request
1 hour 20 minutes
Explore essential machine learning components used to learn, train, and build neural networks and prominent clustering and classification algorithms in this 12-video course. The use of hyperparameters and perceptrons in artificial neuron networks (ANNs) is also covered. Learners begin by studying essential ANN components required to process data, and also different paradigms of learning used in ANN. Examine essential clustering techniques that can be applied on ANN, and the roles of the essential components that are used in building neural networks. Next, recall the approach of generating deep neural networks from perceptrons; learn how to classify differences between models and hyperparameters and specify the approach of tuning hyperparameters. You will discover types of classification algorithm that can be used in neural networks, and features of essential deep learning frameworks for building neural networks. Explore how to choose the right neural network framework for neural network implementations from the perspective of usage scenarios and fitment model, and define computational models that can be used to build neural network models. The concluding exercise concerns ANN training and classification.
Perks of Course
Certificate: Yes
CPD Points: 80
Compliance Standards: AICC
Cleaning Data in R
Price on Request
1 hour
R is a programming language that is essential for data science, used for statistical computing and graphics. In this 13-video course, learners explore essential methods for wrangling and cleaning data with R. Begin by recognizing types of unclean data and criteria for ensuring data quality. First, learners see how to fetch a JSON (JavaScript Object Notation) document over HTTP and load data into a dplyr table. Learn how to load multiple sheets from an Excel document and how to handle common errors encountered when reading CSV (comma-separated values) data. Read data from a relational database with a SQL (structured query language) query. Explore joining tabular data by combining two related data sets by using a join operation, and spreading data-reshaping tabular data by spreading values from rows to columns. Look at summarizing data, applying a summary function using dplyr; imputing data, using mean imputation to replace missing values; and extracting matches, using a regular expression and data wrangling tools from the tidyverse package. The closing exercise practices data wrangling functions using R.
Perks of Course
Certificate: Yes
CPD Points: 62
Compliance Standards: AICC
Cognitive Models: Approaches to Cognitive Learning
Price on Request
45 minutes
Practice plays an important role in AI development and helps one get familiarized with commonly used tools and frameworks. Knowing which methods to apply and when is critical to completing projects quickly and efficiently. Based on code examples provided, you will be able to quickly learn important cognitive modeling libraries and apply this knowledge to new projects in the field. In this course, you'll learn the essentials of working with cognitive models in a software system. First, you will get a detailed overview of each type of learning used in cognitive modeling. Further, you will learn about the toolset used for cognitive modeling with Python and recall which role cognitive models play in AI and business. Finally, you will go through various cognitive model implementations to develop skills necessary to implement cognitive modeling in real world.
Perks of Course
Certificate: Yes
CPD Points: 43
Compliance Standards: AICC
Cognitive Models: Overview of Cognitive Models
Price on Request
35 minutes
To implement cognitive modeling inside AI systems, a developer needs to understand the major differences between commonly used cognitive models and their best qualities. Today cognitive models are actively utilized in healthcare, neuroscience, manufacturing and psychology and their importance compared to other AI approaches is expected to rise. Developing a firm understanding of cognitive modeling and its use cases is essential to anyone involved in creating AI systems. In this course, you'll identify unique features of cognitive models, which help create even more intelligent software systems. First you will learn about the different types of cognitive models and the disciplines involved in cognitive modeling. Further, you will discover main use cases for cognitive models in the modern world and learn about the history of cognitive modeling and how it is related to computer science and AI.
