174 SV courses

Description The Implementing Cisco IP Routing is a qualifying exam for the Cisco Certified Network Professional CCNP, Cisco Certified Internetwork Professional CCIP, and Cisco Certified Design Professional CCDP certifications. This Cisco CCNP Implementing Cisco IP Switched Networks (Switch) v2.0 Training will help you to be certified successfully with all the important knowledge and skills necessary to use advanced IP addressing and routing in implementing scalable and secure Cisco ISR routers connected to LANs and WANs. The exam also covers configuration of secure routing solutions to support branch offices and mobile workers. This is an advanced course on Cisco routing configuration for both IPv4 and IPv6. In this course you will gain the knowledge to configure and optimize a routing domain using OSPF, EIGRP, BGP, PBR, IP SLA and configure redistribution when needed. It also includes the configuration of highly secure routing solutions to support branch offices and mobile workers. Assessment & Certification To achieve a formal qualification, you are required to book an official exam separately with the relevant awarding bodies. However, you will be able to order a course completion CPD Certificate by paying an additional fee. Hardcopy Certificate by post - £19 Soft copy PDF via email - £10 Requirements Our Cisco CCNP Implementing Cisco IP Switched Networks (Switch) v2.0 Training is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation. Career Path After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. Network Design Network Design FREE 00:27:00 Collapsed Core Option 00:30:00 Switching Methods 00:31:00 CAN CAN 00:12:00 Basics of VLANs 00:19:00 Trunking 00:17:00 Creating a Trunk 00:12:00 Voice VLANs 00:21:00 Configuring a Voice VLAN 00:09:00 VTP Versions 00:17:00 Examining VTP 00:11:00 Other DHCP Configurations 00:15:00 Configuring DHCP 00:11:00 DHCP Use 00:11:00 Configure Stateless DHCP 00:20:00 What Must Match 00:06:00 Configure EtherChannel 00:05:00 Load Balancing EtherChannel 00:02:00 Spanning Tree Spanning Tree 00:31:00 Root Bridge (eNotes) 00:23:00 Verify PVST 00:16:00 Influencing the Root Bridge Election 00:28:00 STP Path Manipulation 00:04:00 UplinkFast 00:28:00 PortFast and BPDU Guard 00:06:00 Inter-VLAN Routing Inter VLAN Routing 00:08:00 Router on a Stick 00:19:00 Pros and Cons of an External Router 00:12:00 SVI 00:20:00 L3 Switch Interface (eNotes) 00:07:00 Advanced Switch Configuration Options Advanced Switch Configuration Options 00:16:00 Setting the Clock Manually 00:02:00 Configure NTP 00:07:00 Securing NTP 00:05:00 Overview of SNMP 00:18:00 Configure SNMPv3 00:10:00 What is Cisco IP SLA 00:21:00 SLA Configuration 00:08:00 Configuring SLA Responder 00:07:00 Introducing SPAN 00:18:00 L3 FHRP L3 FHRP 00:15:00 Configuring HSRP 00:17:00 What is HSRP Pre empt 00:23:00 Configuring Tracking 00:05:00 What is VRRP 00:15:00 Configuring VRRP 00:08:00 Security Security 00:25:00 Port Security 00:11:00 Port Errorst 00:21:00 Storm Control 00:02:00 Configuring TACACSRADIUS 00:08:00 Limitations of Both 00:30:00 Order Your Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

