Exploratory Data Analysis for Machine Learning Flashcards
In this overview, we will discuss
-Define Artificial Intelligence (AI) Machine Learning (ML) and Deep Learning (DL).
-Explain how DL helps solve classical ML Limitations.
- Explain key historical developments, and the hype-AI winter cycle.
- Differentiate modern AI from prior AI.
- Relate sample applications of AI.
Artificial Intelligence (AI)
A Program that can sense, reason, act and adapt.
Machine Learning
Algorithms whose performance improve as they are exposed to more data over time.
Deep Learning
Subset of machine learning in which multilayered neural networks learn from vests amounts of data.
Artificial Intelligence dictionary definition.
A Branch of computer science dealing with the simulation of intelligent behavior’s in computers. - Merriam-Webster
Machine Learning
The Study and construction of programs that are not explicitly programmed, but learn patterns as they are exposed to more data over time.
Two types of Machine Learning
-Supervised Learning
-Unsupervised Learning
Supervised Learning
- Dataset: Has a target Column
- Goal :Make Predictions
-Example Fraud Detection
Unsupervised Learning
-Dataset: Does not have a Target Column
- Goal: Find Structure in data.
-Example: Customer segmentation
Machine Learning( example)
- Suppose you wanted to identify fraudulent credit card transactions.
(you could define features to be:
-Transaction time - Transaction amount
-Transaction location
-Category of purchase - The algorithm could learn what feature combinations suggest unusual activity.
Deep Learning
Machine learning that involves using very complicated models called deep neural networks.
- Models determine best representation of original data; in classic machine learning, humans must do this.
Deep Learning example
Classic machine learning - Feature Detection, Machine Learning Classifier Algorithm - Arjun (output).
- Deep Learning ( Steps 1 and 2 are combined into 1 step )
using complex model neural Network.
Deep Learning
Machine learning that involves using very complicated models called deep neural networks.
Deep Learning
Machine learning that involves using very complicated models called deep neural networks.
History of AI
-AI has experienced several hype cycles, where it has oscillated between periods of excitement and disappointment.
-AI has experienced cycles of AI winters and AI booms.
AI solutions include speech recognition, computer vision, assisted medical diagnosis, robotics, and others.
Learning Goals
In this section, we will cover:
- Background and tools used in this course.
-Machine Learning workflow
-Machine Learning vocabulary
Background and Tools
-Examples assume familiarity with:
- Python libraries(e.g. NumPy or Pandas), Jupyter Notebooks.
- Basic statistics including probability, calculating moments, bayes’ rule.
Examples use iPython(via Jupyter Lab/Notebook), with the following libraries:
-NumPy
-Pandas(We will usually read data into a Pandas DataFrame)
-Matplotlib
-Seaborn
-Scikit-Learn
-TensorFlow
-Keras
Machine Learning Workflow
-Problem Statement: What Problem are you trying to solve?
-Data Collection: What data do you need to solve it?
- Data Exploration and Preprocessing: How should you clean your data so y our model can use it?
- Modeling: Build a model to solve your Problem?
-Validation: Did I solve the problem?
-Decision Making and deployment: Communicate to stakeholders or put into production?
Machine Learning Vocabulary
Target: Category or value that we are trying to predict.
Features: Properties of the data used for prediction ( explanatory variables).
Example:/Observation a single data point within the data (one row).
Label: the target value for a single data point.
Modern AI
Factors that have contributed to the current state of Machine Learning are: bigger data sets, faster computers, open source packages, and a wide range of neural network architectures.
Learning Goals
In this section, we will cover:
- Retrieving data from multiple data sources:
- SQL databases
- NoSQL databases
- APIs
- Cloud data sources
- Understand common issues that arise with importing data.
Reading CSV Files
Comma-separated (CSV) files consist of rows of data, separated by commas.
JSON Files
JavaScript Object Notation (JSON) files are a standard way to store data across platforms.
JSON files are very similar in structure to Python dictionaries.