Andrew Ferlitsch

20 Seguidores

Research Scientist with over 20 years experience in research fields and 30 years of industry experience. Have worked a wide range of technology fields for commercial driven research. Primary expertise in last 5+ years has been on geospatial technologies, data science/analytics, open/big data, and autonomous vehicles. Secondary expertise in cloud deployment, databases, digital signage, teleconferencing software, sensor grids, energy management. Long history of contributing back to the field from shareware (80s) to open source (90s) to crowd-sourcing and micro-incubating (2000s). Established and well-recognized in my field among peers international. Decades of experienced working in multi...

ai data science machine learning artificial intelligence neural networks artifical intelligence python statistics robotics natural language processing datasets search intelligent agent learning agent pareto testing quaity assurance qa whiteboard interview coding challenges software programming object oriented programming oop nlp machine learniing recurrent neural networks convolutional neural networks pandas numpy random forest ensemble methods linear regression supervised learning training data dataset tensorflow dynamic programming softmax squashing function argmax central limit theorem z-score reinforcement learning perceptron bellman equations markov principles conditional probability probability local search greedy a-star graph search ids dfs bfs graph

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Andrew Ferlitsch

AI - Intelligent Agents

Pareto Principle Applied to QA

Whiteboarding Coding Challenges in Python

Object Oriented Programming Principles

Python - OOP Programming

Python - Installing and Using Python and Jupyter Notepad

Natural Language Processing - Groupings (Associations) Generation

Natural Language Provessing - Handling Narrarive Fields in Datasets for Classification

Machine Learning - Introduction to Recurrent Neural Networks

Machine Learning - Introduction to Convolutional Neural Networks

Machine Learning - Introduction to Neural Networks

Python - Numpy/Pandas/Matplot Machine Learning Libraries

Machine Learning - Accuracy and Confusion Matrix

Machine Learning - Ensemble Methods

ML - Multiple Linear Regression

ML - Simple Linear Regression

Machine Learning - Dummy Variable Conversion

Machine Learning - Splitting Datasets

Machine Learning - Dataset Preparation

Machine Learning - Introduction to Tensorflow