185 SV courses

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. Course Content Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Machine Learning using Python : A Bootcamp 00:00:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Do you want to understand how to create and manipulate vector images? Our Complete Adobe Illustrator CC 2018 will help start your journey on mastering one of the cornerstones of the design industry. Through this Complete Adobe Illustrator CC 2018 you'll learn how to work with shapes, color, effects, and typography. You will learn how to prepare and optimize your graphics to make them ready for web, print, and video. You will learn how to customize your workspace, use shortcuts and a range of tips and tricks that will enable you to work effectively with the powerful toolset that Illustrator gives us. Learn How to export different formats and prepare your Artwork for printing or screen use, designing Confidently with illustrator after Practicing on downloadable illustrator exercises & videos, you will be able to use the drawing knowledge to draw logos, characters, infographics..etc. Who is this Course for? Complete Adobe Illustrator CC 2018 is perfect for anyone trying to learn potential professional skills. As there is no experience and qualification required for this course, it is available for all students from any academic background. 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 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. Assessment: This course does not involve any MCQ exams. Students need to answer 3 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work. Course Curriculum Introduction to Course Illustrator Course Outline 00:02:00 The Power of Adobe illustrator 00:10:00 Achieve the best learning experience 00:02:00 Illustrator Fundamentals & Basics Illustrator Documents 00:04:00 Ilustrator workspaces & Properties Panel 00:13:00 Artboards 00:13:00 Illustrator Navigation & zooming 00:09:00 Illustrator Prefrences & Settings 00:12:00 Illustrator Basic geometric shapes 00:16:00 Illustrator Transformation & Rotation (resized) 00:12:00 Illustrator Pathfinder & shape builder tool 00:11:00 Illustrator Selection tools & methods 00:09:00 Grouping elements in illustrator 00:06:00 illustrator Layers & Arranging elements 00:08:00 Illustrator Align & distribute panel 00:04:00 Illustrator Gudies, Grids & Rulers 00:11:00 Excercise (layers, selection, pathfinder, guides, coloring, Artboards) 00:17:00 Colors, Appearance & Graphic Styles in Illustrator Illsutrator Stroke Panel 00:09:00 Illsutrator color panels ( color, swatches, themes, guide) 00:19:00 Illustrator Gradiant colors panel 00:13:00 Illustrator Appearane & Graphic style panels 00:09:00 Illustrator effects 00:14:00 Excercise (Appearance, Graphic Style, Gradients, strokes) 00:09:00 Drawing Tools & Techniques Illustrator pencil tool 00:07:00 Illustrator Brush tool 00:04:00 Drawing modes & blob brush tool 00:17:00 Illustrator Pen Tools 00:14:00 Illustrator curvature tool 00:05:00 Pen Tools & curvature tool exercise 00:18:00 Illustrator drawing modifying tools 00:14:00 illustrator Transform & Liquify tools 00:10:00 illustrator puppet warp tool 00:04:00 illustrator envlope distort 00:07:00 Drawing Symmetric Shapes 00:09:00 Drawing Concentric Symmetrical design 00:11:00 Illustrator Clipping Masks 00:11:00 Illustrator Opacity Masks 00:08:00 illustrator live paint bucket tool 00:09:00 Recolor Artwork 00:09:00 Exercise Complex Drawing 00:09:00 Illustrator Complex Drawing techniques explained 00:15:00 Illustrator Brushes Illustrator Art Brushes 00:14:00 Illustrator Scatter Brush 00:10:00 illustrator Bristle Brush 00:08:00 Illustrator Calligraphic brush 00:10:00 Illustrator Pattern brush 00:08:00 Illustrator Images brushes 00:09:00 Exercise (Brushes) 00:03:00 Design With images in illustrator handling images in illustrator 00:16:00 images modifications before tracing 00:06:00 Tracing images in illustrator 00:13:00 Enhancing traced Vectors & coloring hints 00:07:00 Exercise (Images) 00:03:00 Typography in Illustrator Illustrator Typography Anatomy & Character control 00:15:00 illustrator Paragraphes control 00:12:00 Illustrator Chracter & paragraph styles panels 00:11:00 Illustrator Fonts (Filtering, Variable, glyphs, stylistic sets, open type panel, svg) 00:16:00 illustrator Touch type tool 00:02:00 Illustrator type on a path tools 00:07:00 Clipping Typography Masks 00:04:00 Illustrator Typography Art directions & trending visual effects 00:15:00 illustrator type envlope distort 00:04:00 illustrator text wrap- 00:02:00 Exercise (Typography Composition) 00:03:00 Illustrator Advanced Techniques & Tricks Illustrator blend tool 00:10:00 illustrator perspective drawing 00:12:00 67 Illustrator Symbols- 00:12:00 Creating Patterns in Illustrator 00:09:00 illustrator Graphs 00:15:00 illustrator gradiant mesh 00:08:00 Exporting & Finalizing Artwork in illustrator exporting illustrator files & save for web 00:07:00 Preparing & exporting illustrator file for printing 00:07:00 Illustrator Asset Export panel 00:02:00 Creative cloud & libraries 00:07:00 Illustrator export for screens, save for web & Archiving files 00:09:00 Certificate and Transcript Order Your Certificates or 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

