Day 1 of 66DaysOfData 📚

• Python: On my first day of #66DaysofData I revised the basics of python for a fresh start even though I had experience on python. Today I read about various datatypes in python such as booleans, lists, strings, tuples. I also learned about loops and list comprehensions and completed all the exercises on these topics. The Python tutorial along with exercises on kaggle helped me to broaden my knowledge. I hope you will also spend some time learning the topics below. Excited on my journey ahead!!
• Topics:
  • Python syntax, variable assignment and numbers
  • Functions
  • Booleans and Conditionals
  • Lists
  • Loops and List Comprehensions

Day2 of 66DaysOfData 📚

• Python: On my second day of #66DaysofData I learned about strings and dictionaries and their various methods. I also learned about how to work with external libraries in Python. I also did a fun exercise which introduced me to machine learning at the end of the session. I classified the data of people in the Titanic and identified those who had highest chance of survival through random forest model. I hope you will also spend some time learning the topics below. Excited on my journey ahead!!
• Topics:
  • strings and dictionaries
  • Working with External Libraries
  • Titanic Tutorial

Day3 of 66DaysOfData 📚

• Intro to Machine Learning: On my third day of #66DaysofData I learned about how models are used and how they work in machine learning. I learned about decision tree model and also learned how to improve it. I understoood different terminologies used in the model. Than, I learned how to explore data by using pandas library. I understood the concept of DataFrame and interpreted the data description. I hope you will also spend some time learning the topics below. Excited on my journey ahead!!
• Topics:
  • How Models Work
  • Basic Data Exploration

Day4 of 66DaysOfData 📚

• Intro to Machine Learning: On my fourth day of #66DaysofData I learned how to design a machine learning model. I also designed my first machine learning model using scikit learn library. I trained the model and evaluated its predictions. I hope you will also spend some time learning the topics below. Excited on my journey ahead!!
• Topics:
  • My First Machine Learning Model

Day5 of 66DaysOfData 📚

• Intro to Machine Learning: On my fifth day of #66DaysofData I learned to measure the model quality using Mean Absolute Error also known as MAE. I also understood the common problem of comparing predictions of training data to target values in training data and also understood the concept of splitting data into training and testing data. I hope you will also spend some time learning the topics below. Excited on my journey ahead!!
• Topics:
  • Model Validation

Day6 of 66DaysOfData 📚

• Intro to Machine Learning: On my sixth day of #66DaysofData I completed my course on Intro to Machine Learning by Kaggle. Today I learned about the concept of Underfitting and Overfitting in machine learning models and how to find a sweet spot between them using MAE. I also learned about a new machine learning model known as Random Forests which is better than decision tree model. Finally I completed an assignment by working with Random Forests model. I hope you will also spend some time learning the topics below. Excited on my journey ahead!!
• Topics:
  • Underfitting and Overfitting
  • Random Forests
  • Machine Learning Competitions
  • Intro to AutoML

Day7 of 66DaysOfData 📚

• Mathematics for Machine Learning: Linear Algebra: On my seventh day of #66DaysofData I learned a bit about Linear Algebra. I decided to build a good foundation on Mathematics as it is very important to understand the logic behind the machine learning models. Today I learned about associative natures of vectors, vector addition, subtraction, scalar multiplication and different techniques to solve linear equations. I also understood about SSR. Excited on my journey ahead!!
• Topics:
  • Introduction to Linear Algebra and Mathematics for Machine Learning

Day8 of 66DaysOfData 📚

• Mathematics for Machine Learning: Linear Algebra: On my eighth day of #66DaysofData I learned how to calculate the size of a vector. I also learned about the dot product of vectors, projection and cosine dot product. Finally I did some exercises on them. Excited on my journey ahead!!
• Topics:
  • Modulus & Inner Product

Day9 of 66DaysOfData 📚

• Linear Algebra On my ninth day of #66DaysofData I learned different concepts of vector like parametric representations of lines, reak coordinate spaces and more. Finally I did some exercises on them. Excited on my journey ahead!!
• Topics:
  • Vectors

Day10 of 66DaysOfData 📚

• Mathematics for Machine Learning: Linear Algebra: On my tenth day of #66DaysofData I learned about application of changing basis in neural networks. I also learned more about the linear transformations, matrix multiplications, Cramer's rule and much more through 3Blue1Brown's Essence of linear algebra playlist. Excited on my journey ahead!!
• Topics:
  • Essence of Linear ALgebra
  • Vectors

Day11 of 66DaysOfData 📚

• Mathematics for Machine Learning: Linear Algebra: On my eleventh day of #66DaysofData I learned about transformation of matrices in space, Gaussian Elimination, Inverse of a matrix, Determinants and much more. I also learned about echelon form of matrix through aitude. Excited on my journey ahead!!
• Topics:
  • Objects that operate on Vectors

Day12 of 66DaysOfData 📚

• Supervised Vs Unsupervised Learning On my twelfth day of #66DaysofData I learned about supervised and unsupervised learning. Excited on my journey ahead!!
• Topics:
  • Supervised Vs Unsupervised Learning

Day13 of 66DaysOfData 📚

• Kaggle Coding Challenge On my thirteenth day of #66DaysofData I did a problem based on one of the Kaggle competitions. I downloaded the dataset from kaggle implemented Random Forest Model to predict house prices. I also checked the MAE for the model. Excited on my journey ahead!!
• Topics:
  • Random Forest Model