The aim of this course is to guide you to use Photoshop CC, an industry-leading image editing application and help you become an Adobe Certified Associate. With this course, you will discover the basics of digital imaging-from working with multiple images to customising the Photoshop interface according to your needs. Learn to use different Photoshop tools to edit, crop and retouch photos, without compromising the highest-quality output. This course also illustrates the most productive methods to perform common tasks and explains how to work efficiently and excellently with Adobe Photoshop. Furthermore, master the critical features such as adjustment layers, blend modes, filters, and so much more, and unveil the secrets of nondestructive editing using Smart Objects. On completion, you will be empowered and able to instantly get the image results you want and consider yourself as a creative professional. Your Learning Goals: Discover all the tools and features that loaded with Photoshop CC 2019 and get the image results you want. Learn different kinds of Selection techniques Work with images and combine images together seamlessly. Explore the most efficient ways to perform common editing tasks and retouching like a pro. Know the advantage and disadvantage of various image file formats. Learn useful keyboard shortcuts and smart practices to export and share images. Build confidence and be comfortable in using Adobe Photoshop CC. Develop all the skills needed to design your own graphics from start to finish. Who is this Course for? This endorsed Photoshop CC 2019 MasterClass  is ideal for those who have prior experience and practical knowledge in this field and would like to build on their skills to work their way up to a senior-level role. Those who are new to HR and want to expand their knowledge of fundamental principles and procedures will also find this course beneficial. This course is a complete introduction to the fundamentals of HR management and will benefit newcomers in this industry who are looking to add new skills to their CV. Entry Requirement: This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of the English language, numeracy and ICT are required to attend this course. CPD Certificate from Course Gate  At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £9 and the hard copy for £15. Also, you can order both PDF and hardcopy certificates for £22. Career path This course opens a new door for you to enter the relevant job market and also gives you the opportunity to acquire extensive knowledge along with required skills to become successful. You will be able to add our qualification to your CV/resume which will help you to stand out in the competitive job industry. Course Curriculum Introduction Why learn Photoshop? 00:03:00 How to study from this course 00:03:00 Getting an Adobe Certification 00:05:00 Basics Basics Chapter Introduction 00:01:00 Working with Images 00:10:00 User Interface 00:11:00 Navigation 00:09:00 Image Size and Resolution 00:08:00 Cropping Images 00:10:00 Color Modes 00:06:00 Layers Layers Chapter Introduction 00:01:00 Layers Essentials 00:14:00 Layers panel 00:11:00 Special Layers 00:13:00 Layer Styles 00:07:00 Blend Modes 00:07:00 Drawing Drawing Chapter Introduction 00:01:00 Brush Tool Essentials 00:17:00 Creating Custom Brush 00:14:00 Vector Shapes 00:17:00 Tranformations Transformations Chapter Introduction 00:01:00 Tranformations Essentials 00:15:00 Special Transformations 00:10:00 Selections Selections based on color and contrast 00:16:00 Selections based on color and contrast 00:24:00 Advanced Selection Techniques 00:12:00 Complex Selection Project 00:16:00 Masking Masking Chapter Introduction 00:01:00 Non-destructive Workflow 00:24:00 Pixel Masks 00:16:00 Vector Masks for Geometric Shapes 00:10:00 Vector Masks for Organic Shapes 00:07:00 Smart Objects Smart Objects Chapter Introduction 00:01:00 Smart Objects Essential 00:09:00 Vector Smart Objects 00:07:00 Linked Smart Objects 00:11:00 Smart Filters 00:15:00 Adjustment Layers Adjustment Layers Chapter Introduction 00:01:00 Adjustment Layers Essentials 00:10:00 Tonal Adjustments 00:15:00 Color Adjustments 00:19:00 Contrast Adjustments 00:10:00 Retouching Retouching Chapter Introduction 00:01:00 Healing Brushes 00:14:00 Clone Stamp Tool Essentials Part 1 00:23:00 Liquify Filter 00:11:00 Content-aware Techniques 00:13:00 Dodge and Burn Tools 00:15:00 Portrait Retouching project 00:25:00 Photo Restoration project 00:14:00 Advanced Clone Stamp Tool Techniques 00:13:00 Bridge and Camera RAW Chapter Introduction 00:01:00 Bridge Integration 00:08:00 Adobe Camera RAW 00:05:00 Type Type Chapter Introduction 00:01:00 Working with Text 00:15:00 Formatting Text 00:10:00 Creative Techniques with Text 00:18:00 Save and Export Chapter Introduction 00:01:00 Saving Your Work 00:09:00 Creating Print-ready PDFs 00:12:00 Saving Files for the Web 00:08:00 Workflows Workflows Chapter Introduction 00:01:00 Timeline Panel 00:12:00 3D Layers 00:15:00 Lightroom Integration 00:10:00 User Experience Design 00:04:00 Photoshop Mobile Apps 00:13:00 New Features in CC 2018 Variable and SVG Fonts 00:06:00 Updated Brushes panel 00:03:00 Brush Smoothing 00:07:00 Symmetrical Painting 00:02:00 Curvature Tool 00:04:00 Select & Mask 00:03:00 Improved Upscaling with Preserve Details 2.0 00:05:00 General Improvements 00:04:00 Conclusion 00:01:00 CC 2019 New Features Content-Aware Fill Workspace 00:06:00 Painting Improvements 00:07:00 Frame Tool 00:15:00 Updated behaviours 00:10:00 Conclusion Prepare for the Adobe Certified Associate exam 00:09:00 Build Your Portfolio 00:05:00 Exercise Files Exercise files - Photoshop CC 2019 MasterClass 00:00:00 Certificate and Transcript Order Your Certificates or Transcripts 00:00:00

Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:04:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:06:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Data Science & Machine Learning with Python 00:00:00

Discover the power of data science and machine learning with Python! Learn essential techniques, algorithms, and tools to analyze data, build predictive models, and unlock insights. Dive into hands-on projects, from data manipulation to advanced machine learning applications. Elevate your skills and unleash the potential of Python for data-driven decision-making.

