Hands-on Session on Data Science for Beginners
summary
Step‑by‑step workshop shows a runnable path from raw files to a deployed model: notebooks on Google Colab with GPU, hands‑on demos of array and matrix math, PCA and dimensionality reduction, and clustering with K‑means and DBSCAN. Code uses NumPy/pandas/matplotlib/scikit‑learn (also mentions TensorFlow/PyTorch, Flask/Streamlit, Kaggle). Examples load ~9,332‑row CSVs, run linear and logistic regression, ridge/lasso, plot histograms/boxplots, and output metrics (RMSE, R², confusion matrix) — all executed live in the notebook.
AI-written summary · about
chapters (10)
- 0:00Data science intro
- 2:52What is artificial intelligence and machine learning
- 8:06ML lifecycle types
- 22:58Why Python
- 30:05Python data types and operators
- 49:35Python libraries
- 1:08:12Linear algebra NumPy
- 1:53:21Data analysis pandas
- 2:38:24ML with scikit-learn
- 4:01:25Applications and conclusion
related talks
47mData Science and Machine Learning with Python
01h 22mIntroduction to Big Data Processing using Spark and Python
35mWorking with data in Python for the Aspiring Data Scientists
02h 31mTech Tutorial: Data visualization in Python
47mA Practical(ish) Introduction to Data Science
01h 49mData Analytics for Beginners
03h 08mTutorial - Intro to Data Engineering
40mEveryone can Data! - A Practical Data Science Intro