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  • Coursera
    • Board Infinity
      • .NET FullStack Developer Specialization
        • Course 1 – .Net Full Stack Foundation
          • Week 1 – Introduction to ASP.NET
          • Week 2 – C# Programming Fundamentals
          • Week 3 – Advanced C# Programming
        • Course 2 – Frontend Development using React
          • Week 1 – Introduction to HTML & CSS
          • Week 2 – Introduction to JavaScript Programming
          • Week 3 – React for Frontend development
        • Course 3 – Backend Development for .Net Full Stack
          • Week 1 – ASP.NET Core
          • Week 2 – ASP.NET MVC
          • Week 3 – ASP.NET Web API
      • Java Full Stack Developer Specialization
        • Course 1 – Fundamentals of Java Programming
          • Week 1 – Java Fundamentals
          • Week 2 – Core Java
          • Week 3 – OOPS and Other Essential Concepts
        • Course 2 – Frontend for Java Full Stack Development
          • Week 1 – Introduction to HTML & CSS
          • Week 2 – Introduction to JavaScript Programming
          • Week 3 – Angular for Frontend development
        • Course 3 – Data Structures & Backend with Java
          • Week 1 – Data Structures & Backend with Java
          • Week 2 – Spring and Spring Boot Introduction
          • Week 3 – RESTFul web services and Spring Boot Security
    • DeepLearning.AI
      • Deep Learning Specialization
        • Course 1 – Neural Networks and Deep Learning
          • Week 1 – Introduction to Deep Learning
          • Week 2 – Neural Networks Basics
          • Week 3 – Shallow Neural Networks
          • Week 4 – Deep Neural Networks
        • Course 2 – Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
          • Week 1 – Practical Aspects of Deep Learning
          • Week 2 – Optimization Algorithms
          • Week 3 – Hyperparameter Tuning, Batch Normalization and Programming Frameworks
        • Course 3 – Structuring Machine Learning Projects
          • Module 1 – ML Strategy
          • Module 2 – ML Strategy
        • Course 4 – Convolutional Neural Networks
          • Module 1 – Foundations of Convolutional Neural Network
          • Module 2 – Deep Convolutional Models: Case Studies
          • Module 3 – Object Detection
          • Module 4 – Special Applications: Face recognition & Neural Style Transfer
        • Course 5 – Sequence Models
          • Module 1 – Recurrent Neural Networks
          • Module 2 – Natural Language Processing & Word Embeddings
          • Module 3 – Sequence Model & Attention Mechanism
          • Module 4 – Transformer Network
    • Google
      • Google Data Analytics
        • Course 1: Foundations: Data, Data, Everywhere
          • Week 1: Introducing data analytics
          • Week 2: All about analytical thinking
          • Week 3: The wonderful world of data
          • Week 4: Set up your toolbox
          • Week 5: Endless career possibilities
          • Week 5: Endless career possibilities | Course challenge
        • Course 2: Ask Questions to Make Data-Driven Decisions
          • Week 1: Effective questions
          • Week 2: Data-driven decisions
          • Week 3: More spreadsheet basics
          • Week 4: Always remember the stakeholder
          • Week 4: Always remember the stakeholder | Course challenge
        • Course 3: Prepare Data for Exploration
          • Week 1: Data types and structures
          • Week 2: Bias, credibility, privacy, ethics, and access
          • Week 3: Databases: Where data lives
          • Week 4: Organizing and protecting your data
          • Week 5: Course challenge | Prepare data for exploration
        • Course 4: Process Data from Dirty to Clean​
          • Week 1: The importance of integrity
          • Week 2: Sparkling-clean data
          • Week 3: Cleaning data with SQL
          • Week 4: Verify and report on your cleaning results
          • Week 6: Course challenge | Process data from dirty to clean
        • Course 5: Analyze Data to Answer Questions
          • Week 1: Organizing data to begin analysis
          • Week 2: Formatting and adjusting data