Perks of Course
Certificate: Yes
CPD Points: 36
Compliance Standards: AICC
Common Approaches to Sampling Data
Price on Request
45 minutes
Data science is an interdisciplinary field that seeks to find interesting generalizable insights within data and then puts those insights to monetizable use. In this 8-video Skillsoft Aspire course, learners can explore the first step in obtaining a representative sample from which meaningful generalizable insights can be obtained. Examine basic concepts and tools in statistical theory, including the two most important approaches to sampling-probability and nonprobability sampling-and common sampling techniques used for both approaches. Learn about simple random sampling, systematic random sampling, and stratified random sampling, including their advantages and disadvantages. Next, explore sampling bias. Then consider what is probably the most popular type of nonprobability sampling technique-the case study, used in medical education, business education, and other fields. A concluding exercise on efficient sampling invites learners to review their new knowledge by defining the two properties of all probability sampling techniques; enumerating the three types of probability sampling techniques; and listing two types of nonprobability sampling.
Perks of Course
Certificate: Yes
CPD Points: 46
Compliance Standards: AICC
Computational Theory: Language Principle & Finite Automata Theory
Price on Request
45 minutes
In this 12-video course, learners will explore the concept of computational theory and its models by discovering how to model and implement computational theory on formal language, automata theory, and context-free grammar. Begin by examining the computational theory fundamentals and the prominent branches of computation, and also the prominent models of computation for machine learning. Then look at the concept of automata theory and list the prominent automata classes. Next, explore the finite state machine principles, and recognize the essential principles driving formal language theory and the automata theory principles. Learners will recall the formal language elements; define the concept of regular expressions; and list the theorems used to manage the semantics. Examine the concept of regular grammar and list the essential grammars used to generate regular languages. Also, examine regular language closure properties, and defining and listing the prominent features of context-free grammar. The concluding exercise involves identifying practical usage, branches, and models of computational theory, specifying notations of formal language, and listing types of context-free grammar.
Perks of Course
Certificate: Yes
CPD Points: 44
Compliance Standards: AICC
Computational Theory: Using Turing, Transducers, & Complexity Classes
Price on Request
45 minutes
Discover the concepts of pushdown automata, Turing machines, and finite transducers in this 12-video course, in which learners can examine how to identify limitations and complexities in computation and how to apply P and NP classes to manage them. Begin by recalling the machine learning analytical capabilities of grammar, then look at context-free grammar normal forms, using Chomsky normal forms and Greibach normal forms to manage context-free grammars. Describe pushdown automata and features of nondeterministic pushdown automata. This leads on to Turing machines, their capabilities, and the prominent variations in the building themes of Turing machines. Learners explore the concept of finite transducers, and the types of finite transducers. Recall the underlying limitations of computations and the limitations of computational theory, and the complexities of computation, computational theory complexities, and how it can impact Turing machine models and language families. Learn about handling computation complexities with P class and handling computation complexities with NP class. The concluding exercise involves describing properties and variations of Turing machines, types of finite transducers, and properties of recursively enumerable languages.
Perks of Course
Certificate: Yes
CPD Points: 46
Compliance Standards: AICC
Computer Vision: AI & Computer Vision
Price on Request
1 hour 30 minutes
In this course, you'll explore Computer Vision use cases in fields like consumer electronics, aerospace, automotive, robotics, and space. You'll learn about basic AI algorithms that can help you solve vision problems and explore their categories. Finally, you'll apply hands-on development practices on two interesting use cases to predict lung cancer and deforestation.
Perks of Course
Certificate: Yes
CPD Points: 43
Compliance Standards: AICC
Computer Vision: Introduction
Price on Request
40 minutes
In this course, you'll explore basic Computer Vision concepts and its various applications. You'll examine traditional ways of approaching vision problems and how AI has evolved the field. Next, you'll look at the different kinds of problems AI can solve in vision. You'll explore various use cases in the fields of healthcare, banking, retail cybersecurity, agriculture, and manufacturing. Finally, you'll learn about different tools that are available in CV.
