Machine Learning

Data science, machine learning, python, R, big data, spark, the Jupyter notebook, and much more

Introduction to Python & Jupyter Notebook

  1. Introduction to Python

  2. Python setup on Macbook

  3. Creating virtual environment

  4. Jupyter notebook setup

  5. Markdown syntax in Jupyter notebook

This tutorial covers all above topics Introduction to Python 3.6 & Jupyter Notebook

Tutorials

  1. Creating custom Keras callback in python

  2. Image Manipulation Detection in Python

  3. Deploying Keras Model in Production

  4. Sentiment Analysis with VADER

  5. Machine Learning: Understanding Logistic Regression

  6. Part 1: Creating and testing Flask REST API

  7. Part 2: Deploy Flask API in production using WSGI gunicorn with nginx reverse proxy

  8. Part 3: Dockerize Flask application and build CI/CD pipeline in Jenkins

Tutorials for Connecting to Databases

  1. Connect to Cassandra

  2. Connect to PostGreSQL

  3. Connect to MySQL

ETL

  1. Export data from Google Analytics

Coding Problems

  1. Pass the ball game NxN matrix

  2. Python problem 1: find the runner-up score

  3. Check if a given word is Isogram in python

  4. Find if a number is Lychrel Number

  5. Write a program for Hailstone Sequence

  6. Write a python program to find Largest Substring that occurs more than once

  7. Find if credit card number is valid or not

  8. Find extra long factorials in python

  9. Spaced Out?

  10. Sequence of Differences in Python

Interview Questions

  1. Top 100 interview questions on Data Science & Machine Learning

  2. Google data science interview questions and answers

  3. Machine Learning based Multiple choice questions

  4. AWS Lambda Interview Questions

Flask and REST endpoints

  1. Introduction to Flask APIs

  2. Flask vs Django

  3. Making flask production ready using wsgi

  4. Creating REST endpoint

  5. Deployment using Docker

  6. Jenkins Job Setup

  7. Production readiness

Apache Spark

  1. Introduction to Apache Spark

  2. Setting up spark on iOS

  3. Introduction to pyspark

  4. Using Spark in Jupyter notebook

  5. Java self contained application for Spark

Google Dialog Flow

  1. Introduction to Google Dialog Flow, formerly api.ai

  2. What is RPA

  3. Usecases of DialogFlow

  4. Developing fulfilllment API in Spring Boot 2

Applications

  1. Writing Toxic Detection program in machine learning (python, keras, flask, docker)

  2. Gender Detection

  3. Sentiment analysis

Important resources for Learning

  1. Google Video Course for Machine Learning https://developers.google.com/machine-learning/crash-course/framing/video-lecture

  2. Rules of Machine Learning https://developers.google.com/machine-learning/guides/rules-of-ml/#rule_12_don_t_overthink_which_objective_you_choose_to_directly_optimize

  3. The Jupyter Notebook https://jupyter.org


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