          • Week 3: Aggregating data for analysis
          • Week 4: Performing data calculations
          • Week 4: Course challenge | Performing data calculations
        • Course 6: Share Data Through the Art of Visualization
          • Week 1: Visualizing data
          • Week 2: Creating data visualizations with Tableau
          • Week 3: Crafting data stories
          • Week 4: Developing presentations and slideshows
          • Week 4: Course challenge | Share data through the art of visualization
        • Course 7: Data Analysis with R Programming
          • Week 1: Programming and data analytics
          • Week 2: Programming using RStudio
          • Week 3: Working with data in R
          • Week 4: More about visualizations, aesthetics, and annotations
          • Week 5: Documentation and reports
          • Week 5: Course challenge | Data analysis with r programming
        • Course 8: Google Data Analytics Capstone: Complete a Case Study
          • Week 1: Learn about capstone basics
          • Week 3: Optional: Using your portfolio
      • Google Advanced Data Analytics
        • Course 1 – Foundations of Data Science
          • Week 1 – Introduction to data science concepts
          • Week 2 – The impact of data today
          • Week 3 – Your career as a data professional
          • Week 4 – Data applications and workflow
          • Week 5 – Assess your Course 1 end-of-course project
        • Course 2 – Get Started with Python
          • Week 1 – Hello, Python!
          • Week 2 – Functions and conditional statements
          • Week 3 – Loops and strings
          • Week 4 – Data structures in Python
          • Week 5 – Course 2 end-of-course project
        • Course 3 – Go Beyond the Numbers: Translate Data into Insights
          • Week 1 – Find and share stories using data
          • Week 2 – Explore raw data
          • Week 3 – Clean your data
          • Week 4 – Data visualizations and presentations
          • Week 5 – Assess your Course 3 end-of-course project
        • Course 4 – The Power of Statistics
          • Week 1 – Introduction to statistics
          • Week 2 – Probability
          • Week 3 – Sampling
          • Week 4 – Confidence intervals
          • Week 5 – Introduction to hypothesis testing
          • Week 6 – Course 4 end-of-course project
        • Course 5 – Regression Analysis: Simplify Complex Data Relationships
          • Week 1 – Introduction to complex data relationships
          • Week 2 – Simple linear regression
          • Week 3 – Multiple linear regression
          • Week 4 – Advanced hypothesis testing
          • Week 5 – Logistic regression
          • Week 6 – Course 5 end-of-course project
        • Course 6 – The Nuts and Bolts of Machine Learning
          • Week 1 – The different types of machine learning
          • Week 2 – Workflow for building complex models
          • Week 3 – Unsupervised learning techniques
          • Week 4 – Tree-based modeling
          • Week 5 – Assess your Course 6 end-of-course project
      • Google Business Intelligence
        • Course 1 – Foundations of Business Intelligence
          • Week 1 – Data-driven results through business intelligence
          • Week 2 – Business intelligence tools and techniques
          • Week 3 – Context is crucial for purposeful insights
          • Week 4 – Course 1 end-of-course project
        • Course 2 – The Path to Insights: Data Models and Pipelines
          • Week 1 – Data models and pipelines
          • Week 2 – Dynamic database design
          • Week 3 – Optimize ETL processes
          • Week 4 – Course 2 end-of-course project
        • Course 3 -Decisions, Decisions: Dashboards and Reports
          • Week 1 – Business intelligence visualizations
          • Week 2 – Visualize results
          • Week 3 – Automate and monitor
          • Week 4 – Present business intelligence insights
          • Week 5 – Course 3 end-of-course project
      • Google Cybersecurity
        • Course 1 – Foundations of Cybersecurity
          • Week 1 – Welcome to the exciting world of cybersecurity
