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Amazon
Amazon Junior Software Developer
Course 1:Â Introduction to Software Development
Module 1: Getting started with Java
Module 2: Control flow: Statements and loops
Module 3: Object-oriented programming basics
Module 4: Final project and assessment: Introduction to software development
Course 2: Programming with Java
Module 1: Advanced OOP concepts
Module 2: Defensive programming: Error handling
Module 3: File handling
Module 4: Final project and assessment: Programming with Java
Course 3: Data Structures and Algorithms
Module 1: Data structures
Module 2: Searching and sorting algorithms
Module 3: Basic Testing
Module 4: Final project and assessment: Data structures and algorithms
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 1 – 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
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 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 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)
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 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 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 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 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 – IBM Data Analyst Capstone Project
Week 1 – Data Collection
Week 2 – Data Wrangling
Week 3 – Exploratory Data Analysis
Week 4 – Data Visualization
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 1 – Bookkeeping Basics
Week 1 – Accounting Concepts and Measurement
Week 2 – The Accounting Cycle (Part 1)
Week 3 – The Accounting Cycle (Part 2)
Week 4 – Accounting Principles and Practices
Course 2 – Assets in Accounting
Week 1 – Accounting Concepts and Measurement
Week 2 – Inventory Accounting Methods
Week 3 – Property and Equipment
Week 4 – Applying Accounting Principles and Knowledge
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
Meta
Meta AR Developer
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 1 – Introduction to Vuforia and plane detection in Unity
Week 2 – Use plane detection to build a portal to an AR world
Week 3 – Create an AR game using Vuforia
Week 4 – Finish and deploy your AR game built with Vuforia
Course 1 – Foundations of AR
Week 1 – Introduction to AR
Week 2 – AR technologies and capabilities
Week 3 – Computer vision
Week 4 – AR software development lifecycle
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 4 – Unity and C# basics
Week 2 – Asset creation and player controls
Week 3 – C# basics in Unity
Week 4 – C# and basic gameplay
Week 1 – Introduction to Unity
Meta Social Media Marketing
Course 1 – Introduction to Social Media Marketing
Week 1 – The Social Media Landscape
Week 2 – Social Media Platforms Overview
Week 3 – Goals and Planning for Success
Week 4 – Understand Your Audience
Week 5 – Choose Your Social Media Channels
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 3 – Fundamentals of Social Media Advertising
Week 1 – Introduction to Social Media Advertising
Week 2 – Creating Effective Ads on Social Media
Week 3 – Data, Privacy and Policies on Social Media
Week 4 – Getting Started with Advertising on Facebook and Instagram
Course 4 – Advertising with Meta
Week 1 – First Steps in Ads Manager
Week 2 – Determine Your Campaign Objective and Budget
Week 3 – Select Your Audience, Platforms and Schedule
Week 4 – Create Your Ads and Evaluate Your Campaign Results
Week 5 – Build Your Own Campaign in Ads Manager
Course 5 – Measure and Optimize Social Media Marketing Campaigns
Week 1 – Evaluate Your Marketing Results Against Goals
Week 2 – Measure Your Advertising Effectiveness
Week 3 – Optimize Your Ad Campaigns
Week 4 – Communicate Your Marketing Results
Meta Front-End Developer
Course 8 – Front-End Developer Capstone
Week 1 – Starting the project
Week 2 – Project foundations
Week 3 – Project functionality
Week 4 – Project Assessment
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 3 – Version Control
Week 1 – Software collaboration
Week 2 – Command Line
Week 3 – Working with Git
Week 4 – Graded Assessment
Course 4 – HTML and CSS in depth
Week 1 – HTML in depth
Week 2 – Interactive CSS
Course 5 – React Basics
Week 1 – React Components
Week 2 – Data and State
Week 3 – Navigation, Updating and Assets in React.js
Course 1 – Introduction to Front-End Development
Week 1 – Get started with web development
Week 2 – Introduction to HTML and CSS
Week 3 – UI Frameworks
Week 4 – End-of-Course 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 6 – Advanced React
Week 1 – Components
Week 2 – React Hooks and Custom Hooks
Week 3 – JSX and testing
Week 4 – Final project
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
Microsoft
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
Microsoft Power BI Data Analyst
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
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 3 – Optimize a model for performance in Power BI
Module 4 – Final project and assessment: Modeling data in Power BI
Module 1 – Concepts for data modeling
Module 2 – Using Data Analysis Expressions (DAX) in Power BI
Stanford
Machine Learning Specialization
Course 1 – Supervised Machine Learning: Regression and Classification
Week 1: Introduction to Machine Learning
Week 2: Regression with multiple input variables
Week 3: Classification
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 1: Unsupervised learning
Week 2: Recommender systems
Week 3: Reinforcement learning
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
R
R (Programming Language)
React.js
REST APIs
Revit
Rhino 3D
Ruby on Rails
Rust (Programming Language)
S
Scala
Search Engine Optimization (SEO)
SharePoint
SketchUp
SOLIDWORKS
Spring Framework
Swift (Programming Language)
T
Transact-SQL (T-SQL)
U
Unity
V
Visio
Visual Basic for Applications (VBA)
W
Windows Server
WordPress
X
XML
Community
About
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