<aside> 🔑 Hi! this is Sanjeev. I am starting out my ML journey. I've shared these notes to help you out in your journey. This is always a WIP. If you have queries reach out to me on Twitter. Thanks and have a nice day.

</aside>

DATA SCIENCE PROCESS

**1. Look at the big picture.

2. Get the data.
3. Discover and visualize the data to gain insights.
4. Prepare the data for Machine Learning algorithms.
5. Select a model and train it.
6. Fine-tune your model.
7. Present your solution.
8. Launch, monitor, and maintain your system.**

Data Science Process

• Linear regression
• Training and loss
• Iterative Approach
• Gradient descent
• NumPy and Pandas basics
• TensorFlow
• Hyperparameter Tuning
• Generalization: Perils of overfitting
• Bias - Variance Trade off
• Training and testing data
• Validation Set
• Feature Engineering
• Feature Crosses
• Regularization
• Lambda