          • Week 2 – The evolution of cybersecurity
          • Week 3 – Protect against threats, risks, and vulnerabilities
          • Week 4 – Cybersecurity tools and programming languages
        • Course 2 – Play It Safe: Manage Security Risks
          • Week 1 Security domains
          • Week 2 – Security frameworks and controls
          • Week 3 – Introduction to cybersecurity tools
          • Week 4 – Use playbooks to respond to incidents
        • Course 3 – Connect and Protect: Networks and Network Security
          • Week 1 – Network architecture
          • Week 2 – Network operations
          • Week 3 – Secure against network intrusions
          • Week 4 – Security hardening
        • Course 4 – Tools of the Trade: Linux and SQL
          • Week 1 – Introduction to operating systems
          • Week 2 – The Linux operating system
          • Week 3 – Linux commands in the Bash shell
          • Week 4 – Databases and SQL
        • Course 5 – Assets, Threats, and Vulnerabilities
          • Week 1 – Introduction to asset security
          • Week 2 – Protect organizational assets
          • Week 3 – Vulnerabilities in systems
          • Week 4 – Threats to asset security
        • Course 6 – Sound the Alarm: Detection and Response
          • Week 1 – Introduction to detection and incident response
          • Week 2 – Network monitoring and analysis
          • Week 3 -Incident investigation and response
          • Week 4 – Network traffic and logs using IDS and SIEM tools
        • Course 7 – Automate Cybersecurity Tasks with Python
          • Week 1 – Introduction to Python
          • Week 2 – Write effective Python code
          • Week 3 -Work with strings and lists
          • Week 4 – Python in practice
        • Course 8 – Put It to Work: Prepare for Cybersecurity Jobs
          • Week 1 – Protect data and communicate incidents
          • Week 2 – Escalate incidents
          • Week 3 – Communicate effectively to influence stakeholders
          • Week 4 – Engage with the cybersecurity community
          • Week 5 – Find and apply for cybersecurity jobs
    • IBM
      • IBM Data Analytics with Excel and R
        • Course 1: Introduction to data analytics
          • Module 1: What is Data Analytics
          • Module 2: The Data Ecosystem
          • Module 3: Gathering and Wrangling Data
          • Module 4: Mining & Visualizing Data and Communicating Results
          • Module 5: Career Opportunities and Data Analysis in Action
        • Course 2: Excel Basics for Data Analysis
          • Module 1: Introduction to Data Analysis Using Spreadsheets
          • Module 2: Getting Started with Using Excel Speadsheets
          • Module 3: Cleaning & Wrangling Data Using Spreadsheets
          • Module 4: Analyzing Data Using Spreadsheets
        • Course 3: Data Visualization and Dashboards with Excel and Cognos
          • Module 1: Visualizing Data Using Spreadsheets
          • Module 2: Creating Visualizations and Dashboards with Spreadsheets
          • Module 3: Creating Visualizations and Dashboards with Cognos Analytics
        • Course 5: Introduction to R Programming for Data Science
          • Module 1: R Basics
          • Module 2: Common Data Structures
          • Module 3: R Programming Fundamentals
          • Module 4: Working with Data
          • Module 5: Final Project
        • Course 6: SQL for Data Science with R
          • Module 1: Getting Started with SQL
          • Module 2: Introduction to Relational Databases and Tables
          • Module 3: Intermediate SQL
          • Module 4: Getting Started with Databases using R
          • Module 5: Working with Database Objects using R
          • Module 6: Course Project
        • Course 7: Data Analysis with R
          • Module 1: Introduction to Data Analysis with R
          • Module 2: Data Wrangling
          • Module 3: Exploratory Data Analysis
          • Module 4: Model Development in R
          • Module 5: Model Evaluation
          • Module 6: Project
        • Course 8: Data Visualization with R
          • Module 1: Introduction to Data Visualization
