Artificial Intelligence

What you learn in Artificial Intelligence Course?

• Introduction to Artificial Intelligence

What is Artificial Intelligence?
Why do we need to study AI?
Applications of AI
Branches of AI
Defining intelligence using Turing Test
Making machines think like humans
Building rational agents
General Problem Solver
Building an intelligent agent
Installing Python 3
Installing packages
Loading data


• Introduction to Machine Learning

Why Machine Learning?
Problems Machine Learning Can Solve
Types of Machine Learning
Why Python?
Installing scikit-learn
Essential Libraries and Tools
Jupyter Notebook
NumPy, SciPy, matplotlib, pandas
Python 2 Versus Python 3
Meet the Data


• Classification and Regression using Supervised Learning

Supervised versus unsupervised learning
What is classification?
Preprocessing data
Label encoding
Logistic Regression classifier
Naïve Bayes classifier
Confusion matrix
Support Vector Machines
What is Regression?
Building a single variable regressor
Building a multivariable regressor


• Predictive Analytics with Ensemble learning

What is Ensemble Learning?
What are Random Forests and Extremely Random Forests?
Dealing with class imbalance
Finding optimal training parameters using grid search
Computing relative feature importance
Predicting traffic using Extremely Random Forest regressor


• Detective Patterns with Unsupervised Learning

What is unsupervised learning?
Clustering data with K-Means algorithm
Estimating the number of clusters with Mean Shift algorithm
Estimating the quality of clustering with silhouette scores
What are Gaussian Mixture Models?
Building a classifier based on Gaussian Mixture Models
Finding subgroups in stock market using Affinity Propagation model
Segmenting the market based on shopping patterns

• Building Recommender Systems

Creating a training pipeline
Extracting the nearest neighbors
Building a K-Nearest Neighbors classifier
Computing similarity scores
Finding similar users using collaborative filtering
Building a movie recommendation system


• Logic Programming

What is logic programming?
Understanding the building blocks of logic programming
Solving problems using logic programming
Installing Python packages
Matching mathematical expressions
Validating primes
Analyzing geography
Parsing a family tree
Building a puzzle solver


• Heuristic Search Techniques

What is heuristic search?
Constraint Satisfaction Problems
Local search techniques
Constructing a string using greedy search
Solving a problem with constraints
Solving the region-coloring problem
Building an 8-puzzle solver
Building a maze solver


• Genetic Algorithms

Understanding evolutionary and genetic algorithms
Fundamental concepts in genetic algorithms
Generating a bit pattern with predefined parameters
Visualizing the evolution
Solving the symbol regression problem
Building an intelligent robot controller


• Building games with Artificial Intelligence

Using search algorithms in games
Combinatorial search
Minimax algorithm
Alpha-Beta pruning
Negamax algorithm
Installing easyAI library
Building a bot to play Last Coin Standing
Building a bot to play Tic-Tac-Toe
Building two bots to play Connect Four™ against each other
Building two bots to play Hexapawn against each other


• Natural Language Processing

Introduction and installation of packages
Tokenizing text data
Converting words to their base forms using stemming
Converting words to their base forms using lemmatization
Dividing text data into chunks
Extracting the frequency of terms using a Bag of Words model
Building a category predictor
Constructing a gender identifier
Building a sentiment analyzer
Topic modeling using Latent Dirichlet Allocation


• Probabilistic Reasoning for Sequential Data

Understanding sequential data
Handling time-series data with Pandas
Slicing time-series data
Operating on time-series data
Extracting statistics from time-series data
Generating data using Hidden Markov Models
Identifying alphabet sequences with Conditional Random Fields
Stock market analysis


• Building a Speech Recognizer

Working with speech signals
Visualizing audio signals
Transforming audio signals to the frequency domain
Generating audio signals
Synthesizing tones to generate music
Extracting speech features
Recognizing spoken words


• Object Detecting and Tracking

Installing OpenCV
Frame differencing
Tracking objects using colorspaces
Object tracking using background subtraction
Building an interactive object tracker using the CAMShift algorithm
Optical flow based tracking
Face detection and tracking
Eve detection and tracking


• Artificial Neural Networks

Introduction to artificial neural networks
Building a Perceptron based classifier
Constructing a single layer neural network
Constructing a multilayer neural network
Building a vector quantizer
Analyzing sequential data using recurrent neural networks
Visualizing characters in an Optical Character Recognition database
Building an Optical Character Recognition engine


• Re-Inforcement Learning

Understanding the premise
Reinforcement learning versus supervised learning
Real world examples of reinforcement learning
Building blocks of reinforcement learning
Creating an environment
Building a learning agent


• Deep Learning with Convolutional Neural Networks

What are Convolutional Neural Networks?
Architecture of CNNs
Types of layers in a CNN
Building a perceptron-based linear regressor
Building an image classifier using a single layer neural network
Building an image classifier using a Convolutional Neural Network