Collections of Data Science & ML projects and dojo where I practice Data Science, Machine Learning, Deep Learning and Data Visualization related skills, theories, probability, statistics, etc.
- NumPy - package for scientific computing with Python
- Pandas - fast, powerful, flexible and easy to use open source data analysis and manipulation tool
- Pandas Profiling - generate reports from dataframe
- Geo Pandas - support for geographic data to pandas objects.
- Scikit-learn - Simple and efficient tools for predictive data analysis
- TensorFlow - An end-to-end open source machine learning platform
- Keras - Deep Learning framework
- NLTK - Natural Language Toolkit
- dlib - A toolkit for making real world machine learning and data analysis applications in C++
- Face Recognition - The world's simplest facial recognition api for Python and the command line
- Matplotlib - a comprehensive library for creating static, animated, and interactive visualizations in Python
- Seaborn - statistical data visualization
- Bokeh - interactive visualization library for modern web browsers
- Plotly - The front-end for ML and data science models
- Cufflinks - Productivity Tools for Plotly + Pandas
- Streamlit - The fastest way to build and share data apps
- Flask - a micro web framework written in Python
- Apache Spark - a unified analytics engine for large-scale data processing.
- Spark with pyspark - PySpark is the collaboration of Apache Spark and Python
- Databricks - Unified Data Analytics Platform - One cloud platform for massive scale data engineering and collaborative data science.
- Jupyter Notebook - Notebook system for data analysis
- Google Colab - Great Notebook system by google, which give free access to GPUs
- Kaggle - Source of Dataset collections
- Plotly Chart Studio - The fastest way to publish & embed interactive charts online
- The project is to build a machine learning model to predict whether the tumor is benign or malignant basedon several observations/features.
- using data from Breast Cancer Wisconsin (Diagnostic) Data Set - UCI
Data Analysis and Visualization Capstone project from Machine Learning and Datascience Masterclass Course.
- This is the data behind the story Be Suspicious Of Online Movie Ratings, Especially Fandango’s
- using data from 538
- If you are planning on going out to see a movie, how well can you trust online reviews and ratings? Especially if the same company showing the rating also makes money by selling movie tickets.
- Do they have a bias towards rating movies higher than they should be rated?
- etc..
- This project is to build a machine learning model to predict whether or not a customer will Churn or not.
- Includes cohort analysis based on Telco subsriber's contract type, etc.
- Ames Housing Data Project - Linear Regression
- Heart Disease Detection Project - Logistic Regression
- Sona Data - Detecting Rock or Mine Project - KNN
- Wine Fraud Detection Project - SVM
- Mushroom Edible or Poisonous Prediction Project - with AdaBoost
- Mushroom Edible or Poisonous Prediction Project - with Gradient Boosting
- Ecommerce Project - Linear Regression
- Advertisement Project - Logistic Regression
- Anonymized Data Project - KNN
- Supervised Learning Capstone Project - Cohort Analysis & Customer Churn Predictions
- NLP - Flight Tweets Sentiment Analysis - Classification
- NLP - Moview Reivew Sentiment Analysis - Classification
- Color Quantization - KMeans
- CIA Country Analysis and Clustering - KMeans
- Cars Model - Hierarchical Clustering
- Wholesale Customers - DBSCAN Clustering
- Breast Cancer - PCA Manual Implementation
- Breast Cancer - PCA with sklearn
- Iris Flower Predictions App on Flask
- ANN - Loan Default Prediction Prediction Project
- ANN - Predict House Price for House Sales in King County, USA Project
- ANN - Breast Cancer Wisconsin (Diagnostic) Project
- CNN - Convolutional Neural Networks for Image Classification - MNIST data Project
- CNN - Convolutional Neural Networks for Image Classification - CIFAR 10 data Project
- CNN - Convolutional Neural Networks for Image Classification - Real Image - Malaria Detection Project
- CNN - Convolutional Neural Networks for Image Classification - Fashion MNIST Data Project
- RNN - Forzen Dessert Sales Forecasting with LSTM
- NLP - Yelp Reviews Classification - Natural Language Processing Project
- Average Eating Habits of UK Countries - Autoencoders
Milestone project from Complete Machine Learning and Data Science - Zero to Mastery course.
