https://github.com/devparihar5/30-day-machine-learning-challange
Machine Learning 30 Day Challange
https://github.com/devparihar5/30-day-machine-learning-challange
algorithms deep-learning machine-learning neural-networks nlp-machine-learning projects roadmap
Last synced: 3 months ago
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Machine Learning 30 Day Challange
- Host: GitHub
- URL: https://github.com/devparihar5/30-day-machine-learning-challange
- Owner: Devparihar5
- Created: 2023-01-03T09:02:29.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-13T17:24:52.000Z (over 3 years ago)
- Last Synced: 2025-05-08T02:38:43.965Z (about 1 year ago)
- Topics: algorithms, deep-learning, machine-learning, neural-networks, nlp-machine-learning, projects, roadmap
- Language: Jupyter Notebook
- Homepage:
- Size: 35.9 MB
- Stars: 7
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Machine Learning Challange: 30 days roadmap
> This is a tentative roadmap for our 30 days machine learning challange. I will add more information along the way.
## Day One:
Core concepts of Machine Learning
Machine Learning Process
## Day Two:
### Project:
Classification Walkthrough: Titanic Dataset
## Day Three:
### Project:
Regression Walkthrough: California Housing Price Dataset
## Day Four:
* Working with Missing Data
* Examining Missing Data
* Dropping Missing Data
* Imputing Data
* Adding Indicator Columns
## Day Five:
Working with Cleaning Data
* Column Names
* Replacing Missing Values
## Day Six:
* Data Exploration
* Data Size
* Summary Stats
* Histogram
* Scatter Plot
* Joint Plot
* Pair Grid
* Box and Violin Plots
* Comparing Two Ordinal Values
* Correlation
* RadViz
* Parallel Coordinates
## Day Seven:
* Preprocessing Data
* Standardize
* Scale to Range
* Dummy Variables
* Label Encoder
* Frequency Encoding
* Pulling Categories from Strings
* Other Categorical Encoding
* Date Feature Engineering
* Add col _na Feature
* Manual Feature Engineering
## Day Eight:
* Feature Selection
* Collinear Columns
* Lasso Regression
* Recursive Feature Elimination
* Mutual Information
* Principal Component Analysis
* Feature Importance
## Day Nine:
* Dealing with Imbalance Classes
* Use a Different Metric
* Tree-based Algorithms and Ensembles
* Penalize Models
* Upsampling Minority
* Generate Minority Data
* Downsampling Majority
* Upsampling Then Downsampling
## Day Ten:
* Classification Algorithms
## Day Eleven:
* Model Selection
## Day Twelve:
* Metrics and Classification Evaluation
* Confusion Matrix
* Metrics
* Accuracy
* Recall
* Precision
* F1
* Classification Report
* RoC
* Precision-Recall Curve
* Cumulative Gains Plot
* Lift Curve
* Class Balance
* Class Prediction Error
* Discrimination Threshold
## Day Thirteen:
* Explaining Classification Model
## Day Fourteen:
* Regression Algorithms
## Day Fifteen:
* Metrics and Regression Evaluation
## Day Sixteen:
* Explaining Regression Model
## Day Seventeen:
* Dimensionality Reduction
## Day Eighteen:
* Clustering
## Day Nineteen:
* Implementing Pipeline
## Day Twenty:
* Neural networks
* Artificial neural networks (ANN)
## Day Twenty-one:
### Project:
* ANN walkthrough: Predicting Stock Prices
## Day Twenty-two:
* Natural Language Processing (NLP)
## Day Twenty-three:
### Project:
* NLP walkthrough: Mining Newsgroups Dataset
## Day Twenty-four:
* Deep Learning Basics
## Day Twenty-five:
* Problems and Solutions
## Day Twenty-six:
* Machine Learning best practices
## Day Twenty-seven:
### Project:
* Building a Movie Recommendation Engine
## Day Twenty-eight:
### Project:
* Recognizing Faces
## Day Twenty-nine:
### Project:
* Predicting Online Ad Click-Through: Tree-based Algorithm
## Day Thirty:
### Project:
* NewsGroups Dataset with Clustering and Topic Modeling
## Reference : https://www.learnmldaily.com/