          • Module 2: Basic Plots, Maps, and Customization
          • Module 3: Dashboards
          • Module 4: Final Assignment
        • Course 9: Data Science with R – Capstone Project
          • Module 1: Capstone Overview and Data Collection
          • Module 2: Data Wrangling
          • Module 3: Performing Exploratory Data Analysis with SQL, Tidyverse & ggplot2
          • Module 4: Predictive Analysis
          • Module 5: Building a R Shiny Dashboard App
      • IBM Data Science
        • Course 8 – Data Visualization with Python
          • Week 1 – Introduction to Data Visualization Tools
          • Week 2 – Basic and Specialized Visualization Tools
          • Week 3 – Advanced Visualizations and Geospatial Data
          • Week 4 – Creating Dashboards with Plotly and Dash
          • Week 5 – Final Project and Exam
        • Course 9 – Machine Learning with Python
          • Week 1 – Introduction to Machine Learning
          • Week 2 – Regression
          • Week 3 – Classification
          • Week 4 – Linear Classification
          • Week 5 – Clustering
          • Week 6 – Final Exam and Project
        • Course 10 – Applied Data Science Capstone
          • Week 1 – Introduction
          • Week 2 – Exploratory Data Analysis (EDA)
          • Week 3 – Interactive Visual Analytics and Dashboard
          • Week 4 – Predictive Analysis (Classification)
        • Course 2 – Tools for Data Science
          • Week 1 – Overview of Data Science Tools
          • Week 2 – Languages of Data Science
          • Week 3 – Packages, APIs, Datasets and Models
          • Week 4 – Jupyter Notebooks and JupyterLab
          • Week 5 – RStudio & GitHub
          • Week 6 – Create and Share your Jupyter Notebook
          • Week 7 – [Optional] IBM Watson Studio
        • Course 3 – Data Science Methodology
          • Week 1 – From Problem to Approach and From Requirements to Collection
          • Week 2 – From Understanding to Preparation and From Modeling to Evaluation
          • Week 3 – From Deployment to Feedback
        • Course 4 – Python for Data Science, AI & Development
          • Week 1 – Python Basics
          • Week 2 – Python Data Structures
          • Week 3 – Python Programming Fundamentals
          • Week 4 – Working with Data in Python
          • Week 5 – Crowdsourcing Short squeeze Dashboard
        • Course 5 – Python Project for Data Science
          • Week 1 – Crowdsourcing Short squeeze Dashboard
        • Course 6 – Databases and SQL for Data Science with Python
          • Week 1 – Getting Started with SQL
          • Week 2 – Introduction to Relational Databases and Tables
          • Week 3 – Intermediate SQL
          • Week 4 – Accessing Databases using Python
          • Week 5 – Course Assignment
          • Week 6 – Bonus Module: Advanced SQL for Data Engineering (Honors)
        • Course 1 – What is Data Science?
          • Week 2 – Data Science Topics
          • Week 3 – Data Science in Business
          • Week 1 – Defining Data Science and What Data Scientists Do
        • Course 7 – Data Analysis with Python
          • Week 6 – Final Assignment
          • Week 1 – Importing Datasets
          • Week 2 – Data Wrangling
          • Week 3 – Exploratory Data Analysis
          • Week 4 – Model Development
          • Week 5 – Model Evaluation
      • IBM Data Analyst
        • Course 1 – Introduction to Data Analytics
          • Week 1 – What is Data Analytics
          • Week 2 – The Data Ecosystem
          • Week 3 – Gathering and Wrangling Data
          • Week 4 – Mining & Visualizing Data and Communicating Results
          • Week 5 – Career Opportunities and Data Analysis in Action
        • Course 2 – Excel Basics for Data Analysis
          • Week 2 – Getting Started with Using Excel Speadsheets
          • Week 3 – Cleaning & Wrangling Data Using Spreadsheets
          • Week 4 – Analyzing Data Using Spreadsheets
          • Week 1 – Introduction to Data Analysis Using Spreadsheets
        • Course 4 – Python for Data Science, AI & Development
          • Week 1 – Python Basics
          • Week 2 – Python Data Structures
