548 Machine Learning (ML) courses in Esher delivered Online

This Microsoft Power BI course will help you become a Power BI expert. It'll enhance your skills by offering you comprehensive knowledge on Power BI Desktop and Power BI Online, and unleash the capabilities of Power BI to perform a comprehensive analysis of data from multiple sources and present the data using the perfect visualization.

All about containers, Docker, and Kubernetes for Python engineers.

Course Overview Learn about the functions of Microsoft Azure from this AZ-900 | Microsoft Azure Fundamentals Full Course course. The course will give you a clear understanding of the basics of Microsoft Azure and how you can use this cloud platform to grow and strengthen your online existence. In this AZ-900 | Microsoft Azure Fundamentals Full Course course, you will learn about the tools and basic functions of Microsoft Azure. You will be familiarized with the core Azure services, security, privacy and compliance policies. This course will teach you how you can secure your website and account using multi-factor authentication and protect data from hackers. This course will also help you to understand the supports Azure can offer you and get the best suitable one for you. Microsoft Azure is one of the most popular and safe cloud platforms. This AZ-900 | Microsoft Azure Fundamentals Full Course course will teach you the functions of Microsoft Azure from scratch. You don't need any prior knowledge or technical background to understand the lessons of this course. Learning Outcomes familiarize with the fundamentals of cloud services Understand the benefits of using cloud services Learn about the differences between capital expenditure and operational expenditure Be able to compare and contrast the IAAS, PAAS and SAAS service Learn about different cloud models and how they work Understand the core Azure architectural components Learn about the solutions you will get from Azure Learn about the management tools of Azure Get to know about the security and private privacy protocols of Microsoft Azure Understand how Azure identity services work Familiarize with role-based access control system Understand the policies and compliance standards in Azure Who is this course for? This comprehensive AZ-900 | Microsoft Azure Fundamentals Full Course is ideal for those who want to learn more about the functions of Microsoft Azure. You will learn about the application of Microsoft Azure and the career prospect from this course. 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. Certification After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24. PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry-leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognized accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path AZ-900 | Microsoft Azure Fundamentals Full Course is a useful qualification to possess and would be beneficial for any related profession or industry such as: Managers Managing Directors Management Executives Data Security Officers Programmers Microsoft Azure Developers Technicians Computer Operators Cloud Engineers Cloud Data Consultants Azure Consultants Data Scientists Course Introduction Introduction 00:04:00 Module 1 : Cloud Concepts What is Cloud Computing - I 00:05:00 What is Cloud Computing - II 00:06:00 Benefits of Cloud Computing 00:09:00 Key Concepts and Terminology 00:06:00 Economies of Scale 00:01:00 CapEx Vs OpEx 00:03:00 Cloud Models : What is Public Cloud 00:02:00 Cloud Models : Characteristics of Public Cloud 00:02:00 Cloud Models : What is Private Cloud 00:01:00 Cloud Models : Characteristics of Private Cloud 00:01:00 Cloud Models : Hybrid Cloud 00:01:00 Cloud Models : Characteristics of Hybrid Cloud 00:01:00 Review and What Next!! 00:01:00 What is IAAS 00:04:00 Use Cases of IAAS 00:02:00 What is PAAS ? 00:02:00 Use Cases of PAAS 00:04:00 What is SAAS ? 00:02:00 Cloud Models : Shared Responsibility Model 00:09:00 Module 2 : Core Azure Services Introduction 00:01:00 Azure Regions 00:01:00 Special Azure regions 00:01:00 Region pairs 00:01:00 Feature Availability Region Wise 00:01:00 Availability Zones 00:01:00 Availability Sets 00:02:00 What are Resource Groups? 00:02:00 Azure Resource Manager 00:01:00 What Next!! - Azure Core Services and Products 00:02:00 What is Azure Compute 00:01:00 Azure Virtual Machines - Audiocast Only 00:01:00 Azure Virtual Machines I - LAB 00:15:00 Azure Virtual Machines II - LAB 00:01:00 Azure Virtual Machines III - LAB 00:02:00 Azure Virtual Machines IV - LAB 00:04:00 Azure Virtual Machines V - LAB 00:03:00 Azure Virtual Machines VI - LAB 00:03:00 What are Containers? 