Introduction to Machine Learning Workshop
summary
Hands-on intro that codes in Python using Jupyter Notebook via Anaconda, reading a CSV into pandas, encoding categorical columns with label encoding, and training a decision tree classifier from scikit‑learn on a small transportation dataset (gender, car ownership, travel cost, income → transport). The notebook shows how to split inputs/outputs, fit the model, plot the tree (auto-generated tree has four layers versus the hand-drawn five), and run example predictions that match the dataset labels.
AI-written summary · about
chapters (27)
- 0:00Start
- 0:27Agenda
- 1:17What Is Machine Learning
- 2:16Traditional Programming Techniques
- 4:31Libraries
- 5:16Pandas
- 5:45Matplotlib
- 6:10Scikit-Learn
- 13:07What Packages Are Going To Be Included in the Installation
- 18:04Access a Jupiter Notebook
- 19:07Create a New Jupyter Notebook
- 25:12Google Collab
- 27:03Create a New Notebook
- 31:53Randint
- 33:27To Import a Package on Drupal Notebook
- 37:40Decision Trees
- 38:27Leaf Node
- 39:19Decision Tree
- 58:21Convert It to a Csv File
- 1:01:03Demo
- 1:02:40Split Our Data Frame into Attributes and Output
- 1:10:01Label Encoders
- 1:12:31Decision Tree Model
- 1:19:26Input Data
- 1:23:41Cnn
- 1:25:50Collaborative Filtering
- 1:30:07Nlp
related talks
47mData Science and Machine Learning with Python
27mDemystifying Machine Learning
42mMachine Learning Crash Course
01h 27mLearning scikit learn - An Introduction to Machine Learning in Python
04h 03mTutorial - Intro to Data Science
01h 02mA developers guide to Machine Learning
26mSkynet 101 How to Keep Your Machine Learning Code From Getting Away From You
46mIntroduction to Machine Learning