          • Week 3 – Python Programming Fundamentals
          • Week 4 – Working with Data in Python
          • Week 5 – Crowdsourcing Short squeeze Dashboard
        • Course 5 – Python Project for Data Science
          • Week 1 – Crowdsourcing Short squeeze Dashboard
        • Course 9 – IBM Data Analyst Capstone Project
          • Week 1 – Data Collection
          • Week 2 – Data Wrangling
          • Week 3 – Exploratory Data Analysis
          • Week 4 – Data Visualization
        • Course 6 – Databases and SQL for Data Science with Python
          • Week 1 – Getting Started with SQL
          • Week 2 – Introduction to Relational Databases and Tables
          • Week 3 – Intermediate SQL
          • Week 4 – Accessing Databases using Python
          • Week 5 – Course Assignment
          • Week 6 – Bonus Module: Advanced SQL for Data Engineering (Honors)
        • Course 7 – Data Analysis with Python
          • Week 1 – Importing Datasets
          • Week 2 – Data Wrangling
          • Week 3 – Exploratory Data Analysis
          • Week 4 – Model Development
          • Week 5 – Model Evaluation
          • Week 6 – Final Assignment
        • Course 3 – Data Visualization and Dashboards with Excel and Cognos
          • Week 2 – Creating Visualizations and Dashboards with Spreadsheets
          • Week 3 – Creating Visualizations and Dashboards with Cognos Analytics
          • Week 1 – Visualizing Data Using Spreadsheets
        • Course 8 – Data Visualization with Python
          • Week 1 – Introduction to Data Visualization Tools
          • Week 2 – Basic and Specialized Visualization Tools
          • Week 3 – Advanced Visualizations and Geospatial Data
          • Week 4 – Creating Dashboards with Plotly and Dash
          • Week 5 – Final Project and Exam
      • Key Technologies for Business
        • Course 1: Introduction to Cloud Computing
          • Week 1 – Overview of Cloud Computing
          • Week 2 – Cloud Computing Models
          • Week 3 – Components of Cloud Computing
          • Week 4 – Emergent Trends and Practices
          • Week 5 – Cloud Security, Monitoring, Case Studies, Jobs
          • Week 6 – Final Project and Assignment
        • Course 2: Introduction to Artificial Intelligence (AI)
          • Week 1 – What is AI? Applications and Examples of AI
          • Week 2 – AI Concepts, Terminology, and Application Areas
          • Week 3 – AI: Issues, Concerns and Ethical Considerations
          • Week 4 – The Future with AI, and AI in Action
        • Course 3: What is Data Science?
          • Week 1 – Defining Data Science and What Data Scientists Do
          • Week 2 – Data Science Topics
          • Week 3 – Data Science in Business
    • Intuit
      • Intuit Academy Bookkeeping
        • Course 3 – Liabilities and Equity in Accounting
          • Week 1 – Liabilities and Equity in Accounting
          • Week 2 – Payroll, Obligations, and Loans
          • Week 3 – Equity and Liabilities
          • Week 4 – Practice with Liabilities and Equity
        • Course 4 – Financial Statement Analysis
          • Week 1 – Understanding Reconciliations
          • Week 2 – How to Read Financial Statements
          • Week 3 – Analyzing Key Reports and Transactions
          • Week 4 – Application and Practice with Reconciliations and Financial Analysis
        • Course 1 – Bookkeeping Basics
          • Week 3 – The Accounting Cycle (Part 2)
          • Week 4 – Accounting Principles and Practices
          • Week 1 – Accounting Concepts and Measurement
          • Week 2 – The Accounting Cycle (Part 1)
        • Course 2 – Assets in Accounting
          • Week 2 – Inventory Accounting Methods
          • Week 3 – Property and Equipment
          • Week 1 – Accounting Concepts and Measurement
          • Week 4 – Applying Accounting Principles and Knowledge
    • Meta
      • Meta AR Developer
        • Course 2 – AR in marketing using Meta Spark
          • Week 1 – Meta Spark Quick Start
          • Week 2 – Meta Spark fundamentals
          • Week 3 – Meta Spark pro
          • Week 4 – Game creation in Meta Spark
        • Course 3 – AR for web using JavaScript
