Journey of 100DaysOfData Challenge

Books and Resources
1. DeepLearning Specialization
2. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
3. Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
4. DeepLearning.AI TensorFlow Developer Professional Certificate

Day1 of 100DaysOfData!

• On my journey of Machine learning and Deep Learning, Today I continued reading Chapter End-to-End Machine learning Project. After framing the problem, getting the data, exploring the data, sampling data into train and test set, and creating a whole transformation pipeline to clean up and prepare data for Machine Learning algorithms automatically, today I learned to select and train a Machine Learning Model using Scikit Learn. I am working on a District house price prediction model. It is a multiple(more than one feature/attribute), univariate(only one value to predict) regression(based on all the attributes we have to predict a single value) supervised(labeled data) Machine Learning System.
• I learned to select and train a Machine Learning Model using Scikit Learn, learned about various performance metrics, and learned little about the Linear Regression model, Decision Tree Regressor, and Random Forest Regressor model. I also came across Cross-validation and using it easily from Scikit Learn.
• Hands On Machine Learning with Scikit Learn, Keras and TensorFlow | (Book)
• Day 1 of #100DaysOfData - Personal Blog | (Blog)

Day2 of 100DaysOfData!

• On my journey of Machine Learning and Deep Learning, even Today I continued reading Chapter End-to-End Machine learning Project. After framing the problem, getting the data, exploring the data, sampling data into train and test set, creating a whole transformation pipeline to clean up and prepare data for Machine Learning algorithms automatically, and selecting and training a model, today I learned to fine-tune my selected model.
• Here I have learned about Grid Search, Randomized Search, Ensemble methods, Analyze the best models, their error, and figuring out the importance of each attribute for making an accurate prediction, and finally to evaluate my system on the test set for production. I also took some time out to look into Artificial Neural Networks.