00:04:00 Containers ( LAB Activity ) 00:07:00 Containers VS Virtual Machines 00:04:00 What Are Virtual Networks 00:01:00 Virtual Networks - LAB 00:15:00 Azure Load Balancer 00:01:00 VPN Gateway 00:01:00 Azure Application Gateway - I 00:02:00 Azure Application Gateway - II 00:01:00 Azure Content Delivery Networks (CDN's) 00:02:00 How CDN works ? 00:03:00 Azure CDN - Lab Activity 00:07:00 Azure Storage Services 00:01:00 Structured Data 00:01:00 Semi Structured Data 00:01:00 Unstructured Data 00:01:00 Azure Storage Account - Types 00:03:00 Azure Storage Account - Blob - Lab Activity - I 00:07:00 Azure Storage Account - Blog - Lab Activity - II 00:07:00 Azure Storage Account - Blob - Lab Activity - III 00:16:00 Azure Storage Account - Blog - Lab Activity - IV 00:09:00 Azure Storage Account - Blob - Lab Activity - V 00:04:00 Azure Storage Account - Blob - Lab Activity - VI 00:07:00 Azure Database Services 00:02:00 Azure SQL - Lab Demo 00:09:00 Azure MarketPlace 00:02:00 What is Internet of Things ( IOT ) - Intro 00:01:00 IOT Hub 00:01:00 IOT Hub Demo 00:09:00 Azure Big Data and Analytics 00:01:00 Azure SQL Data Warehouse 00:01:00 Azure HDInsights 00:01:00 Azure Data Lake Analytics 00:01:00 Machine Learning 00:02:00 Azure Machine Learning Services and Studio 00:02:00 What is Server less Computing ? 00:02:00 The concept of DevOps 00:03:00 Azure Management Tools 00:01:00 Creating Resources with Powershell - Lab Activity 00:05:00 Creating Resources with Azure CLI - Lab Activity 00:07:00 Provision Resources using Cloud Shell - Lab Activity 00:05:00 Deployment with JSON - Lab Activity 00:08:00 Azure Advisor 00:01:00 Module 2 : What did we learn 00:01:00 Module 3 Security, Privacy, Compliance and Trust What to expect in Module 3 00:01:00 Azure Firewalls 00:02:00 Azure Firewall - Lab Activity - notes 00:02:00 Azure Firewall - Lab Activity 00:19:00 Azure DDOS 00:02:00 Network Security Groups 00:03:00 Application Security Groups 00:02:00 Which Network Security Solution to choose from ? 00:04:00 AuthZ and AuthN 00:01:00 Azure Active Directory 00:02:00 Multi Factor Authentication 00:03:00 Azure Security Center 00:02:00 Azure Security center - LAB activity 00:08:00 Azure Key Vault 00:02:00 Azure Information Protection 00:02:00 Azure Advanced Threat Protection 00:04:00 What is Azure Policy 00:03:00 Azure Policy - Lab Activity 00:06:00 Azure Role Based Access Control ( RBAC ) 00:02:00 Azure Role Based Access Control ( RBAC ) - Lab Activity 00:07:00 Azure Locks 00:01:00 Azure Locks - Lab Activity 00:02:00 Azure Blueprints 00:01:00 Subscription Governance 00:02:00 Azure Tags 00:03:00 Azure Monitoring 00:02:00 Azure Monitor- Lab Activity 00:03:00 Azure Service Health 00:01:00 Monitoring Applications and Services 00:04:00 Compliance Terms and Requirements 00:02:00 Microsoft Privacy Statement 00:01:00 Microsoft Trust Center 00:01:00 Service Trust Portal 00:01:00 Azure Government Services 00:02:00 Azure Germany Services 00:01:00 Azure China 21Vianet 00:02:00 Module 4 : Azure Pricing and Support Module 4 Introduction : What tÌ¥o expect in this module 00:02:00 Azure Subscriptions 00:06:00 What are Management Groups 00:01:00 Purchase Azure Product & Services : Available Options 00:01:00 Usage Metrics 00:01:00 Factors Affecting Costs 00:02:00 The concept of Zones for Billing 00:02:00 Azure Pricing Calculator 00:04:00 Azure Total Cost of Ownership ( TCO ) 00:02:00 Ways to Minimize Costs in Azure 00:04:00 Azure Cost Management 00:02:00 Azure Support Plans 00:03:00 Alternative Support Options 00:02:00 Service Level Agreements ( SLA's ) 00:03:00 Composite SLA's 00:03:00 Improving Application SLA's 00:04:00 Public and Preview Features 00:01:00 Providing Feedback 00:01:00 General Availability 00:01:00 Azure Updates , Announcements and Roadmaps 00:01:00 Course Conclusion Course Conclusion 00:01:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Artificial Intelligence is here. What does it mean for Project Management and Project Managers? I delivered a special live briefing on 1 November 2022, to answer the questions I was asking: 🤖 What is Artificial Intelligence? 🦾 What is the role of AI in Project Management? ⚠️ What are the issues surrounding AI? 🧩 What do Project Managers need to do to prepare ourselves? ⚖️ And ended with my assessment of Artificial Intelligence in Project Management