          • Week 1 – Basics of Web AR development
          • Week 2 – Javascript in PlayCanvas
          • Week 3 – Content development and integration
          • Week 4 – Creating an AR learning experience with PlayCanvas
        • Course 1 – Foundations of AR
          • Week 3 – Computer vision
          • Week 4 – AR software development lifecycle
          • Week 1 – Introduction to AR
          • Week 2 – AR technologies and capabilities
        • Course 4 – Unity and C# basics
          • Week 1 – Introduction to Unity
          • Week 2 – Asset creation and player controls
          • Week 3 – C# basics in Unity
          • Week 4 – C# and basic gameplay
        • Course 5 – Using AR Foundation in Unity
          • Week 1 – Say hello to AR in Unity
          • Week 2 – AR Foundation marker-based game creation
          • Week 3 – Create your first AR game using AR Foundation
          • Week 4 – Finish and deploy your first AR game using AR Foundation
        • Course 6 – AR games using Vuforia SDK
          • Week 3 – Create an AR game using Vuforia
          • Week 4 – Finish and deploy your AR game built with Vuforia
          • Week 1 – Introduction to Vuforia and plane detection in Unity
          • Week 2 – Use plane detection to build a portal to an AR world
      • Meta Front-End Developer
        • Course 3 – Version Control
          • Week 1 – Software collaboration
          • Week 3 – Working with Git
          • Week 2 – Command Line
          • Week 4 – Graded Assessment
        • Course 2 – Programming with JavaScript
          • Week 1 – Introduction to Javascript
          • Week 2 – The Building Blocks of a Program
          • Week 3 – Programming Paradigms
          • Week 4 – Testing
          • Week 5 – End-of-Course Graded Assessment
        • Course 4 – HTML and CSS in depth
          • Week 1 – HTML in depth
          • Week 2 – Interactive CSS
        • Course 5 – React Basics
          • Week 2 – Data and State
          • Week 1 – React Components
          • Week 3 – Navigation, Updating and Assets in React.js
        • Course 9 – Coding Interview Preparation
          • Week 1 – Introduction to the coding interview
          • Week 2 – Introduction to Data Structures
          • Week 3 – Introduction to Algorithms
          • Week 4 – Final project
        • Course 1 – Introduction to Front-End Development
          • Week 2 – Introduction to HTML and CSS
          • Week 3 – UI Frameworks
          • Week 4 – End-of-Course Graded Assessment
          • Week 1 – Get started with web development
        • Course 7 – Principles of UX/UI Design
          • Week 3 – Applied Design Fundamentals
          • Week 4 – Designing your UI
          • Week 5 – Course summary and final assessment
          • Week 1 – Introduction to UX and UI design
          • Week 2 – Evaluating interactive design
        • Course 8 – Front-End Developer Capstone
          • Week 2 – Project foundations
          • Week 3 – Project functionality
          • Week 4 – Project Assessment
          • Week 1 – Starting the project
        • Course 6 – Advanced React
          • Week 3 – JSX and testing
          • Week 4 – Final project
          • Week 1 – Components
          • Week 2 – React Hooks and Custom Hooks
      • Meta Social Media Marketing
        • Course 2 – Social Media Management
          • Week 1 – Establish Your Presence
          • Week 2 – Social Media Content
          • Week 3 – Social Media Content Management
          • Week 4 – Evaluate Your Efforts
        • Course 1 – Introduction to Social Media Marketing
          • Week 5 – Choose Your Social Media Channels
          • Week 1 – The Social Media Landscape
          • Week 2 – Social Media Platforms Overview
          • Week 3 – Goals and Planning for Success
          • Week 4 – Understand Your Audience
        • Course 3 – Fundamentals of Social Media Advertising
          • Week 1 – Introduction to Social Media Advertising
          • Week 4 – Getting Started with Advertising on Facebook and Instagram
          • Week 3 – Data, Privacy and Policies on Social Media