Duration 5 Days 30 CPD hours This course is intended for This course is designed for professionals in the following job roles: Network security engineer CCNP Security candidate Channel Partner Overview After taking this course, you should be able to: Introduce site-to-site VPN options available on Cisco router and firewalls Introduce remote access VPN options available on Cisco router and firewalls Review site-to-site and remote access VPN design options Review troubleshooting processes for various VPN options available on Cisco router and firewalls The Implementing Secure Solutions with Virtual Private Networks (SVPN) v1.0 course teaches you how to implement, configure, monitor, and support enterprise Virtual Private Network (VPN) solutions. Through a combination of lessons and hands-on experiences you will acquire the knowledge and skills to deploy and troubleshoot traditional Internet Protocol Security (IPsec), Dynamic Multipoint Virtual Private Network (DMVPN), FlexVPN, and remote access VPN to create secure and encrypted data, remote accessibility, and increased privacy. Course Outline Introducing VPN Technology Fundamentals Implementing Site-to-Site VPN Solutions Implementing Cisco Internetwork Operating System (Cisco IOS©) Site-to-Site FlexVPN Solutions Implement Cisco IOS Group Encrypted Transport (GET) VPN Solutions Implementing Cisco AnyConnect VPNs Implementing Clientless VPNs Lab Outline Explore IPsec Technologies Implement and Verify Cisco IOS Point-to-Point VPN Implement and Verify Cisco Adaptive Security Appliance (ASA) Point-to-Point VPN Implement and Verify Cisco IOS Virtual Tunnel Interface (VTI) VPN Implement and Verify Dynamic Multipoint VPN (DMVPN) Troubleshoot DMVPN Implement and Verify FlexVPN with Smart Defaults Implement and Verify Point-to-Point FlexVPN Implement and Verify Hub and Spoke FlexVPN Implement and Verify Spoke-to-Spoke FlexVPN Troubleshoot Cisco IOS FlexVPN Implement and Verify AnyConnect Transport Layer Security (TLS) VPN on ASA Implement and Verify Advanced Authentication, Authorization, and Accounting (AAA) on Cisco AnyConnect VPN Implement and Verify Clientless VPN on ASA

This Kaizen and Time Wastes Course is a great way to introduce people to the concept of Kaizen in the workplace. It's also a great way to build upon the knowledge of people already familiar with the subject.

Learning Outcomes After completing this course, learners will be able to: Learn Python for data analysis using NumPy and Pandas Acquire a clear understanding of data visualisation using Matplotlib, Seaborn and Pandas Deepen your knowledge of Python for interactive and geographical potting using Plotly and Cufflinks Understand Python for data science and machine learning Get acquainted with Recommender Systems with Python Enhance your understanding of Python for Natural Language Processing (NLP) Description Whether you are from an engineering background or not you still can efficiently work in the field of data science and the machine learning sector, if you have proficient knowledge of Python. Since Python is the easiest and most used programming language, you can start learning this language now to advance your career with the Data Science And Machine Learning Using Python : A Bootcamp course. This course will give you a thorough understanding of the Python programming language. Moreover, it will show how can you use Python for data analysis and machine learning. Alongside that, from this course, you will get to learn data visualisation, and interactive and geographical plotting by using Python. The course will also provide detailed information on Python for data analysis, Natural Language Processing (NLP) and much more. Upon successful completion of this course, get a CPD- certificate of achievement which will enhance your resume and career. Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Method of Assessment After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly. Career Path After completing this course, you can explore various career options such as Web Developer Software Engineer Data Scientist Machine Learning Engineer Data Analyst In the UK professionals usually get a salary of £25,000 - £30,000 per annum for these positions.