- The project is to build a machine learning model capable of predicting whether or not someone has a Heart Disease based on their medical attributes.
- using data from Heart Disease Data Set of UCI - kaggle version
Milestone project from Complete Machine Learning and Data Science - Zero to Mastery course.
- The project is to build a machine learning model to predict the sale price of bulldozers based on the past prices.
- using data from Blue Book for Bulldozers - kaggle version
Project from Complete Machine Learning and Data Science - Zero to Mastery course.
- The project is to build deep learning model with Tensorflow to predict the dog breeds.
- using data from Dog Breed Identification - kaggle version
Data Analysis and Visualization Capstone project from Data Science and Machine Learning Bootcamp Course.
- analyzing 911 calls data from kaggle
- top 5 zips code for 911 calls
- top 5 townships for 911 calls
- most common Reason for a 911
- different types of visualizations based on the findings
- etc..
- Machine learning app using streamlit, for building a regression model using the Random Forest algorithm.
- Data Visualization with Python - Project: Data analysis and Data Visualization using Pandas, Matplotlib for Countries's GDP, Life Expectancy comparison across continents, GDP per Capita Relative Growth, Population Reative Growth comparison etc.
- Fuel Economy Case Study - Project: Analyzing Fuel Economy Data provied by EPA for distributions of greenhouse gas score, combined mpg in 2008 and 2018, correlation between displacement and combined mpg ,greenhouse gas score and combined mpg. Are more unique models using alternative fuels in 2018 compared to 2008? By how much? How much have vehicle classes improved in fuel economy (increased in mpg)? What are the characteristics of SmartWay vehicles? Have they changed over time? (mpg, greenhouse gas) What features are associated with better fuel economy (mpg)? What is the top vehicle which improved the most in terms of combined mpg from 2008 to 2018?
- Wine Quality Case Study - Project: Analyzing wine data for the following points for wine businesses to model better wine. Is a certain type of wine (red or white) associated with higher quality? What level of acidity (pH value) receives the highest average rating? Do wines with higher alcoholic content receive better ratings? Do sweeter wines (more residual sugar) receive better ratings? White Vs Red Wine Proportions by Color & Quality
- TV, Halftime Shows, and the Big Game - Project: Analyzing Superbowls data and answering questions like - What are the most extreme game outcomes? How does the game affect television viewership? How have viewership, TV ratings, and ad cost evolved over time? Who are the most prolific musicians in terms of halftime show performances?
- Weather Trend - Project: Analyzing Global weather trends, Singapore weather trends, Comparing Global vs Singapore 10 years Moving Average trends
- Real-time Insights from Social Media Data - Project: Analyzing Twitter data and answering questions like: What are gobal trend and local trends?, finding the common trends
- frequency analysis on tweets and hashtags, etc.
- Statistics From Stock Data: Analyzing google, apple and amzon stock prices and checking the rolling mean.
- Android Play Store App Data Analysis - Project: Analyzing andriod play store data and answering questions like - How many apps are paid? How much money are they making? When were these apps released?
- Music Recommender - ML Project: A simple machine learning project for recommending the music genre based on age, gender.
- Smarty Brain Image Prediction - ML Project: ML project for analyzing images and make predictions.