          • Week 2 – Creating Effective Ads on Social Media
        • Course 4 – Advertising with Meta
          • Week 4 – Create Your Ads and Evaluate Your Campaign Results
          • Week 5 – Build Your Own Campaign in Ads Manager
          • Week 2 – Determine Your Campaign Objective and Budget
          • Week 1 – First Steps in Ads Manager
          • Week 3 – Select Your Audience, Platforms and Schedule
        • Course 5 – Measure and Optimize Social Media Marketing Campaigns
          • Week 1 – Evaluate Your Marketing Results Against Goals
          • Week 4 – Communicate Your Marketing Results
          • Week 2 – Measure Your Advertising Effectiveness
          • Week 3 – Optimize Your Ad Campaigns
    • Microsoft
      • Microsoft Power BI Data Analyst
        • Course 1 – Preparing Data for Analysis with Microsoft Excel
          • Module 1 – Excel fundamentals
          • Module 2 – Formulas and functions
          • Module 3 – Preparing data for analysis using functions
          • Module 4 – Final project and assessment: Preparing data for analysis with Microsoft Excel
        • Course 2 – Harnessing the Power of Data with Power BI
          • Module 1 – Data analysis in business
          • Module 2 – The right tools for the job
          • Module 3 – Final project and assessment: Harnessing the power of data in Power BI
        • Course 3 – Extract, Transform and Load Data in Power BI
          • Module 1 – Data Sources in Power BI
          • Module 2 – Transforming Data in Power BI
          • Module 3 – Advanced ETL in PowerBI
          • Module 4 – Graded Assessment and Course wrap up
        • Course 4 – Data Modeling in Power BI
          • Module 1 – Concepts for data modeling
          • Module 2 – Using Data Analysis Expressions (DAX) in Power BI
          • Module 3 – Optimize a model for performance in Power BI
          • Module 4 – Final project and assessment: Modeling data in Power BI
        • Course 5 – Data Analysis and Visualization with Power BI
          • Module 1 – Creating Reports
          • Module 2 – Navigation and Accessibility
          • Module 3 – Bringing Data to the User
          • Module 4 – Identifying Patterns and Trends
          • Module 5 – Guided Project: Data Analysis and Visualization with Microsoft Power BI
          • Module 6 – Final project and assessment: Data Analysis and Visualization with Power BI
        • Course 6 – Creative Designing in Power BI
          • Module 1 – Visualization and Design
          • Module 2 – Designing Powerful Report Pages
          • Module 3 – Dashboard Design and Storytelling
          • Module 4 – Final project and assessment: Creative Design in Power BI
        • Course 7 – Deploy and Maintain Power BI Assets and Capstone project
          • Module 1 – Power BI in enterprise
          • Module 2 – Deploying assets
          • Module 3 – Security and monitoring
          • Module 4 – Guided project: Deploy and maintain Power BI Assets and Capstone project
          • Module 5 – Capstone project
        • Course 8 – Microsoft PL-300 Exam Preparation and Practice
          • Module 1 – Preparing Data
          • Module 2 – Modeling Data
          • Module 3 – Visualize and analyze data
          • Module 4 – Deploy and maintain assets
          • Module 5 – Practice Exam
      • Microsoft UX Design
        • Course 1: Fundamentals of UI/UX Design
          • Module 1: Introduction to user experience design
          • Module 2: Roles in a UX design team
          • Module 3: Frameworks and process: Design thinking
          • Module 4: Introducing the portfolio project
        • Course 2: Designing for User Experience
          • Module 1: User research and empathy building
          • Module 2: User needs assessment
          • Module 3: Design ideation
          • Module 4: Storyboarding
          • Module 5: Information architecture
        • Course 3: User Interface Design and Prototyping
          • Module 1: Wireframes and mockups
          • Module 2: Design systems and style guides
          • Module 3: Interactive prototypes
          • Module 4: Design critiques and user testing
