# 100-Days-Of-ML-Code
100 Days of Machine Learning Coding as proposed by Siraj Raval (opens new window)
Get the datasets from here (opens new window)
# Data PreProcessing | Day 1
Check out the code from here (opens new window).
# Simple Linear Regression | Day 2
Check out the code from here (opens new window).
# Multiple Linear Regression | Day 3
Check out the code from here (opens new window).
# Logistic Regression | Day 4
# Logistic Regression | Day 5
Moving forward into #100DaysOfMLCode today I dived into the deeper depth of what Logistic Regression actually is and what is the math involved behind it. Learned how cost function is calculated and then how to apply gradient descent algorithm to cost function to minimize the error in prediction.
Due to less time I will now be posting an infographic on alternate days.
Also if someone wants to help me out in documentaion of code and already has some experince in the field and knows Markdown for github please contact me on LinkedIn 😃 .
# Implementing Logistic Regression | Day 6
Check out the Code here (opens new window)
# K Nearest Neighbours | Day 7
# Math Behind Logistic Regression | Day 8
#100DaysOfMLCode To clear my insights on logistic regression I was searching on the internet for some resource or article and I came across this article (https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc) by Saishruthi Swaminathan.
It gives a detailed description of Logistic Regression. Do check it out.
# Support Vector Machines | Day 9
Got an intution on what SVM is and how it is used to solve Classification problem.
# SVM and KNN | Day 10
Learned more about how SVM works and implementing the K-NN algorithm.
# Implementation of K-NN | Day 11
Implemented the K-NN algorithm for classification. #100DaysOfMLCode Support Vector Machine Infographic is halfway complete. Will update it tomorrow.
# Support Vector Machines | Day 12
# Naive Bayes Classifier | Day 13
Continuing with #100DaysOfMLCode today I went through the Naive Bayes classifier. I am also implementing the SVM in python using scikit-learn. Will update the code soon.
# Implementation of SVM | Day 14
Today I implemented SVM on linearly related data. Used Scikit-Learn library. In Scikit-Learn we have SVC classifier which we use to achieve this task. Will be using kernel-trick on next implementation. Check the code here (opens new window).
# Naive Bayes Classifier and Black Box Machine Learning | Day 15
Learned about different types of naive bayes classifiers. Also started the lectures by Bloomberg (opens new window). First one in the playlist was Black Box Machine Learning. It gives the whole overview about prediction functions, feature extraction, learning algorithms, performance evaluation, cross-validation, sample bias, nonstationarity, overfitting, and hyperparameter tuning.
# Implemented SVM using Kernel Trick | Day 16
Using Scikit-Learn library implemented SVM algorithm along with kernel function which maps our data points into higher dimension to find optimal hyperplane.
# Started Deep learning Specialization on Coursera | Day 17
Completed the whole Week 1 and Week 2 on a single day. Learned Logistic regression as Neural Network.
# Deep learning Specialization on Coursera | Day 18
Completed the Course 1 of the deep learning specialization. Implemented a neural net in python.
# The Learning Problem , Professor Yaser Abu-Mostafa | Day 19
Started Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. It was basically an introduction to the upcoming lectures. He also explained Perceptron Algorithm.
# Started Deep learning Specialization Course 2 | Day 20
Completed the Week 1 of Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization.
# Web Scraping | Day 21
Watched some tutorials on how to do web scraping using Beautiful Soup in order to collect data for building a model.
# Is Learning Feasible? | Day 22
Lecture 2 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. Learned about Hoeffding Inequality.
# Decision Trees | Day 23
# Introduction To Statistical Learning Theory | Day 24
Lec 3 of Bloomberg ML course introduced some of the core concepts like input space, action space, outcome space, prediction functions, loss functions, and hypothesis spaces.
# Implementing Decision Trees | Day 25
Check the code here. (opens new window)
# Jumped To Brush up Linear Algebra | Day 26
Found an amazing channel (opens new window) on youtube 3Blue1Brown. It has a playlist called Essence of Linear Algebra. Started off by completing 4 videos which gave a complete overview of Vectors, Linear Combinations, Spans, Basis Vectors, Linear Transformations and Matrix Multiplication.
Link to the playlist here. (opens new window)
# Jumped To Brush up Linear Algebra | Day 27
Continuing with the playlist completed next 4 videos discussing topics 3D Transformations, Determinants, Inverse Matrix, Column Space, Null Space and Non-Square Matrices.
Link to the playlist here. (opens new window)
# Jumped To Brush up Linear Algebra | Day 28
In the playlist of 3Blue1Brown completed another 3 videos from the essence of linear algebra. Topics covered were Dot Product and Cross Product.
Link to the playlist here. (opens new window)
# Jumped To Brush up Linear Algebra | Day 29
Completed the whole playlist today, videos 12-14. Really an amazing playlist to refresh the concepts of Linear Algebra. Topics covered were the change of basis, Eigenvectors and Eigenvalues, and Abstract Vector Spaces.
Link to the playlist here. (opens new window)
# Essence of calculus | Day 30
Completing the playlist - Essence of Linear Algebra by 3blue1brown a suggestion popped up by youtube regarding a series of videos again by the same channel 3Blue1Brown. Being already impressed by the previous series on Linear algebra I dived straight into it. Completed about 5 videos on topics such as Derivatives, Chain Rule, Product Rule, and derivative of exponential.
Link to the playlist here. (opens new window)
# Essence of calculus | Day 31
Watched 2 Videos on topic Implicit Diffrentiation and Limits from the playlist Essence of Calculus.
Link to the playlist here. (opens new window)
# Essence of calculus | Day 32
Watched the remaining 4 videos covering topics Like Integration and Higher order derivatives.