Perks of Course
Certificate: Yes
CPD Points: 38
Compliance Standards: AICC
Core Statistical Concepts: An Overview of Statistics & Sampling
Price on Request
45 minutes
With data now being one of the most valuable assets to tap into, the demand for data science skills increases by the day. Statistics and sampling are at the core of data science. Use this course as a theoretical introduction to using samples to reveal various statistics. Examine what exactly is meant by statistics and samples. Explore descriptive statistics, namely measures of central tendency and of dispersion. Study probability sampling techniques, including simple random sampling and cluster sampling. Investigate how undersampling and oversampling are used to generate more balanced datasets. Upon completion, you'll know the best way to use statistics and samples for your specific goals and needs.
Perks of Course
Certificate: Yes
CPD Points: 47
Compliance Standards: AICC
Creating Data APIs Using Node.js
Price on Request
1 hour 30 minutes
Data science skills are of no value unless you have data to work with. Automating your data retrieval through application program interfaces (APIs) is a process that any data scientist must understand. In this 12-video course, learners will explore how to create RESTful OAuth APIs using Node.js. Begin with API prerequisites, installing the prerequisites to create an API using Node.js, and building a RESTful API using Node.js and Express.js. You will next discover how to build a RESTful API with OAuth in Node.js, before examining what OAuth is and why it is required. Learn about creating an HTTP server using Hapi.js; then look at how to use modules in your API using Node.js, and how to return data with JSON using Node.js. Learners explore using nodemon for development workflow in Node.js and learn how to make HTTP requests with Node.js by using request library. Use POSTman to test your Node.js API and deploy APIs with Node.js. Connect to social media APIs with Node.js to return data. A concluding exercise deals with building RESTful APIs.
Perks of Course
Certificate: Yes
CPD Points: 90
Compliance Standards: AICC
Data Access & Governance Policies: Data Access Governance & IAM
Price on Request
1 hour
This course explores how a DAG (Data Access Governance), a structured data access framework, can reduce the likelihood of data security breaches, and reduce the likelihood of future breaches. Risk and data safety compliance addresses how to identify threats against an organization's digital data assets. You will learn about legal compliance, industry regulations, and compliance with organizational security policies. You will learn how the IAM (identity and access management) relates to users, devices, or software components. Learners will then explore how a PoLP (Principle of Least Privilege) dictates to whom and what permission is given to users to access data. You will learn to create an IAM user and group within AWS (Amazon Web Services), and how to assign file system permissions to a Windows server in accordance with the principle of least privilege. Finally, you will examine how vulnerability assessments are used to identify security weaknesses, and different types of preventative security controls, for example, firewalls or malware scanning.
Perks of Course
Certificate: Yes
CPD Points: 58
Compliance Standards: AICC
Data Access & Governance Policies: Data Classification, Encryption, & Monitoring
Price on Request
1 hour 20 minutes
Explore how data classification determines which security measures apply to varying classes of data. This 12-video course classifies data into a couple of main categories, internal data and sensitive data. You will learn to classify data by using Microsoft FSRM (File Server Resource Manager), a role service in Windows Server that enables you to manage and classify data stored on file servers. Learners will explore different tools used to safeguard sensitive information, such as data encryption. You will learn how to enable Microsoft BitLocker, a full volume encryption feature included with Microsoft Windows, to encrypt data at rest. An important aspect of data access governance is securing data that is being transmitted over a network, and you will learn to configure a VPN (virtual private network) using Microsoft System Center Configuration Manager. You will learn to configure a Custom Filtered Log View using MS Windows Event Viewer to track user access to a database. Finally, you will learn to audit file access on an MS Windows Server 2016 host.
Perks of Course
Certificate: Yes
CPD Points: 78
Compliance Standards: AICC
Data Analysis using Spark SQL
Price on Request
55 minutes
Analyze an Apache Spark DataFrame as though it were a relational database table. During this Aspire course, you will discover the different stages involved in optimizing any query or method call on the contents of a Spark DataFrame. Discover how to create views out of a Spark DataFrame's contents and run queries against them; and how to trim and clean a DataFrame. Next, learn how to perform an analysis of data by running different SQL queries; how to configure a DataFrame with an explicitly defined schema; and define what a window is in the context of Spark. Finally, observe how to create and analyze categories of data in a data set by using Windows.
