Roadmap to Learn Artificial Intelligence for Everyone from the Beginning.
Opportunity Overview
AI, or artificial intelligence, is increasingly important in today’s world for a variety of reasons. Firstly, it can automate routine and repetitive tasks, which can free up human workers to focus on more complex and creative tasks. Secondly, AI can analyze large amounts of data much faster and more accurately than humans, improving efficiency and allowing organizations to make better decisions. Thirdly, AI can help organizations develop new products, services, and business models that were previously impossible or impractical. Fourthly, AI can personalize experiences for individual users, improving customer satisfaction and engagement. Additionally, AI has the potential to revolutionize healthcare by helping providers diagnose diseases earlier and more accurately, as well as develop more personalized treatment plans. Furthermore, AI can be used to monitor and detect potential safety hazards in real-time, making workplaces and public spaces safer for everyone. Overall, AI has the potential to improve many aspects of our lives and change the way we work, communicate, and interact with the world around us.
Roadmap step by step
General course
AI for Everyone Coursera 10 h https://www.coursera.org/learn/ai-for-everyone
Basic Mathematics for Machine Learning
|
Topic |
Resource |
Estimated Time Required |
Direct Link |
|
Linear Algebra |
Linear Algebra on Khan Academy |
4 weeks |
|
|
Calculus |
Calculus on Khan Academy |
4 weeks |
|
|
Probability and Statistics |
Probability and Statistics on Khan Academy |
4 weeks |
|
|
Linear Algebra for ML and data science |
Coursera |
21 hours |
https://www.coursera.org/learn/machine-learning-linear-algebra |
|
Mathematics for ML and Data science specialization |
Coursera |
3 months |
https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science |
Programming Language and Data Preprocessing
|
Topic |
Resource |
Estimated Time Required |
Direct Link |
|
Programming Language (Python) |
Python Crash Course |
4 weeks |
|
|
Introduction to data science in Python |
Coursera |
34 hours |
|
|
Data Preprocessing |
Data Wrangling with Pandas on DataCamp |
4 weeks |
https://www.coursera.org/projects/practical-data-wrangling-with-pandas |
Machine Learning Engineer
|
Topic |
Resource |
Estimated Time Required |
Direct Link |
|
Machine Learning |
Applied Machine Specialization on Coursera |
3 months |
https://www.coursera.org/specializations/machine-learning-introduction |
Data Scientist
|
Topic |
Resource |
Estimated Time Required |
Direct Link |
|
Data Science |
Data Science Methodology on Coursera |
4 weeks |
|
|
Data Visualization |
Data Visualization with Python on DataCamp |
4 weeks |
https://www.coursera.org/learn/python-for-data-visualization |
Data Engineer
|
Topic |
Resource |
Estimated Time Required |
Direct Link |
|
Big Data |
Big Data Essentials on Coursera |
4 weeks |
|
|
Database Management Systems |
Database Management Essentials on Coursera |
4 weeks |
Artificial Intelligence
|
Topic |
Resource |
Estimated Time Required |
Direct Link |
|
IBM |
Artificial Intelligence on IBM |
6 months |
https://www.coursera.org/professional-certificates/applied-artifical-intelligence-ibm-watson-ai |
Final Project
|
Topic |
Resource |
Estimated Time Required |
Direct Link |
|
Final Project |
Applied data science Capston |
17 hours |
https://www.coursera.org/learn/applied-data-science-capstone |
Another plan to be expert in data science
This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World.
Mentors
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