Python for Machine Learning
Python for Data Visualization
| Libraries |
|---|
| Matplotlib |
| Seaborn |
| ggplot |
| GraphX |
| Plotly |
| Functions |
|---|
| Exploratory data analysis |
| Data storytelling |
| Decision-support dashboard design |
| Public education(news, media, and data blogging) |
Python for Machine Learning
| Libraries |
|---|
| scikit-learn |
| Tensor Flow |
| PyTorch |
| Functions (By Use Case) |
|---|
| Regression |
| Clustering |
| Dimension reduction |
| Association rules |
| Deep learning |
| Instance-based |
| Decision trees |
| Bayesian |
| Ensemble |
| Regularization |
Python for Data Engineering
Functions
- Learn to build simple MapReduce jobs (sans Java)
- Write Spark jobs(sans Scala)
- Programming IoT device(Raspberry Pi)
- Building ETL processes(Airflow)
Types of Machine Learning Methods
| Supervised Learning | Making predictions straight from labeled data |
|---|---|
| Unsupervised Learning | Making predictions straight from unlabeled data |
| Semi-Supervised Learning | Uses both labeled and unlabeled data to make a set of predictions |
Popular Ways to Group ML Algorithms
| By Learning | By Function | By Use Case |
|---|---|---|
| Supervised | Regression | Fraud detection |
| Unsupervised | Clustering | Recommendation engines |
| Semi-supervised | Dimension reduction | Price forecasting |
| Association rules | Inventory demand forecasting | |
| Deep learning | Water consumption forecasting | |
| Instance-based | Infrastructure demand forecasting | |
| Decision trees | And so on | |
| Bayesian | ||
| Ensemble | ||
| Regularization |