[PROGRESS] Grace-Hephzibah

Grace-Hephzibah commented 2 years ago

Participant Details

Name/Nickname: Grace Pronouns: she/her Domain: Machine Learning

I am AI and ML Enthusiast and I hope to learn as much as possible on this track

Milestones

[x] Code for 30 days
[x] Explore the mathematical part related to ML
[x] Become more proficient using Streamlit
[x] Build at least 1 data model every week - (around 5 data models this month-end)

Checklist

[x] I've read the Code of Conduct, Rules and Guidelines
[x] I've named this Issue as indicated
[x] I've been assigned a mentor
[x] Acknowledgement of this post
Github Repo Link:

Link: 30-Days-Of-ML

Grace-Hephzibah commented 2 years ago

Day 1

What I Learned Today

[x] Object-Oriented Programming in Python
[x] Setting up and using Anaconda Environment
[x] Basic Markdown
[x] Week 1 Project

Week 1 works mentioned on the Road Map Done

What I'll Learn Tomorrow

Explore regression and related mathematical concepts together with it

Grace-Hephzibah commented 2 years ago

Day 2

What I Learned Today

[x] Linear Regression using OLS Method
[x] Multiple Regression using OLS Method
[x] Understanding the usage of scatter plot and line plot
[x] Understanding the usage and elimination requirement of unnecessary data

What I'll Learn Tomorrow

Same which I learnt today but using the Sklearn package.

Grace-Hephzibah commented 2 years ago

Day 3

What I Learned Today

Linear Regression using Sklearn
Multiple Regression using Sklearn
Underfitting and Overfitting Model
Train - Test - Split usage

What I'll Learn Tomorrow

Logistic Regression

Grace-Hephzibah commented 2 years ago

Day 4

What I Learned Today

Logistic Regression uses and its non-linearity
Binary Prediction and its maths
Deep analysis on MLE, Log-Likelihood, LL-Null, LLR, and many more
Simple Logistic Regression
Little complicated Logistic Regression
Confusion matrix

What I'll Learn Tomorrow

Streamlit and usage of its various components

Grace-Hephzibah commented 2 years ago

Day 5

What I Learned Today

Covered Streamlit
Various text formats and it's rendering style
Various types of displaying data like a dictionary, data frame, etc
Displaying Plots and Media
Basic Widgets
Bar, Progress and Status
Layouts and Color Schemes
Caching in Streamlit

Resource: Streamlit Playlist

What I'll Learn Tomorrow

K-clustering

Grace-Hephzibah commented 2 years ago

Day 6

What I Learned Today

General overview of k clustering
Theoretical examples to understand the topic

What I'll Learn Tomorrow

Code implementation of k-clustering

Akshu-on-github commented 2 years ago

Glad to see the progress @Grace-Hephzibah! Thanks for sharing the YT resource - Could you raise an 'Add Resource' Issue? It would be an extra green square for you! 🟩 If possible, we'd love to see the code that you've worked on - but no pressure 😄

Grace-Hephzibah commented 2 years ago

The github repo is linked in the first comment of this issue Link: https://github.com/Grace-Hephzibah/30-Days-Of-ML

I will raise a request to add that as a resource. 😊✨

I am also sharing my progress on twitter just to stay accountable. I really want to push myself. I am tagging Codezilla.

Thanks for the reply👍🏼

Grace-Hephzibah commented 2 years ago

Day 7

What I Learned Today

Explored more K Clustering
Explored the elbow method
Understood the idea behind gathering information for every cluster

What I'll Learn Tomorrow

Code implementation of K-Clustering 😅

Grace-Hephzibah commented 2 years ago

Day 8

What I Learned Today

Implemented K-Clustering
Implemented the Elbow Method

What I'll Learn Tomorrow

K-Clustering on the Iris Dataset

Grace-Hephzibah commented 2 years ago

Day 9

What I Learned Today

K-Clustering revision
Introduction to Neural Network
Basics and methods to train a model