- NumPy Crash Course
- Pandas Crash Course
- Visualization Crash Course
- Basic Artifical Neural Networks - ANNs
- Convolutional Neural Networks - CNNs
- Convolutional Neural Networks for Image Classification - MNIST data
- Convolutional Neural Networks for Image Classification - CIFAR 10 data
- Convolutional Neural Networks for Image Classification - Real Image - Malaria Detection Project
- Convolutional Neural Networks for Image Classification - Fashion MNIST Data Project
- Recurrent Neural Networks - RNNs
- Natural Language Processing - NLP
- Auto Encoders
- Generative Adverserial Networks - GANs
- Deployment
- new track 2021 Python for Machine Learning & Data Science Masterclass
- Python Crash Course
- NumPy
- Pandas
- Matplotlib
- Seaborn Data Visualizations
- Data Analysis and Data Visualization Capstone Project
- Linear Regression Models
- Feature Engineering and Data Preparation
- Cross Validation, Grid Search and Linear Regression Project
- Logistic Regression Models
- KNN - K Nearest Neighbors
- SVM - Support Vector Machines
- Tree Based Methods - Decision Tree Learning
- Random Forests
- Boosting Methods
- Supervised Learning Capstone Project - Cohort Analysis & Customer Churn Predictions
- Naive Bayes Classification and Natural Language Processing (Supervised Learning)
- K Means Clustering (Unsupervised Learning)
- Hierarchical Clustering (Unsupervised Learning)
- DBSCAN (Unsupervised Learning)
- Principal Component Analysis (Unsupervised Learning)
- Data Analysis with Pandas
- Data Analysis with NumPy
- Linear Regression with Polyfit - Data 36
- Matplotlib - Data Visualizations
- Scikit-learn - Creating Machine Learning Models
- Milestone Project - Supervised Learning (Classification)- Heart Disease Detection
- Milestone Project - Supervised Learning (Regression)- Bulldozer Sales Price Prediction
- Deep Learning Project - Dog breed predictions
- Deep Learning and TensorFlow Fundamentals
- Neural Network Regression with Tensorflow
- The Fundamentals of Machine Learning
- The Machine Learning Landscape
- End-to-End Machine Learning Project
- Classification
- Training Models
- Supervised Learning
- Lesson 01: Machine Learning Bird's Eye View
- Lesson 02: Linear Regression
- Lesson 03: Perceptron Algorithm
- Lesson 04: Decision Trees
- Lesson 05: Naive Bayes
- Lesson 06: Support Vector Machines
- Lesson 07: Ensemble Methods
- Lesson 08: Model Evaluation Metrics
- Lesson 09: Training and Tuning
- Lesson 10: Finding Donors Project
- Week 01 - Exploring a Larger Dataset
- Week 02 - Augmentation: A technique to avoid overfitting
- Week 03 - Transfer Learning
- Week 04 - Multiclass Classification
DeepLearning.AI - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- Week 01 - A New Programming Paradigm
- Week 02 - Introduction to Computer Vision
- Week 03 - Enhancing Vision with CNN
- Week 04 - Using Real-world images
- Python Crash Course
- Python for Data Analysis - NumPy
- Python for Data Analysis - Pandas
- Python for Data Visualization - Matplotlib
- Python for Data Visualization - Seaborn
- Pandas Built In Data Visualization
- Visualization with Plotly and Cufflinks
- Data Capstone Projects
- Linear Regression
- Logistic Regression
- K Nearest Neighbors (KNN)
- Decision Tree and Random Forest
- Support Vector Machine (SVM)
- K Means Clustering
- Principal Component Analysis
- Recommender Systems
- Natural Language Processing
- Neural Nets and Deep Learning
- Big Data and Spark with Python
- SciPy
- Part 1 - The Field of Data Science
- Part 2 - Probability
- Part 3 - Statistics (Descriptive & Inferential)
- Part 4 - Python
- Part 5 - Advanced Statistical Methods in Python / Machine Learning in Python
- Part 6 - Mathematics
- Part 7 - Deep Learning
- Software Integration
- Case Study - Absenteeism
To skill up my ML & DS related skills in specific areas and topics:
- Project: Titanic dataset
- 01.ML Basic
- 02.Intro to Feature Engineering