This course does not require any prior knowledge of Apache Spark or Hadoop. The author explains Spark architecture and fundamental concepts to help you come up to speed and grasp the content of this course. The course will help you understand Spark programming and apply that knowledge to build data engineering solutions.

Dive deep into the world of ChatGPT with our ChatGPT Masterclass. From basic functionalities to advanced applications across various domains, this course equips you with the knowledge to leverage ChatGPT effectively. Enhance your professional skills, academic pursuits, and personal projects by mastering ChatGPT today. Learning Outcomes Understand the mechanics behind ChatGPT’s responses. Create precise and effective prompts for ChatGPT. Utilize ChatGPT for creative and technical writing. Leverage ChatGPT for educational support and learning. Integrate ChatGPT with Microsoft Excel for data management. Employ ChatGPT to achieve professional excellence. Explore practical and innovative ChatGPT prompts. Implement ChatGPT strategies in social media marketing. Understand the capabilities of ChatGPT Plus and New Bing. Course Curriculum Module 01: Getting Started with ChatGPT Introduction to ChatGPT: Basics of how to interact with and utilize ChatGPT effectively. Module 02: Understanding ChatGPT How ChatGPT Works: Insights into the AI and machine learning principles powering ChatGPT. Module 03: Crafting Effective ChatGPT Prompts: A Guide Prompt Engineering: Techniques for developing precise prompts that generate desired outcomes. Module 04: Writing with ChatGPT Creative and Technical Writing: Using ChatGPT to assist with various writing tasks and projects. Module 05: ChatGPT for Students Educational Applications: How students can use ChatGPT for studying, research, and homework assistance. Module 06: ChatGPT for MS Excel Excel Integration: Harnessing ChatGPT for automating tasks and analyzing data in Microsoft Excel. Module 07: ChatGPT for Professional Excellence Career Development: Applying ChatGPT in professional settings for communication, problem-solving, and innovation. Module 08: Useful ChatGPT Prompts Practical Prompts: A collection of effective ChatGPT prompts for various uses. Module 09: Social Media Marketing with ChatGPT Marketing Strategies: Leveraging ChatGPT for content creation, customer engagement, and campaign management. Module 10: ChatGPT Plus and New Bing Advanced Features: Exploring the enhanced capabilities and applications of ChatGPT Plus and New Bing. Module 11: ChatGPT in Personal Life Everyday Uses: Practical ways to incorporate ChatGPT into daily personal tasks and activities. Module 12: The Future with ChatGPT Looking Ahead: Discussing potential future developments in ChatGPT technology and its implications for various sectors.

