100 Days Of ML Code with CITY.AI

Code Together with CITY.AI - FREE!! 100 Days of ML Code Challenge

Week 1: All Required Basics**

• Review Python Basics
• Review Linear Algebra, Calculus, Maths
• Intro to Github and how to use it for team collaboration in Silicon-Valley style

Week 2: Machine Learning Foundations

• NumPy, Pandas, Python libraries
• Import dataset from various sources: files, databases, public datasets
• Handling missing data
• Encoding categorical data
• Feature engineering
• Model evaluation and validation assessment

Week 3: Data Visualization

• web scraping
• Matplotlib, Seaborn
• Plotly
• Bokeh
• Dash
• Other libraries

Week 4: Supervised Learning: Regression

• Simple Linear Regression
• Gradient Descent
• Higher Dimensions Linear Regression
• Polynomial Regression
• Regularization

Week 5: Supervised Learning : Classification

• Perceptron Algorithm
• Logistic Regression
• K-Nearest Neighbours
• Decision Trees
• Naive Bayes
• Support Vector Machine
• Ensemble Methods
• Parameters Tuning
• XGboost

Week 6: Supervised Learning Practice Projects

Week 7: Unsupervised Learning: Clustering

• K-Mean clustering
• Hierarchical and Density-based
• Gaussian Mixture Model and Cluster Validation
• Feature Scaling
• Principle Component Analysis (PCA)

Week 8: Unsupervised Learning Practice Projects

Week 9: Feature Extraction and Dimensionality Reduction

• Random Projection
• Independent Component Analysis (ICA)

Week 10: Feature Extraction and Dimensionality Reduction Practice Projects

Week 11: Introduction to Deep Learning

• Neural Networks
• Cloud Computing
• Deep Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Deep Learning Example Applications

Week 12: Deep Learning Practice Projects

Week 13: DevOps for ML/DL/AI Engineers

• Basics of Flask, Docker, Kubenetes
• REST API
• DevOps Basics (Continuous Integration, Continuous Delivery, CI/CD pipeline, DevOps tools)
• DevOps Lab:
  • Step1: Model development
  • Step2: Developing the interface our Flask app will use to load and call the model
  • Step3: Building the Docker Image with our Flask REST API and model
  • Step4: Testing our Docker image before deployment
  • Step5: Creating our Kubernetes cluster and deploying our application to it
  • Step6: Testing the deployed model
  • Step7: Testing the throughput of our model
  • Step8: Update our model and automate the deployment process
  • Step9: Cleaning up resources

Week 14: Work on your own Capstone Project

Week 15: Hackathon-Style Final Pitching your Capstone Project

Technology/Software Stack

• Google Colab
• Jupyter Notebook
• Python
• Keras
• Tensorflow
• Cloud services (to be announce)

For more information please visit out event bright event page here: https://www.eventbrite.com/e/code-together-with-bangkok-ai-free-100-days-of-ml-code-challenge-tickets-54478303071

Note: Big thianks for Siraj Raval for his original initiation and idea (https://youtu.be/cuQMBj1cWPo)

please share this on social media with #100DaysOfMLCode to give a shout out for him