course | Introduction to Machine Learning and Artificial Intelligence

The Business of AI Adoption
 

• Defining artificial intelligence
• The uncertainties of new technology
• AI in the field
• The problem of trust
• Work is evolving
• Driverless transportation
• Trust and the machine
• The human-smart machine trust gap
• Trusting a smart machine
• Trusting the smart machine developer

 

Artificial Intelligence (AI)
 

• Artificial Intelligence and Its Application Areas
• AI and Machine learning
• Pattern recognition
• Supervised and unsupervised learning,
• Structured and unstructured data
• Discussion on Industry, industrial analytics 

Machine Learning
 

• Basic ideas of machine learning
• Bias-variance complexity trade-off
• Model types
• Deep neural network
• Recurrent neural network or long short-term memory network
• Support vector machines
• Random forest or decision trees
• Self-organizing maps (SOM)
• Bayesian network and ontology
• Training and assessing a model
• How good is my model?
• Role of domain knowledge
• Optimization using a model

 

Getting Started with Python
 

• Installing a Python Data Science Environment
• Using and understanding iPython (Jupyter) Notebooks
• Python basics: Part 1
• Understanding Python code
• Importing modules
• Python basics: Part 2
• Running Python scripts

 

Statistics and Probability Refresher and Python Practice
 

• Types of data
• Mean, median, and mode
• Using mean, median, and mode in Python
• Standard deviation and variance
• Probability density function and probability mass function
• Types of data distributions
• Percentiles and moments 

Matplotlib and Advanced Probability Concepts
 

• A crash course in Matplotlib
• Covariance and correlation
• Conditional probability
• Bayes' theorem

 

Algorithm Overview
 

• Data Prep
• Linear Algorithms
• Non-Linear Algorithms
• Ensembles

 

Predictive Models
 

• Linear regression
• Polynomial regression
• Multivariate regression and predicting car prices
• Multi-level models

 

Applied Machine Learning with Python
 

• Machine learning and train/test
• Using train/test to prevent overfitting of a polynomial regression
• Bayesian methods: Concepts
• Implementing a spam classifier with Naïve Bayes
• K-Means clustering 

Recommender Systems

• What are recommender systems?
• Item-based collaborative filtering
• How item-based collaborative filtering works?
• Finding movie similarities
• Improving the results of movie similarities
• Making movie recommendations to people
• Improving the recommendation results

 

More Applied Machine Learning Techniques

• K-nearest neighbors - concepts
• Using KNN to predict a rating for a movie
• Dimensionality reduction and principal component analysis
• A PCA example with the Iris dataset
• Data warehousing overview
• Reinforcement learning

 

Dealing with Data in the Real World

• Bias/variance trade-off
• K-fold cross-validation to avoid overfitting
• Data cleaning and normalization
• Cleaning web log data
• Normalizing numerical data
• Detecting outliers 

Apache Spark: Machine Learning on Big Data
 

• Installing Spark
• Spark introduction
• Spark and Resilient Distributed Datasets (RDD)
• Introducing MLlib
• Decision Trees in Spark with MLlib
• K-Means Clustering in Spark
• TF-IDF
• Searching Wikipedia with Spark MLlib
• Using the Spark 2.0 DataFrame API for MLlib

 

Testing and Experimental Design
 

• A/B testing concepts
• T-test and p-value
• Measuring t-statistics and p-values using Python
• Determining how long to run an experiment for
• A/B test gotchas

 

GUIs and REST
 

• Build a UI for your Models
• Build a REST API for your Models