Overview The Ultimate Microsoft Office 4 Courses Bundle Course will provide you with everything you need to master the skills for confidently using Microsoft Office in the workplace. You will develop the essential knowledge and abilities employers expect in Word, Excel, PowerPoint and Access. This is a unique bundle course that will teach you the essential shortcuts and time-saving tools, including how to modify the programs according to your needs. You will also be tutored in a wide range of topics in an extremely expert manner that will make your life easier, no matter what your work requires. After the completion of this online Microsoft Office training program, you will receive a valid acknowledgement in Microsoft Office, and be able to accomplish office tasks quickly, with greater efficiency. Why choose this course Earn an e-certificate upon successful completion. Accessible, informative modules taught by expert instructors Study in your own time, at your own pace, through your computer tablet or mobile device Benefit from instant feedback through mock exams and multiple-choice assessments Get 24/7 help or advice from our email and live chat teams Full Tutor Support on Weekdays Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Online study materials Mock exams Multiple-choice assessment Certificate of Achievement Endorsed Certificate of Achievement from the Quality Licence Scheme Once the course has been completed and the assessment has been passed, all students are entitled to receive an endorsed certificate. This will provide proof that you have completed your training objectives, and each endorsed certificate can be ordered and delivered to your address for only £119. Please note that overseas students may be charged an additional £10 for postage. CPD Certificate of Achievement from Janets Upon successful completion of the course, you will be able to obtain your course completion e-certificate. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99. Endorsement This course and/or training programme has been endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. This course and/or training programme is not regulated by Ofqual and is not an accredited qualification. Your training provider will be able to advise you on any further recognition, for example progression routes into further and/or higher education. For further information please visit the Learner FAQs on the Quality Licence Scheme website. Method of Assessment In order to ensure the Quality Licensing scheme endorsed and CPD acknowledged certificate, learners need to score at least 60% pass marks on the assessment process. After submitting assignments, our expert tutors will evaluate the assignments and give feedback based on the performance. After passing the assessment, one can apply for a certificate. Requirements There is no specific requirements for our Ultimate Microsoft Office 4 Courses Bundle course. It is compatible and accessible from any device including Windows, Mac, Android, iOS, Tablets etc. The course requires a moderate Internet connection so it is possible to start learning with any type of Internet from anywhere at anytime without any limitations. 

Duration 5 Days 30 CPD hours This course is intended for Network Engineers Channel Partners System Engineers Overview By the end of this course, you will be able to: Describe how ONTAP 9 fits into NetApp?s Cloud and Data Fabric strategy Identify supported ONTAP platforms Define ONTAP cluster components Create a cluster Manage ONTAP administrators Configure and manage storage resources Configure and manage networking resources Describe a Storage Virtual Machine?s (SVM?s) role in NetApp?s storage architecture Create and configure an SVM Create and manage FlexVols Implement storage efficiency features Create protocol servers within an SVM Upgrade and revert ONTAP patches and releases Describe the levels on which ONTAP protects data Describe the ONTAP 9 data protection features Understand the various data mirroring relationships available with ONTAP 9 Configure and operate SnapMirror and SnapVault data replication Demonstrate Storage Virtual Machine data protection Explain the components and configuration involved with SyncMirror and MetroCluster Describe NDMP protocol operation, configuration and management Pre/Post Assessment The ONTAP 9.0 Cluster Administration and Data Protection combo course uses lecture and hands-on exercises to teach basic administration and configuration of a cluster as well as the core backup and restore technologies found in ONTAP 9. The hands-on labs allow you to practice working with ONTAP features and manage your storage and network resources using the cluster shell and OnCommand System Manager. You will learn how to implement and manage SnapMirror, SnapVault, and SnapLock technology which are used to replicate and restore mission-critical data in the enterprise. The course also surveys real-world scenarios and use cases to teach you when to use each of the NetApp protection solutions. Backup and restore operations are taught using the command line and OnCommand System Manager.Includes: ONTAP commands for software versions 8.3.x to 9.0 The ONTAP 9.0 Cluster Administration and Data Protection combo course uses lecture and hands-on exercises to teach basic administration and configuration of a cluster as well as the core backup and restore technologies found in ONTAP 9. The hands-on labs allow you to practice working with ONTAP features and manage your storage and network resources using the cluster shell and OnCommand System Manager. You will learn how to implement and manage SnapMirror, SnapVault, and SnapLock technology which are used to replicate and restore mission-critical data in the enterprise. The course also surveys real-world scenarios and use cases to teach you when to use each of the NetApp protection solutions. Backup and restore operations are taught using the command line and OnCommand System Manager. Includes: ONTAP commands for software versions 8.3.x to 9.0

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production