Link to the playlist here. (opens new window)
# Random Forests | Day 33
# Implementing Random Forests | Day 34
Check the code here. (opens new window)
# But what is a Neural Network? | Deep learning, chapter 1 | Day 35
An Amazing Video on neural networks by 3Blue1Brown youtube channel. This video gives a good understanding of Neural Networks and uses Handwritten digit dataset to explain the concept. Link To the video. (opens new window)
# Gradient descent, how neural networks learn | Deep learning, chapter 2 | Day 36
Part two of neural networks by 3Blue1Brown youtube channel. This video explains the concepts of Gradient Descent in an interesting way. 169 must watch and highly recommended. Link To the video. (opens new window)
# What is backpropagation really doing? | Deep learning, chapter 3 | Day 37
Part three of neural networks by 3Blue1Brown youtube channel. This video mostly discusses the partial derivatives and backpropagation. Link To the video. (opens new window)
# Backpropagation calculus | Deep learning, chapter 4 | Day 38
Part four of neural networks by 3Blue1Brown youtube channel. The goal here is to represent, in somewhat more formal terms, the intuition for how backpropagation works and the video moslty discusses the partial derivatives and backpropagation. Link To the video. (opens new window)
# Deep Learning with Python, TensorFlow, and Keras tutorial | Day 39
Link To the video. (opens new window)
# Loading in your own data - Deep Learning basics with Python, TensorFlow and Keras p.2 | Day 40
Link To the video. (opens new window)
# Convolutional Neural Networks - Deep Learning basics with Python, TensorFlow and Keras p.3 | Day 41
Link To the video. (opens new window)
# Analyzing Models with TensorBoard - Deep Learning with Python, TensorFlow and Keras p.4 | Day 42
Link To the video. (opens new window)
# K Means Clustering | Day 43
Moved to Unsupervised Learning and studied about Clustering. Working on my website check it out avikjain.me (opens new window) Also found a wonderful animation that can help to easily understand K - Means Clustering Link (opens new window)
# K Means Clustering Implementation | Day 44
Implemented K Means Clustering. Check the code here.
# Digging Deeper | NUMPY | Day 45
Got a new book "Python Data Science HandBook" by JK VanderPlas Check the Jupyter notebooks here. (opens new window)
Started with chapter 2 : Introduction to Numpy. Covered topics like Data Types, Numpy arrays and Computations on Numpy arrays.
Check the code -
Introduction to NumPy (opens new window)
Understanding Data Types in Python (opens new window)
The Basics of NumPy Arrays (opens new window)
Computation on NumPy Arrays: Universal Functions (opens new window)
# Digging Deeper | NUMPY | Day 46
Chapter 2 : Aggregations, Comparisions and Broadcasting
Link to Notebook:
Aggregations: Min, Max, and Everything In Between (opens new window)
Computation on Arrays: Broadcasting (opens new window)
Comparisons, Masks, and Boolean Logic (opens new window)
# Digging Deeper | NUMPY | Day 47
Chapter 2 : Fancy Indexing, sorting arrays, Struchered Data
Link to Notebook:
Fancy Indexing (opens new window)
Sorting Arrays (opens new window)
Structured Data: NumPy's Structured Arrays (opens new window)
# Digging Deeper | PANDAS | Day 48
Chapter 3 : Data Manipulation with Pandas
Covered Various topics like Pandas Objects, Data Indexing and Selection, Operating on Data, Handling Missing Data, Hierarchical Indexing, ConCat and Append.
Link To the Notebooks:
Data Manipulation with Pandas (opens new window)
Introducing Pandas Objects (opens new window)
Data Indexing and Selection (opens new window)
Operating on Data in Pandas (opens new window)
Handling Missing Data (opens new window)
Hierarchical Indexing (opens new window)
Combining Datasets: Concat and Append (opens new window)
# Digging Deeper | PANDAS | Day 49
Chapter 3: Completed following topics- Merge and Join, Aggregation and grouping and Pivot Tables.
Combining Datasets: Merge and Join (opens new window)
Aggregation and Grouping (opens new window)
Pivot Tables (opens new window)
# Digging Deeper | PANDAS | Day 50
Chapter 3: Vectorized Strings Operations, Working with Time Series
Links to Notebooks:
Vectorized String Operations (opens new window)
Working with Time Series (opens new window)
High-Performance Pandas: eval() and query() (opens new window)
# Digging Deeper | MATPLOTLIB | Day 51
Chapter 4: Visualization with Matplotlib
Learned about Simple Line Plots, Simple Scatter Plotsand Density and Contour Plots.
Links to Notebooks:
Visualization with Matplotlib (opens new window)
Simple Line Plots (opens new window)
Simple Scatter Plots (opens new window)
Visualizing Errors (opens new window)
Density and Contour Plots (opens new window)
# Digging Deeper | MATPLOTLIB | Day 52
Chapter 4: Visualization with Matplotlib
Learned about Histograms, How to customize plot legends, colorbars, and buliding Multiple Subplots.
Links to Notebooks:
Histograms, Binnings, and Density (opens new window)
Customizing Plot Legends (opens new window)
Customizing Colorbars (opens new window)
Multiple Subplots (opens new window)
Text and Annotation (opens new window)
# Digging Deeper | MATPLOTLIB | Day 53
Chapter 4: Covered Three Dimensional Plotting in Mathplotlib.
Links to Notebooks:
Three-Dimensional Plotting in Matplotlib (opens new window)
# Hierarchical Clustering | Day 54
Studied about Hierarchical Clustering. Check out this amazing Visualization. (opens new window)