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

Learn to identify and communicate your boundaries with this practical framework for leaders and managers.

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.

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

Duration 69 Days 414 CPD hours Cisco Learning Library: Security offers a subscription to all Cisco online cybersecurity and cyber operations training, including extensive sk This comprehensive technical training library offers full-length, interactive certification courses, product and technology training with labs, and thousands of reference materials. Security Library Certification Courses CCNP Security Implementing and Operating Cisco Security Core Technologies (SCOR) v1.0 Securing Networks with Cisco Firepower Next Generation Firewall (SSNGFW) v1.0 Securing Networks with Cisco Firepower Next-Generation IPS (SSFIPS) v4.0 Implementing and Configuring Cisco Identity Services Engine (SISE) v3.0 Securing Email with Cisco Email Security Appliance (SESA) v3.0 Securing the Web with Cisco Web Security Appliance (SWSA) v3.0 Implementing Secure Solutions with Virtual Private Networks (SVPN) v1.0 Implementing Automation for Cisco Security Solutions (SAUI) v1.0 CCIE Security Implementing and Operating Cisco Security Core Technologies (SCOR) v1.0 Product and Technology Training Implementing and Operating Cisco Security Core Technologies (SCOR) v1.0 Implementing Automation for Cisco Security Solutions (SAUI) v1.0 Understanding Cisco Cybersecurity Fundamentals (SECFND) v1.0 Implementing Cisco Cybersecurity Operations (SECOPS) v1.0 Implementing Secure Solutions with Virtual Private Networks (SVPN) v1.0 Implementing an Integrated Threat Defense Solution (SECUR201) v1.0 Integrated Threat Defense Investigation and Mitigation (SECUR202) v1.0 Securing Cisco Networks with Snort Rule Writing Best Practices (SSFRules) v2.0 Securing Cisco Networks with Open Source Snort (SSFSNORT) v3.0 Securing Networks with Cisco Firepower Next Generation Firewall (SSNGFW) v1.0 Securing Email with Cisco Email Security Appliance (SESA) v3.0 Securing the Web with Cisco Web Security Appliance (SWSA) v3.0 Securing Networks with Cisco Firepower Next-Generation IPS (SSFIPS) v4.0 Introduction to 802.1X Operations for Cisco Security Professionals (802.1X) v2.0 Securing Industrial IoT Networks with Cisco Technologies (ISECIN) v1.0 Implementing and Configuring Cisco Identity Services Engine (SISE) v3.0 Protecting Against Malware Threats with Cisco AMP for Endpoints (SSFAMP) v5.0 Introducing Cisco Cloud Consumer Security (SECICC) v1.0 Securing Cloud Deployments with Cisco Technologies (SECCLD) v1.0 Configuring Cisco ISE Essentials for SD-Access (ISESDA) v1.0 Securing Branch Internet and Cloud Access with Cisco SD-WAN (A-SDW-BRSEC)