Perks of Course
Certificate: Yes
CPD Points: 54
Compliance Standards: AICC
Data Analysis Using the Spark DataFrame API
Price on Request
1 hour 10 minutes
An open-source cluster-computing framework used for data science, Apache Spark has become the de facto big data framework. In this Skillsoft Aspire course, learners explore how to analyze real data sets by using DataFrame API methods. Discover how to optimize operations with shared variables and combine data from multiple DataFrames using joins. Explore the Spark 2.x version features that make it significantly faster than Spark 1.x. Other topics include how to create a Spark DataFrame from a CSV file; apply DataFrame transformations, grouping, and aggregation; perform operations on a DataFrame to analyze categories of data in a data set. Visualize the contents of a Spark DataFrame, with Matplotlib. Conclude by studying how to broadcast variables and DataFrame contents in text file format.
Perks of Course
Certificate: Yes
CPD Points: 70
Compliance Standards: AICC
Data Architecture Deep Dive - Microservices & Serverless Computing
Price on Request
25 minutes
Explore numerous types of data architecture that are effective data wrangling tools when working with big data in this 9-video Skillsoft Aspire course. Learn the strategies, design, and constraints involved in implementing data architecture. You will learn the concepts of data partitioning, CAP theorem (consistency, availability, and partition tolerance), and process implementation using serverless and Lambda data architecture. This course examines Saga, newly introduced in data management pattern catalog of microservices; API (application programming interface) composition; CQRS (Command Query Responsibility Segregation); event sourcing; and application event. This course explores the differences in traditional data architecture and serverless architecture which allows you to use client-side logic and third-party services. You will learn how to use AWS (Amazon Web Services) Lambda to implement a serverless architecture. This course then explores batch processing architecture, which processes data files by using long running batch jobs to filter actual content, real-time architecture, and machine learning at scale architecture built to serve machine learning algorithms. Finally, you will explore how to build a successful data POC (proof of concept).
Perks of Course
Certificate: Yes
CPD Points: 25
Compliance Standards: AICC
Data Compliance Issues & Strategies
Price on Request
45 minutes
the key areas for compliance in data protection: policies and legal regulations. You will learn how an organization can develop a policy framework. In this course, learners examine the legal regulations applicable to data protection, and company policies, the internal documents, and procedures an organization implements to comply with the law. You will learn how to develop a policy framework, and how to establish internal rules for personnel. You will also learn about some of the organizations that have developed regional policies, for example, the APEC (Asia-Pacific Economic Cooperation) Privacy Framework, and the OECD (Organisation for Economic Co-operation) Privacy Principles. Finally, you will explore procedures for internal and external reporting, and other responses to data breaches
Perks of Course
Certificate: Yes
CPD Points: 43
Compliance Standards: AICC
Data Driven Organizations
Price on Request
1 hour 15 minutes
Examine data-driven organizations, how they use data science, and the importance of prioritizing data in this 13-video course. Data-driven organizations are committed to gathering and utilizing data necessary for a business holistically to gain competitive advantage. You will explore how to create a culture within an organization by involving management and training employees. You will examine analytic maturity as a metric to measure an organization's progress. Next, learn how to analyze data quality; how it is measured in a relative manner, not an absolute manner; and how it should be measured, weighed and appropriately applied to determine the value or quality of a data set. You will learn the potential business effects of missing data and the three main reasons why data are not included in a collection: missing at random, missing due to data collection, and missing not at random. This course explores the wide range of impacts when there is duplicate data. You will examine how truncated or censored data have inconsistent results. Finally, you will explore data provenance and record-keeping.
