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Duration 4 Days 24 CPD hours This course is intended for #NAME? Overview At the completion of this course, students already familiar with the RHCT/RHCSA administration skills will have exposure to all competencies tested by the RHCSA and RHCE exams. If you are an experienced Linux© system administrator and hold a Red Hat Certified System Administrator (RHCSA©) credential or possess equivalent skills and want to broaden your ability to administer Linux systems at an enterprise level, this is the perfect course.The course will empower you to deploy and manage network servers running caching domain name service (DNS), MariaDB, Apache HTTPD, Postfix SMTP null clients, network file sharing with network file system (NFS) and server message block (SMB), iSCSI initiators and targets, advanced networking and firewall configurations, and to use bash shell scripting to help automate, configure, and troubleshoot your system. Through lectures and hands-on labs, you will be exposed to all competencies covered by the Red Hat Certified Engineer (RHCE) exam (EX300), supplementing what you have already learned in earning your RHCSA credential.This course is based on Red Hat© Enterprise Linux 7. Getting Started with the Classroom Environment Given a virtualized environment, begin to administrate multiple systems using prerequisite skills Enhance User Security Configure system to use Kerberos to verify credentials and grant privileges via sudo Bash Scripting and Tools Automate system administration tasks utilizing Bash scripts and text-based tools File Security with GnuPG Secure files with GnuPG. Software Management Use yum plugins to manage packages and understand the design of packages to build a simple package Network Monitoring Profile running services then capture and analyze network traffic Route Network Traffic Configure system to route traffic and customize network parameters with sysctl Secure Network Traffic Secure network traffic through SSH port forwarding and iptables filtering/network address translation (NAT) NTP Server Configuration Configure an NTP server Filesystems and Logs Manage local file system integrity, monitor system over time, and system logging Centralized and Secure Storage Access centralized storage (iSCSI) and encrypt filesystems SSL-encapsulated Web Services Understand SSL certificates and deploy an SSL encapsulated web service Web Server Additional Configuration Configure web server with virtual hosts, dynamic content, and authenticated directories Basic SMTP Configuration Configure an SMTP server for basic operation (null client, receiving mail, smarthost relay) Caching-Only DNS Server Understand DNS resource records and configure a caching-only name server File Sharing with NFS Configure file sharing between hosts with NFS File Sharing with CIFS Configure file and print sharing between hosts with CIFS File Sharing with FTP Configure file sharing with anonymous FTP Troubleshooting Boot Process Understand the boot process and recover unbootable systems with rescue mode

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS Overview This course is designed to teach you how to: Define machine learning (ML) and deep learning Identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Module 1: Machine learning overview A brief history of AI, ML, and DL The business importance of ML Common challenges in ML Different types of ML problems and tasks AI on AWS Module 2: Introduction to deep learning Introduction to DL The DL concepts A summary of how to train DL models on AWS Introduction to Amazon SageMaker Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model Module 3: Introduction to Apache MXNet The motivation for and benefits of using MXNet and Gluon Important terms and APIs used in MXNet Convolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset Module 4: ML and DL architectures on AWS AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) Hands-on lab: Deploying a trained model for prediction on AWS Lambda Additional course details: Nexus Humans Deep Learning on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Deep Learning on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for This course is designed for post-sales audiences and is aimed at channel partners, customer network engineers and administrators whose interest is focused around designing a scalable infrastructure with the FlexPod. Overview Upon completing this course, you will be able to meet these overall objectives: Describe the FlexPod data center solutions and architecture Identify FlexPod workload sizing and technical specifications Describe the FlexPod deployment and management strategies The goal of this course is to evaluate the FlexPod solution design process in regards to the contemporary data center challenges. The course provides a comprehensive understanding of the reconnaissance and analytics to assess computing solution performance characteristics and requirements. In addition this course will describe the hardware components of the FlexPod and the process for selecting proper hardware for a given set of requirements. FlexPod Data Center Solutions and Architecture Describe data center elements Identify data center business challenges Identify data center environmental challenges Identify data center technical challenges Describe the data center consolidation trend Describe the FlexPod solution Identify the benefits of FlexPod Describe FlexPod platforms Describe FlexPod validated and supported designs Identify the supported Cisco UCS components Identify the supported Cisco Nexus switch components Identify the supported NetApp storage components FlexPod Workload Sizing and Technical Specifications Describe FlexPod performance characteristics Describe server virtualization performance characteristics Describe desktop virtualization performance characteristics Describe reconnaissance and analysis tools Describe the process for deploying analysis tools Configure the Microsoft MAP Toolkit Identify FlexPod Design components Describe FlexPod Sizing considerations Employ Cisco UCS Application Sizer Employ Cisco UCS VXI Resource Comparison tool Describe NetApp Solution Builder Sizing tool FlexPod Deployment and Management Strategies Describe key FlexPod LAN features Describe key FlexPod SAN features Identify FlexPod server provisioning features List FlexPod high availability features Describe supported FlexPod SAN features Describe FlexPod virtual storage tiering features Identify Cisco FlexPod validated designs Identify FlexPod data center with VMware vSphere 5.1 Identify FlexPod data center with VMware vSphere 5.1 with Cisco Nexus 7000 Identify FlexPod data center with Microsoft Private Cloud Enterprise Design Guide Identify FlexPod Select with Cloudera's Distribution including Apache Hadoop (CDH) Identify FlexPod Cisco Nexus 7000 and NetApp MetroCluster for multisite deployment Identify data center operations and management challenges Describe FlexPod validated management solutions Describe Cisco UCS Director turnkey solutions Identify Cisco UCS Director management types Describe Cisco UCS Director automation Describe self-service provisioning and reporting Identify the customer challenges and goals Describe the workload analysis Describe the component selection process Review the selected component Analyze the solution Additional course details: Nexus Humans Cisco Designing the FlexPod Solution (FPDESIGN) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Cisco Designing the FlexPod Solution (FPDESIGN) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 1 Days 6 CPD hours This course is intended for Software Engineers Overview The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.