Documenting my AI journey :D
For now (the next few months), I will mainly be following Alexey's ML ZoomCamp along with Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow.
Other than that, I will document my side projects as well as ML and DL topics that I learn about.
ZoomCamp Plan
Id | Module Session | Progress |
---|---|---|
01 | Introduction to Machine Learning | ✅ |
02 | Machine Learning for Regression | ✅ |
03 | Machine Learning for Classification | ✅ |
04 | Evaluation Metrics for Classification | ✅ |
05 | Deploying Machine Learning Models | ❌ |
5b | Bento ML | ❌ |
06 | Decision Trees and Ensemble Learning | ❌ |
07 | Midterm Project | ❌ |
07 | Midterm Project Evaluation | ❌ |
08 | Neural Networks and Deep Learning | ❌ |
09 | Serverless Deep Learning | ❌ |
10 | Kubernetes and TensorFlow-Serving | ❌ |
11 | Kubeflow and KFServing | ❌ |
12 | Capstone Project | ❌ |
12 | Capstone Project Evaluation | ❌ |
13 | The third Project | ❌ |
13 | The third Project Evaluation | ❌ |
14 | Article | ❌ |
- Day 1
- Day 2
- Car Price Prediction Project - 1
- Data preparation
- Exploratory Data Analysis (EDA)
- Setting up the validation framework
- Notebook
- Car Price Prediction Project - 1
- Day 3
- Car Price Prediction Project - 2
- Linear Regression
- Notebook
- Car Price Prediction Project - 2
- Day 4
- Car Price Prediction Project - 3 (final)
- Evaluating the model with RMSE
- Simple Feature Engineering
- Regularization
- Tuning the Model
- Notebook
- Week 2 ZoomCamp HW
- Car Price Prediction Project - 3 (final)
- Day 5
- Day 6
- Customer Churn Prediction Project - 1
- Data Preparation
- Setting Up the Validation Framework
- EDA
- Feature Importance: Churn Rate and Risk Ratio
- Feature Importance: Mutual Information
- Feature Importance: Correlation Coefficient
- Notebook
- Customer Churn Prediction Project - 1
- Day 7
- Customer Churn Prediction Project - 2 (final)
- One-hot Encoding
- Logistic Regression
- Notebook
- Week 3 ZoomCamp HW
- Customer Churn Prediction Project - 2 (final)
- Day 8
- Evaluation Metrics for Classification - 1
- Accuracy
- Confusion Matrix
- Precision
- Recall
- Evaluation Metrics for Classification - 1
- Day 9
- Evaluation Metrics for Classification - 2
- ROC Curve
- AUC-ROC Curve
- Cross Validation
- Accuracy
- Evaluation Metrics for Classification - 2
- Day 10
- Deployment