Perks of Course
Certificate: Yes
CPD Points: 74
Compliance Standards: AICC
Data Exploration using R
Price on Request
40 minutes
The tool of choice for data science professionals in every modern industry and field, the programming language R has become an essential skill for statistical computing and graphics. It both creates reproducible high-quality analyses and takes advantage of superior graphic and charting capabilities. In this 10-video Skillsoft Aspire course, you will explore data in R by using the dplyr library, including working with tabular data, piping data, mutating data, summarizing data, combining datasets, and grouping data. Key concepts covered in this course include using the dplyr library to load data frames; selecting subsets of data by using dplyr; and how to filter tabular data using dplyr. You will also learn to perform multiple operations by using the pipe operator; how to create new columns with the mutate method; and how to summarize data using summary functions. Next, use the dplyr join functions to combine data. Then learn how to use the group by method from the dplyr library, and how to query data with various dplyr library functions.
Perks of Course
Certificate: Yes
CPD Points: 40
Compliance Standards: AICC
Data Insights, Anomalies, & Verification: Handling Anomalies
Price on Request
45 minutes
In this 9-video course, learners examine statistical and machine learning implementation methods and how to manage anomalies and improvise data for better data insights and accuracy. The course opens with a thorough look at the sources of data anomaly and comparing differences between data verification and validation. You will then learn about approaches to facilitating data decomposition and forecasting, and steps and formulas used to achieve the desired outcome. Next, recall approaches to data examination and use randomization tests, null hypothesis, and Monte Carlo. Learners will examine anomaly detection scenarios and categories of anomaly detection techniques and how to recognize prominent anomaly detection techniques. Then learn how to facilitate contextual data and collective anomaly detection by using scikit-learn. After moving on to tools, you will explore the most prominent anomaly detection tools and their key components, and recognize the essential rules of anomaly detection. The concluding exercise shows how to implement anomaly detection with scikit-learn, R, and boxplot.
Perks of Course
Certificate: Yes
CPD Points: 45
Compliance Standards: AICC
Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools
Price on Request
50 minutes
Discover how to use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy. This 10-video course begins with an overview of machine learning anomaly detection techniques, by focusing on the supervised and unsupervised approaches of anomaly detection. Then learners compare the prominent anomaly detection algorithms, learning how to detect anomalies by using R, RCP, and the devtools package. Take a look at the components of general online anomaly detection systems and then explore the approaches of using time series and windowing to detect online or real-time anomalies. Examine prominent real-world use cases of anomaly detection, along with learning the steps and approaches adopted to handle the entire process. Learn how to use boxplot and scatter plot for anomaly detection. Look at the mathematical approach to anomaly detection and implementing anomaly detection using a K-means machine learning approach. Conclude your coursework with an exercise on implementing anomaly detection with visualization, cluster, and mathematical approaches.
Perks of Course
Certificate: Yes
CPD Points: 50
Compliance Standards: AICC
Data Lake Architectures & Data Management Principles
Price on Request
35 minutes
A key component to wrangling data is the data lake framework. In this 9-video Skillsoft Aspire course, learners discover how to implement data lakes for real-time management. Explore data ingestion, data processing, and data lifecycle management with Amazon Web Services (AWS) and other open-source ecosystem products. Begin by examining real-time big data architectures, and how to implement Lambda and Kappa architectures to manage real-time big data. View benefits of adopting Zaloni data lake reference architecture. Examine the essential approach of data ingestion and comparative benefits provided by file formats Avro and Parquet. Explore data ingestion with Sqoop, and various data processing strategies provided by MapReduce V2, Hive, Pig, and Yam for processing data with data lakes. Learn how to derive value from data lakes and describe benefits of critical roles. Learners will explore steps involved in the data lifecycle and the significance of archival policies. Finally, learn how to implement an archival policy to transition between S3 and Glacier, depending on adopted policies. Close the course with an exercise on ingesting data and archival policy.