        • Course 4: UX Design in Practice: Accessibility and Collaboration
          • Module 1: Visual design, high-fidelity mockups, and platform considerations
          • Module 2: Accessibility and inclusive design
          • Module 3: AI for UX design
          • Module 4: Collaborative design and communication
          • Module 5: Portfolio presentation and job resources
    • Stanford
      • Machine Learning Specialization
        • Course 2 – Advanced Learning Algorithms
          • Week 1: Neural Networks
          • Week 2: Neural network training
          • Week 3: Advice for applying machine learning
          • Week 4: Decision trees
        • Course 3 – Unsupervised Learning, Recommenders, Reinforcement Learning
          • Week 3: Reinforcement learning
          • Week 1: Unsupervised learning
          • Week 2: Recommender systems
        • Course 1 – Supervised Machine Learning: Regression and Classification
          • Week 3: Classification
          • Week 1: Introduction to Machine Learning
          • Week 2: Regression with multiple input variables
    • University Of Michigan
      • Python for Everybody Specialization
        • Course 1: Programming for Everybody (Getting Started with Python)
          • Week 3 – Chapter One: Why We Program (continued)
          • Week 4 – Chapter Two: Variables and Expressions
          • Week 5 – Chapter Three: Conditional Code
          • Week 6 – Chapter Four: Functions
          • Week 7 – Chapter Five: Loops and Iteration
        • Course 2: Python Data Structures
          • Week 1 – Chapter Six: Strings
          • Week 3 – Chapter Seven: Files
          • Week 4 – Chapter Eight: Lists
          • Week 5 – Chapter Nine: Dictionaries
          • Week 6 – Chapter Ten: Tuples
        • Course 3: Using python to access web data
          • Week 2 – Regular Expressions (Chapter 11)
          • Week 3 – Networks and Sockets (Chapter 12)
          • Week 4 – Programs that Surf the Web (Chapter 12)
          • Week 5 – Web Services and XML (Chapter 13)
          • Week 6 – JSON and the REST Architecture (Chapter 13)
        • Course 4: Using Databases with Python
          • Week 1 – Object Oriented Python
          • Week 2 – Basic Structured Query Language
          • Week 3 – Data Models and Relational SQL
          • Week 4 – Many-to-Many Relationships in SQL
        • Course 5: Capstone: Retrieving, Processing, and Visualizing Data with Python
  • Linkedin
    • .Net Framework
    • A
      • Accounting
      • Adobe Illustrator
      • Adobe InDesign
      • Adobe Lightroom
      • Adobe Photoshop
      • Adobe Premiere Pro
      • Adobe XD
      • Adobe After Effects
      • Agile Methodologies
      • Amazon Web Services (AWS)
      • Android
      • Angular
      • Arcgis Products
      • AutoCAD
      • Autodesk Fusion 360
      • Autodesk Maya
      • Avid Media Composer
      • AWS Lambda
      • Adobe Acrobat
      • Adobe Animate
    • B
      • Bash
    • C
      • C++
      • Cascading Style Sheets (CSS)
      • Cybersecurity
      • C (Programming Language)
      • C#
    • D
      • Django
      • Dreamweaver
    • E
      • Eclipse
    • F
      • Final Cut Pro
      • Front-End Development
    • G
      • Git
      • GO (Programming Language)
      • Google Ads
      • Google Analytics
      • Google Cloud Platform (GCP)
    • H
      • Hadoop
      • HTML
    • I
      • IT Operations
      • iMovie
    • J
      • JavaScript
      • jQuery
    • K
      • Kotlin
      • Keynote
    • L
      • Linux
      • Logic Pro
    • M
      • Machine Learning
      • Matlab
      • Maven
      • Microsoft Access
      • Microsoft Azure
      • Microsoft Excel
      • Microsoft Outlook
      • Microsoft Power BI
      • Microsoft PowerPoint
      • Microsoft Project
      • MySql
    • N
      • Node.js
      • NoSQL
    • O
      • Object-Oriented Programming (OOP)
      • Objective-C
    • P
      • PHP
      • Pro Tools
      • Python
    • Q
      • Quickbooks
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Deep Learning Specialization Answers

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