#### Advanced Artificial intelligence Training In Hyderabad

###### 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

- a problem with GPS

• Building an intelligent agent

- Types of models

• Installing Python 3

- Installing on Ubuntu

- Installing on Windows

• Installing packages

• Loading data

• Predicting traffic using Extremely Random Forest regressor

###### DETECTING 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

###### CLASSIFICATION AND REGRESSION USING SUPERVISED LEARNING

• Supervised versus unsupervised learning

• What is classification?

• Preprocessing data

- Binarization

- Mean removal

- Scaling

- Normalization

• Label encoding

• Logistic Regression classifier

• Naïve Bayes classifier

• Confusion matrix

• Support Vector Machines

• Classifying income data using Support Vector Machines

• What is Regression?

• Building a single variable regressor

• Building a multivariable regressor

• Estimating housing prices using a Support Vector Regressor

###### 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

• Parsing a family tree

• Analyzing geography

• Building a puzzle solver

###### PREDICTIVE ANALYTICS WITH ENSEMBLE LEARNING

• What is Ensemble Learning?

- Building learning models with Ensemble Learning

• What are Random Forests and Extremely Random Forests?

- Building Random Forest and Extremely Random Forest classifiers

- Estimating the confidence measure of the predictions

• Dealing with class imbalance

• Finding optimal training parameters using grid search

• Computing relative feature importance

• 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

###### HEURISTIC SEARCH TECHNIQUES

• What is heuristic search?

- Uninformed versus Informed search

• Constraint Satisfaction Problems

• Local search techniques

- Simulated Annealing

• 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

• Synthesizing tones to generate music

• Extracting speech features

• Recognizing spoken words

###### BUILDING GAMES WITH ARTIFICIAL INTELLIGENCE

• Using search algorithms in games

• Combinatorial search

• Minimax algorithm

• Alpha-Beta pruning

• Negamax algorithm

• nstallingeasyAI 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

###### OBJECT DETECTION 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

- Using Haar cascades for object detection

- Using integral images for feature extraction

• Eye detection and tracking

###### 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

###### ARTIFICIAL NEURAL NETWORKS

• Introduction to artificial neural networks

- Building a neural network

- Training a neural network

• 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

###### 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

###### REINFORCEMENT 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

###### BUILDING A SPEECH RECOGNIZER

• Working with speech signals

• Visualizing audio signals

• Transforming audio signals to the frequency domain

• Generating audio signals

###### 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