Perks of Course
Certificate: Yes
CPD Points: 34
Compliance Standards: AICC
Data Lake Framework & Design Implementation
Price on Request
35 minutes
A key component to wrangling data is the data lake framework. In this 9-video Skillsoft Aspire course, discover how to design and implement data lakes in the cloud and on-premises by using standard reference architectures and patterns to help identify the proper data architecture. Learners begin by looking at architectural differences between data lakes and data warehouses, then identifying the features that data lakes provide as part of the enterprise architecture. Learn how to use data lakes to democratize data and look at design principles for data lakes, identifying the design considerations. Explore the architecture of Amazon Web Services (AWS) data lakes and their essential components, then look at implementing data lakes using AWS. You will examine the prominent architectural styles used when implementing data lakes on-premises and on multiple cloud platforms. Next, learners will see the various frameworks that can be used to process data from data lakes. Finally, the concluding exercise compares data lakes and the data warehouse, showing how to specify data lake design patterns, and implement data lakes by using AWS.
Perks of Course
Certificate: Yes
CPD Points: 33
Compliance Standards: AICC
Data Lake Sources, Visualizations, & ETL Operations
Price on Request
1 hour 25 minutes
This course discusses the transition of data warehousing to cloud-based solutions using the AWS (Amazon Web Services) cloud platform. You will explore Amazon Redshift, a fully managed petabyte-scale data warehouse service which forms part of the larger AWS cloud-computing platform. The 12-video course demonstrates how to create and configure an Amazon Redshift cluster; to load data into it from an S3 (simple storage service) bucket; and configure a Glue crawler for stored data. This course examines how to visualize the data stored in the data lake and how to perform ETL (extract, transform, load) operations on the data using Glue scripts. You will work with the DynamoDB, a NoSQL database service that supports key-value and document data structures. You will learn how to use AWS QuickSight, a high-performance business intelligence service which integrates seamlessly with Glue tables by using the Amazon Athena Query Service. Finally, you will configure jobs to run extract, transform, and load operations on data stored in our data lake.
Perks of Course
Certificate: Yes
CPD Points: 87
Compliance Standards: AICC
Data Lakes on AWS
Price on Request
1 hour 10 minutes
This course discusses the transition of data warehousing to cloud-based solutions using the AWS (Amazon Web Services) cloud platform. In 11 videos, the course explores how data lakes store data using a flat structure, and the data are tagged, making it easy to search and query. You will learn how to build a data lake on the AWS cloud by storing data in S3 (simple storage service) buckets. You will learn to set up your data lake architecture lake using AWS Glue, a fully managed ETL (extract, transform, load) service. You will learn to configure and run Glue crawlers, and you will examine how crawlers merge data stored in an S3 folder path; and to use S3 to generate metadata tables in Glue. Learners will use Athena, Amazon's interactive query service as a simple way to analyze data in S3 using standard SQL. Finally, you will examine how to merge the data crawled by our CSV (comma separated values) crawler into a single table.
Perks of Course
Certificate: Yes
CPD Points: 69
Compliance Standards: AICC
Data Pipeline: Process Implementation Using Tableau & AWS
Price on Request
40 minutes
Explore the concept of data pipelines, the processes and stages involved in building them, and technologies such as Tableau and Amazon Web Services (AWS) that can be used in this 11-video course. Learners begin with an initial look at the data pipeline and its features, and then the steps involved in building one. You will go on to learn about the processes involved in building data pipelines, the different stages of a pipeline, and the various essential technologies that can be used to implement one. Next, learners explore the various types of data sources that are involved in the data pipeline transformation phases. Then you learn to define scheduled data pipelines and list all the associated components, tasks, and attempts. You will learn how to install Tableau Server and command line utilities and then build data pipelines using the Tableau command line utilities. Finally, take a look at the steps involved in building data pipelines on AWS. The closing exercise involves building data pipelines with Tableau.
Perks of Course
Certificate: Yes
CPD Points: 38
Compliance Standards: AICC