Intro to Data Science and Machine Learning

“Machine intelligence is the last invention that
humanity will need to make.”

Transcript

1. Introduction to Data Science & Machine Learning Olayinka Peter June 23rd, 2021
2. Get ready like you’re going to the office. “Machine intelligence is the last invention that humanity will need to make.” -Nick Bostrom
3. Artificial Intelligence Developing Intelligent Systems Data Science Driving Business & User Decisions Data Engineering Building Scalable Infrastructure Data Data Wrangling Machine Learning Software Engineering
4. Wetin be Data Science? Wetin be Machine Learning? Some DS & ML tools Different types of Machine Learning Opportunities in the field of Data Science & Machine Learning Stuff we'll cover
5. Wetin be Data Science? Some kinda stuff that aims to use scientific approaches to extract meaning and insights from data
6. Data Science applicable areas Descriptive Analysis. Fraud and Risk Detection. Healthcare. Internet Search. Targeted Advertising. Website Recommendations. Airline Route Planning.
7. Popular Data Science programming tools .Python (or R). .Numpy .Pandas .Matplotlib .Plotly, .and many others
8. Wetin be Machine Learning? Some weird science (and art) of programming computers so they can learn from data
9. Machine Learning applicable areas Image Recognition. Speech Recognition. Traffic Prediction. Product Recommendations. Self-driving Cars. Email Spam and Malware Filtering. Virtual Personal Assistant. Online Fraud Detection.
10. Popular Machine Learning programming tools .Python .Scikit-learn .TensorFlow .PyTorch .AWS ML .AutoML .and many others
11. Get ready like you’re going to the office. “A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.” -Dave Waters
12. Supervised learning Unsupervised learning Reinforcement learning Types of Machine Learning
13. Kinds of ML Problems 1 2 3 Classification Clustering Regression In regression problems (supervised learning), the predicted valued outputs are continuous. In classification problems (supervised learning), predicted valued output are discrete. In clustering (unsupervised learning), no answers (class labels) are given.
14. Data Scientist Data Analyst Data Wrangler Business- Intelligence Developer Data Architect Data Engineer Business Analyst Statistician Machine Learning Engineer Machine Learning Scientist AI Engineer MLOps Engineer ML Infrastructure Architect Career opportunities in Data Science and Machine Learning
15. We're done here. twitter: @olayinkapeter_