BASIC
- Introduction to computer.
- Networking.
- Topologies.
- MS. word.
- MS. powerpoint.
- MS. excel.
PYTHON
- Introduction to Python.
- Variables & Data types.
- Conditional Formating & Loops.
- Functions & Modules.
- Numpy, Pandas, Matplotlib, Seaborn.
Basic Data Visualization
- Histograms.
- Scatter Plots.
- Bar Charts.
- Introduction To Seaborn, Plotly.
Model Evaluation
- Accuracy.
- Precision.
- Recall.
- F1 Score.
- Confusion Matrix.
Machine Learning Basics
- Supervised Vs Unsupervised Learning.
- Linear Regression & Logistic Regression.
- Decision Trees.
- K-Nearest Neighbors & K-Means Clustering .
Advanced Machine Learning
- Random Forest.
- Gradient Boosting .
- Hyperparameter Tuning.
- Cross-Validation.
- Gridsearchcv.
- Lightgbm.
