Introduction to Machine Learning and Artificial Intelligence

Machine Learning

• Machine learning overview
• 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 evaluation of the model
• The role of domain knowledge
• Optimization using a model

Phyton

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

Statistics and Probability

• Types of data
• Mean, median, and mode
• Standard deviation and variance
• Probability density function
• 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 training
• Machine learning testing
• Prevent hyper-polynomial regression
• Bayesian Methods
• Implementing the Spam Classifier with Naïve Bayes
• K- means cluster

Recommender Systems

• Introduction
• Item-based collaborative filtering
• Item-based collaborative Process
• Finding movie similarities
• Improving the results of movie similarities
• Making movie recommendations
• Improving the recommendation results

Machine Learning Techniques

• K-nearest neighbors
• KNN and  predict a rating for a movie
• Dimensionality reduction and principal component analysis
• Data warehousing overview
• Reinforcement learning

Dealing with Data in the Real World

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

Apache Spark: Machine Learning on Big Data

• Installing Spark
• Spark introduction
• Resilient Distributed Datasets (RDD)
• MLlib introduction
• Decision Trees in Spark with MLlib
• K-Means Clustering in Spark
• TF-IDF
• Searching wikipedia with Spark MLlib

Testing and Experimental Design

• A and B testing concepts
• T-test and p-value
• Measuring t-statistics and p-values using Python
• Determining  runtime  for experiment
• A and B test gotchas

GUIs and REST

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