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

Artificial Intelligence isn’t science fiction anymore — it’s shaping the way we search, shop, scroll, and sometimes even spill the tea. This course lays the groundwork for understanding how AI actually works — minus the jargon and dramatic movie scenes. You’ll explore essential concepts such as algorithms, data patterns, logic systems, and machine-based learning models, all presented in a format that makes sense even if your only prior experience with AI is arguing with your phone's voice assistant. Perfect for curious minds across industries, this foundational course covers the key principles that drive AI technologies, from basic neural networks to the role of big data in decision-making. Whether you're brushing up for academic reasons or looking to speak AI without sounding like a tech cliché, you’ll find this course insightful, neatly organised, and refreshingly down-to-earth. All content is delivered online, allowing you to study at your own pace — no awkward group projects or lab goggles required. Key Features CPD Accredited FREE PDF + Hardcopy certificate Fully online, interactive course Self-paced learning and laptop, tablet and smartphone-friendly 24/7 Learning Assistance Discounts on bulk purchases Course Curriculum Module 01 : Introduction to Artificial Intelligence Module 02 : Mathematics for AI Module 03 : Knowledge Representation in AI - Part 1 Module 04 : Knowledge Representation in AI - Part 2 Module 05 : Machine Learning - Part 1 Module 06 : Machine Learning - Part 2 Module 07 : Deep Learning Module 08 : Natural Language Processing Module 09 : Computer Vision Module 10 : Robotics Module 11 : Building AI Applications Learning Outcomes: Grasp the fundamentals of artificial intelligence and its applications. Develop a strong mathematical foundation for AI algorithms. Master knowledge representation techniques in AI. Explore the principles and applications of machine learning. Dive into the world of deep learning and its use in AI. Understand the core concepts of natural language processing and computer vision. Accreditation This course is CPD Quality Standards (CPD QS) accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Technology enthusiasts eager to delve into AI. Students pursuing a career in AI and machine learning. Professionals seeking to upskill in the AI domain. Engineers and programmers interested in AI development. Entrepreneurs exploring AI for business solutions. Anyone with a curiosity about the future of artificial intelligence. Graduates looking to enhance their tech-related knowledge. Innovators with an interest in robotics and AI applications. Career path AI Research Scientist Machine Learning Engineer Data Scientist Natural Language Processing Engineer Computer Vision Specialist Robotics Software Engineer Certificates Digital certificate Digital certificate - Included Once you've successfully completed your course, you will immediately be sent a FREE digital certificate. Hard copy certificate Hard copy certificate - Included Also, you can have your FREE printed certificate delivered by post (shipping cost £3.99 in the UK). For all international addresses outside of the United Kingdom, the delivery fee for a hardcopy certificate will be only £10. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Overview This comprehensive course on Python for Data Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python for Data Analysis comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is this course for? There is no experience or previous qualifications required for enrolment on this Python for Data Analysis. It is available to all students, of all academic backgrounds. Requirements Our Python for Data Analysis is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 19 sections • 99 lectures • 00:08:00 total length •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 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 •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 •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 •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 •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 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •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 •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 •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 •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 •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 •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 •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 •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 •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 •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 •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- Python for Data Analysis: 00:00:00

Building Data Science Products? Think Business First Modern machine learning libraries are both a blessing and a curse. Due to the ease with which the libraries can be used, most users (newbies and practitioners alike) focus too much on tools and techniques. We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies.Learning Objectives We will discuss the high-level thinking process of coming up with a machine learning algorithm by asking a business question before even thinking about the tools or technologies. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies. Fractions of PDUs may also be reported. The smallest increment of a PDU that can be reported is 0.25. This means that if you spent 15 minutes participating in a qualifying PDU activity, you may report 0.25 PDU. If you spend 30 minutes in a qualifying PDU activity, you may report 0.50 PDU.