- 03.Explore Data
- 04.Create and Clean Features
- 05.Prepare Features for Modelling
- 06.Compare and Evaluate Models
- Project: Titanic dataset
- 01.Review of Foundation
- 02.Logistic Regression
- 03.Support Vector Machine
- 04.Multi-layer Perceptron
- 05.Random Forest
- 06.Boosting
- 07.Final Model Selection and Evaluation
- Project: Titanic dataset
- 01.ML Basic
- 02.Exploratory Data Analysis and Data Cleaning
- 03.Evaluation - Measuring Success
- 04.Optimizing a Model
- 05.End to End Pipeline
- Assuming Data is good to go
- Neglecting to consult subject matter experts
- Overtiffing your models
- Not standardizing your data
- Focusing on Wrong Factors
- Data Leakage
- Forgetting traditional statistics tools
- Assuming Deployment is a breeze
- Assuming Machine Learning is the answer
- Developing in a silo
- Not treating for imbalanced sampling
- Interpreting your coefficients without properly treating for multicollinearity
- Evaluating by accuracy alone
- Giving overly technical presentations
- Practical Intro to Machine Learning: classification modelling
- 01.Exploratory Data Analysis
- 02.Model Fitting
- 03.Measuring Performance
- Python
- Pandas
- Data Cleaning
- Introduction to Machine Learning
- Machine Learning Intermediate
- Feature Engineering
- Machine Learning Explaniability
- Data Visualization
- Intro to Deep Learning
- Intro to Game AI and Reinforcement Learning
- Natural Language Processing
- Micro-challenges
- Computer Vision
- Intro to SQL
- Advanced SQL
- ML Crash Course
- Problem Framing
- Data Prep
- Clustering
- Recommendation
- Testing and Debugging
- GANs
- Deep Learning: Face Recognition
- Deep Learning - Image Recognition
- Buliding Deep Learning Applications with Keras 2.0
- Applied Machine Learning - Feature Engineering
- Applied Machine Learning - Algorithms
- Applied Machine Learning - Foundation
- ML - Mistakes to avoid in Machine Learning
- ML - Classification Modelling with Iris flowers
- Classification Modeling
- Data Science A-Z Modeling
- Designing for Neural Networks and AI Interfaces
- Introduction to GPT-3: A Leap in Artificial Intelligence
- The Hundreded page - Machine Learning book
- Introduction
- Notation and Definitions
- Fundamental Algorithms
- Anatomy of a Learning Algorithm
- Basic Practice
- Neural Networks and Deep Learning
- Problems and Solutions
- Advanced Practice
- Unsupervised Learning
- Unsupervised Learning - in-depth material
- Other Forms of Learning
- Conclusion
- Intro to Spark SQL and DataFrames
- Apache Spark Essential Training
- Spark for Machine Learning & AI
- Apache PySpark by Example
- Apache Spark Deep Learning Essential Training
- Advanced Pandas
- Data Visualizations with Plotly
- Data Analysis with Pandas and Python - BP
- Python Data Playbook - Cleaning Data
- Pandas Playbook - Manipulating Data
- More Python Data Tools - Microsoft
- 01.Plotly Foundation
- 02.Using Scenarios with Plotly
- 03.Creating Visualization with Plotly
- Basic Charts (Line Graph, Bar Graph, Scatter Plot, Bubble Chart)
- Statstical Charts (Histogram, Distribution, Scatter Matrix, Correlation Matrix with Imshow, Color Scales and Sequences)
- 3D Plotting (3D Scatter Plot, 3D Bubble Chart, 3D Line Chart, 3D Surface Plot)
- Mapping (Scatter Plot on Map, Choropleth, Mapbox and Geopandas)
- Sunburst Chart
- Snakey Chart
- 04.Adding Interactivity with Plotly
- Linear Regression Analysis
- Multi Regression Analysis
- Pratical Statistics
- Admission Case Study with Python (Simpson's Paradox)
- Simulating Coin Flips & Probability
- Stimulating multiple Coin Flips & Bionmial Distribution
- Cancer Test Results
- Conditional Probability & Bayes Rules
- Excel Data Manipulation, Analysis and Visualization
Topics include:
- Set theory, including Venn diagrams
- Properties of the real number line
- etc
This project is licensed under the MIT License - see the LICENSE.md file for details