What I'll Learn Tomorrow

Continuation of Neural Network

Grace-Hephzibah commented 2 years ago

Day 10

What I Learned Today

Started working with streamlit with little complexity

What I'll Learn Tomorrow

Continuation of Neural Networks

Grace-Hephzibah commented 2 years ago

Day 11

What I Learned Today

Joining in elements for tougher and complex views

What I'll Learn Tomorrow

Continuation of Neural Networks

Grace-Hephzibah commented 2 years ago

Day 12

What I Learned Today

Full revision from first to last.
Made notes to some parts that I missed

What I'll Learn Tomorrow

Continue Neural Networks

Grace-Hephzibah commented 2 years ago

Day 13

What I Learned Today

Data processing revision + extra insights
Linear model under deep learning (just started)

What I'll Learn Tomorrow

Continue with the linear model

Grace-Hephzibah commented 2 years ago

Day 14

What I Learned Today

A theoretical approach to various types in Linear Model in Neural Networks

What I'll Learn Tomorrow

More on Linear Model

Grace-Hephzibah commented 2 years ago

Day 15

What I Learned Today

Graphical representation of simple neural networks
Objective function.

What I'll Learn Tomorrow

More on the theoretical part on simple neural networks.

Grace-Hephzibah commented 2 years ago

Day 16

What I Learned Today

Explored L2-norm loss and cross-entropy under the objective function
Explored Parameter gradient descent under the optimization algorithm

What I'll Learn Tomorrow

Revision on all the topics so far covered on neural networks
Start coding on this 😅

Grace-Hephzibah commented 2 years ago

Day 17

What I Learned Today

Full revision of neural networks

What I'll Learn Tomorrow

Coding!

Grace-Hephzibah commented 2 years ago

Day 18

What I Learned Today

Coding lectures on the linear model

What I'll Learn Tomorrow

Continuing the same tomorrow

Grace-Hephzibah commented 2 years ago

Day 19

What I Learned Today

Completed the coding lectures today

What I'll Learn Tomorrow

Practice sessions on the same

Grace-Hephzibah commented 2 years ago

Day 20

What I Learned Today

Practiced linear model

What I'll Learn Tomorrow

Starting with TensorFlow

Grace-Hephzibah commented 2 years ago

Day 21

What I Learned Today

Installed TensorFlow
Set up a virtual environment

What I'll Learn Tomorrow

Explore TensorFlow library (overview)

Grace-Hephzibah commented 2 years ago

Day 22

What I Learned Today

TensorFlow Outline and Comparison with Other Libraries
TensorFlow 1 vs TensorFlow 2
Types of File Formats Supporting TensorFlow
Outlining the Model with TensorFlow 2

What I'll Learn Tomorrow

Coding Practice on the same

Grace-Hephzibah commented 2 years ago

Day 23

What I Learned Today

Customizing models in TensorFlow

What I'll Learn Tomorrow

Coding 😅 (the same)

Grace-Hephzibah commented 2 years ago

Day 24

What I Learned Today

Coded out the same example which I used in NumPy for the linear model in neural networks

What I'll Learn Tomorrow

More on Deep Learning

Grace-Hephzibah commented 2 years ago

Day 25

What I Learned Today

Explored

Layer
Deep Net
The purpose of non-linearity
Activation functions
Softmax activation function
Back Propagation

What I'll Learn Tomorrow

Continuation on theory

Grace-Hephzibah commented 2 years ago

Day 26

What I Learned Today

Explored

Underfitting
Overfitting
Validation

What I'll Learn Tomorrow

More on validation

Grace-Hephzibah commented 2 years ago

Day 27

What I Learned Today

Explored

N-Fold cross-validation
Early stopping
Initialization
Xavier Initialization

What I'll Learn Tomorrow

More on gradient descent

Grace-Hephzibah commented 2 years ago

Day 28

What I Learned Today

Explored

Stochastic Gradient Descent
Drawbacks with Gradient Descent
Momentum
Learning Rate Schedules
Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
Adam (Adaptive Moment Estimation)

What I'll Learn Tomorrow

Data Preprocessing

Grace-Hephzibah commented 2 years ago

Day 29

What I Learned Today

Data Preprocessing

What I'll Learn Tomorrow

More on data preprocessing

Grace-Hephzibah commented 2 years ago

Day 30

What I Learned Today

Explored

Data preprocessing
Standardization
Preprocessing categorical data
Binary encoding
One-hot encoding

What I'll Learn Tomorrow

Challenge completed ✨

CodezillaClub / 30-Days-of-Code