11428 Environment courses in Hamilton delivered Online

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 5 Days 30 CPD hours This course is intended for Data center administrators Data center engineers Systems engineers Server administrators Network managers Cisco integrators and partners Data center designers Technical solutions architects Network architects Overview After taking this course, you should be able to: Describe the foundations of data center networking Describe Cisco Nexus products and explain the basic Cisco NX-OS functionalities and tools Describe Layer 3 first-hop redundancy Describe Cisco FEX connectivity Describe Ethernet port channels and vPCs Introduce switch virtualization, machine virtualization, and describe network virtualization Compare storage connectivity options in the data center Describe Fibre Channel communication between the initiator server and the target storage Describe Fibre Channel zone types and their uses Describe NPV and NPIV Describe data center Ethernet enhancements that provide a lossless fabric Describe FCoE Describe data center server connectivity Describe Cisco UCS Manager Describe the purpose and advantages of APIs Describe Cisco ACI Describe the basic concepts of cloud computing The Understanding Cisco Data Center Foundations (DCFNDU) v1.1 course helps you prepare for entry-level data center roles. In this course, you will learn the foundational knowledge and skills you need to configure Cisco© data center technologies including networking, virtualization, storage area networking, and unified computing. You will get an introduction to Cisco Application Centric Infrastructure (Cisco ACI), automation and cloud computing. You will get hands-on experience with configuring features on Cisco Nexus Operating System (Cisco NX-OS) and Cisco Unified Computing System (Cisco UCS). This course does not lead directly to a certification exam, but it does cover foundational knowledge that can help you prepare for several CCNP and other professional-level data center courses and exams. Describing the Data Center Network Architectures Cisco Data Center Architecture Overview Three-Tier Network: Core, Aggregation, and Access Spine-and-Leaf Network Two-Tier Storage Network Describing the Cisco Nexus Family and Cisco NX-OS Software Cisco Nexus Data Center Product Overview Cisco NX-OS Software Architecture Cisco NX-OS Software CLI Tools Cisco NX-OS Virtual Routing and Forwarding Describing Layer 3 First-Hop Redundancy Default Gateway Redundancy Hot Standby Router Protocol Virtual Router Redundancy Protocol Gateway Load Balancing Protocol Describing Cisco FEX Server Deployment Models Cisco FEX Technology Cisco FEX Traffic Forwarding Cisco Adapter FEX Describing Port Channels and vPCs Ethernet Port Channels Virtual Port Channels Supported vPC Topologies Describing Switch Virtualization Cisco Nexus Switch Basic Components Virtual Routing and Forwarding Cisco Nexus 7000 VDCs VDC Types VDC Resource Allocation VDC Management Describing Machine Virtualization Virtual Machines Hypervisor VM Manager Describing Network Virtualization Overlay Network Protocols VXLAN Overlay VXLAN BGP EVPN Control Plane VXLAN Data Plane Cisco Nexus 1000VE Series Virtual Switch VMware vSphere Virtual Switches Introducing Basic Data Center Storage Concepts Storage Connectivity Options in the Data Center Fibre Channel Storage Networking VSAN Configuration and Verification Describing Fibre Channel Communication Between the Initiator Server and the Target Storage Fibre Channel Layered Model FLOGI Process Fibre Channel Flow Control Describing Fibre Channel Zone Types and Their Uses Fibre Channel Zoning Zoning Configuration Zoning Management Describing Cisco NPV Mode and NPIV Cisco NPV Mode NPIV Mode Describing Data Center Ethernet Enhancements IEEE Data Center Bridging Priority Flow Control Enhanced Transmission Selection DCBX Protocol Congestion Notification Describing FCoE Cisco Unified Fabric FCoE Architecture FCoE Initialization Protocol FCoE Adapters Describing Cisco UCS Components Physical Cisco UCS Components Cisco Fabric Interconnect Product Overview Cisco IOM Product Overview Cisco UCS Mini Cisco IMC Supervisor Cisco Intersight Describing Cisco UCS Manager Cisco UCS Manager Overview Identity and Resource Pools for Hardware Abstraction Service Profiles and Service Profile Templates Cisco UCS Central Overview Cisco HyperFlex Overview Using APIs Common Programmability Protocols and Methods How to Choose Models and Processes Describing Cisco ACI Cisco ACI Overview Multitier Applications in Cisco ACI Cisco ACI Features VXLAN in Cisco ACI Unicast Traffic in Cisco ACI Multicast Traffic in Cisco ACI Cisco ACI Programmability Common Programming Tools and Orchestration Options Describing Cloud Computing Cloud Computing Overview Cloud Deployment Models Cloud Computing Services Lab outline Explore the Cisco NX-OS CLI Explore Topology Discovery Configure HSRP Configure vPCs Configure VRF Explore the VDC Elements Install ESXi and vCenter Configure VSANs Validate FLOGI and FCNS Configure Zoning Configure Unified Ports on a Cisco Nexus Switch and Implement FCoE Explore the Cisco UCS Server Environment Configure a Cisco UCS Service Profile Configure Cisco NX-OS with APIs Explore the Cisco UCS Manager XML API Management Information Tree Explore Cisco ACI

Master Python 3.10 from the experts with this course and demonstrate programming wizardry in Python. Go from a geek to a wizard in no time with this well-structured video course and write Python code like a pro. Learn about the often-forgotten basics and special features of programming and Python in general.

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QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

FREE Certification:QLS Endorsed + CPD Accredited | Instant Access | Round-the-Clock Tutor Support | All-Inclusive Cost