AI for Python Training

• Introduction
  • What is Artificial Intelligence?
  • Applications of AI
  • Branches of AI
  • Building Agents
  • Development Environments
• Classification and Regression
  • Supervised vs. Unsupervised Learning
  • What is Classification?
  • Preprocessing and Encoding
  • Types of Classifiers
  • What is Regression?
  • Building Regressors
• Predictive Analytics
  • What is Ensemble Learning?
  • Using Decision Trees
  • Random Forests
  • Finding Optimal Training Parameters
  • Computing Relative Feature Importance
• Pattern Detection and Unsupervised Learning
  • What is Unsupervised Learning?
  • Clustering Data With K-Means
  • Estimating Clusters With Mean Shift
  • Gaussian Mixture Models
  • Affinity Propagation Models
• Recommender Systems
  • Building Recommender Systems
  • Creating a Training Pipeline
  • Extracting Nearest Neighbors
  • Computing Similarity Scores
  • Collaborative Filtering
• Logic Programming
  • What is Logic Programming?
  • Solving Problems With Logic Programming
  • Matching Mathematical Expressions
  • Validating Primes
• Heuristic Searches
  • Heuristic Search Techniques
  • Constraint Satisfaction Problems
  • Local Search Techniques
  • Solving Problems With Constraints
• Genetic Algorithms
  • Evolutionary and Genetic Algorithms
  • Fundamental Concepts
  • Generating a Bit Pattern
  • Visualizing the Evolution
  • Solving the Symbol Regression Problem
• Building Games
  • Using Search Algorithms in Games
  • Combinatorial Search
  • Minimax Algorithm
  • Alpha-Beta Pruning
  • Negamax Algorithm
  • Building Game Bots
• Natural Language Processing
  • Tokenizing Text Data
  • Converting Words to Base Forms
  • Dividing Text Into Chunks
  • Extracting Word Frequencies
  • Topic Modeling Using Latent Dirichlet Allocation
• Probabilistic Reasoning
  • Understanding Sequential Data
  • Slicing Time-Series Data
  • Extracting Statistics from Time-Series Data
  • Generating Data Using Hidden Markov Models
  • Identifying Alphabet Sequences
• Speech Recognizers
  • Working With Speech Signals
  • Visualizing Audio Signals
  • Transforming Audio Signals to the Frequency Domain
  • Generating Audio Signals
  • Synthesizing Tones
  • Extracting Speech Features
  • Recognizing Spoken Words
• Object Detection and Tracking
  • Frame Differencing
  • Tracking Objects Using Colorspaces
  • Tracking Objects Using Background Subtraction
  • Optical Flow Based Tracking
• Artificial Neural Networks
  • Building a Perceptron Based Classifier
  • Single Layer Neural Networks
  • Multilayer Neural Networks
  • Vector Quantizers
• Reinforcement Learning
  • Understanding the Premise
  • Reinforcement Learning vs. Supervised Learning
  • Building Blocks of Reinforcement Learning
  • Creating an Environment
  • Building a Learning Agent
• Deep Learning and Convolutional Neural Networks
  • What are Convolutional Neural Networks?
  • Architecture
  • Types of Layers
  • Building a Perceptron Based Linear Regressor

Before Taking this Class

Students should have solid experience in writing programs using Python.

Software/Setup For this Class

Python 3 or higher and Anaconda.