Artificial Intelligence Basics for Everyone Online Course

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Having background knowledge about different aspects of artificial intelligence, from machine learning to deep learning frameworks, from ethical aspects of artificial intelligence to data gathering using web scraping tools, from python programming to cloud-base services, in today's AI-based world is very useful and beneficial. Gone are the days when businesses make decisions based solely on speculation. In today's digital landscape, organizations are adopting a data-driven culture to avoid falling behind their competitors. Data-driven refers to a strategy method of exploiting data insights to identify new business possibilities, better serve customers, boost revenue, and enhance operations. It enables firms to make decisions based on evidence-based data and carefully prepare to achieve business objectives. Business leaders in data-driven organizations understand the benefits of relying on data insights to make wise business moves. Today, businesses' success depends upon full access to valuable data and quick action.

TechClass Artificial Intelligence Basics for Everone Online Course aims to give you background knowledge about various aspects of artificial intelligence, including machine learning, deep learning, deep learning frameworks like TensorFlow and PyTorch, and programming languages with the main focus on the Python. It also gives you a taste of business intelligence and its famous tools like Tableau and Microsoft PIt also ower BI as well as databases; relational ones and non-relational ones. By the end of this course, you will have a good basic understanding of the main pillars of data analysis and data science as your very first step in your journey to the data-driven world.

Learning outcomes

  • Get familiar with artificial intelligence and its application
  • Learn about the differences between artificial intelligence, machine learning, and deep learning
  • Learn about data science and its link to AI
  • Understand why data scientists need to have background knowledge in mathematics and statistics
  • Learn what business intelligence is and why it is important for businesses
  • Learn about top BI tools and how they meet businesses requirements
  • Learn about web scraping and its application in today's digital landscape
  • Learn about cloud base services, especially cloud base machine learning services provided by Microsoft and Amazon
  • Learn about natural language processing, its us-cases
  • Learn about exploratory data analysis and statistical data analysis

Table of contents

Chapter 1. Intro to Course

  • 1.1. Welcome!
  • 1.2. About TechClass Data Science Department
  • 1.3. Learning Outcomes
  • 1.4. Your Expectations, Goals, and Knowledge
  • 1.5. Abbreviations
  • 1.6. Copyright Notice

Chapter 2. Introduction to Artificial Intelligence

  • 2.1. What is AI?
  • 2.2. History of AI
  • 2.3. Some Applications of AI
  • 2.4. Test your Knowledge
  • 2.5. Recommended Courses

Chapter 3. Introduction to Machine Learning

  • 3.1. The concept of Learning
  • 3.2. What is Machine Learning?
  • 3.3. Machine Learning and Data
  • 3.4. Test your Knowledge
  • 3.5. Recommended Courses

Chapter 4. AI and Data Science

  • 4.1. What is Data Science?
  • 4.2. Who is a Data Scientist?
  • 4.3. Data Science Workflow
  • 4.4. Test your Knowledge
  • 4.5. Recommended Courses

Chapter 5. Introduction to Deep Learning

  • 5.1. What is Deep Learning?
  • 5.2. Deep Learning vs. Machine Learning
  • 5.3. Artificial NN vs. Biological NN
  • 5.4. Test your Knowledge
  • 5.5. Recommended Courses

Chapter 6. Ethics of AI

  • 6.1. What is Ethics?
  • 6.2. What is AI Ethics?
  • 6.3. AI Ethics Framework
  • 6.4. Test your Knowledge
  • 6.5. Recommended Courses

Chapter 7. Data Science and Programming

  • 7.1. What is Programming?
  • 7.2. Programming for Data Science
  • 7.3. Python for Data Science
  • 7.4. Test your Knowledge
  • 7.5. Recommended Courses

Chapter 8. Popular Frameworks of Deep Learning

  • 8.1. Deep Learning Frameworks
  • 8.2. TensorFlow
  • 8.3. Keras
  • 8.4. PyTorch
  • 8.5. Test your Knowledge
  • 8.6. Recommended Courses

Chapter 9. Natural Language Processing (NLP)

  • 9.1. What is NLP?
  • 9.2. Why Should We Learn NLP?
  • 9.3. Applications of NLP
  • 9.4. Test your Knowledge
  • 9.5. Recommended Courses

Chapter 10. Mathematics and Statistics for Data Science

  • 10.1. Essential Mathematics for Data Science
  • 10.2. Essential Statistics for Data Science
  • 10.3. Test your Knowledge
  • 10.4. Recommended Courses

Chapter 11. Data Analysis

  • 11.1. What is Data Analysis?
  • 11.2. Exploratory Data Analysis
  • 11.3. Statistical Data Analysis
  • 11.4. Descriptive Statistics vs. Inferential Statistics
  • 11.5. Data Storytelling
  • 11.6. Why do We Need Storytelling Skills?
  • 11.7. Test your Knowledge
  • 11.8. Recommended Courses

Chapter 12. Databases

  • 12.1. Databases
  • 12.2. Relational Databases
  • 12.3. SQL
  • 12.4. NoSQL Databases
  • 12.5. Test your Knowledge
  • 12.6. Recommended Courses

Chapter 13. Business Intelligence

  • 13.1. What is Business Intelligence?
  • 13.2. Why is BI important for Businesses?
  • 13.3. Test your Knowledge
  • 13.4. Recommended Courses

Chapter 14. BI Tools

  • 14.1. The Best BI Tools (Fundamentals of BI)
  • 14.2. What is Tableau?
  • 14.3. What is Microsoft Power BI?
  • 14.4. Test your Knowledge
  • 14.5. Recommended Courses

Chapter 15. Web Scraping

  • 15.1. What is Web Scrapping?
  • 15.2. Importance of Web Scraping and APIs
  • 15.3. Python Libraries for Web Scraping
  • 15.4. Test your Knowledge
  • 15.5. Recommended Courses

Chapter 16. Cloud Computing

  • 16.1. Cloud-Based Services
  • 16.2. Benefits of Cloud Computing
  • 16.3. Amazon Web Services (AWS)
  • 16.4. Microsoft Azure Cloud Services
  • 16.5. Test your Knowledge
  • 16.6. Recommended Courses

Chapter 17. Final Tasks

  • 17.1. Self-Study Essay

Chapter 18. Finishing the Course

  • 18.1. What We Have Learned
  • 18.2. Where To Go Next?
  • 18.3. Congrats! You did it!

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