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About this course

Artificial Intelligence (AI) is a way to make machines think and behave intelligently. These machines are controlled by software inside them, so AI has a lot to do with intelligent software programs that control these machines.

FAQs
Can I just enroll in a single course? I'm not interested in the entire course

Yes, One can enroll for one particular day as well based on their interested topics. Topics details are listed under syllabus tab.

What is the refund policy?

We will refund the entire cost if candidate is not happy with training.

What background knowledge is necessary

We do not presume any kind of prior knowledge before starting this course. Candiate can opt this course without any prior knowledge.

Do I need to take the courses in a specific order

No, You can take this course at any point of time without having any prior knowledge.

Syllabus

This course is organized in such a way as to serve as an in depth knowledge of Artificial Intelligence. Each topics will be covered in details with examples.

Module 1
Total Session: 6
Introduction to Artificial Intelligence
  • What is Artificial Intelligence?
    30 minutes
  • Applications of AI
    30 minutes
  • Branches of AI
    30 minutes
  • Making machines think like humans
    30 minutes
  • Building an intelligent agent
    30 minutes
  • Types of models
    30 minutes
  • Tests
    10 Questions
Module 2
Total Session: 9
Classification and Regression Using Supervised Learning
  • Supervised versus unsupervised learning
    30 minutes
  • Preprocessing data
    30 minutes
  • Binarization
    30 minutes
  • Mean removal
    30 minutes
  • Scaling and Normalization
    30 minutes
  • Logistic Regression classifier
    30 minutes
  • Naïve Bayes classifier
    30 minutes
  • Confusion matrix
    30 minutes
  • Regression
    30 minutes
  • Tests
    10 Questions
Module 3
Total Session: 5
Predictive Analytics with Ensemble Learning
  • What is Ensemble Learning?
    30 minutes
  • Decision Trees
    30 minutes
  • Random Forests
    30 minutes
  • Dealing with class imbalance
    30 minutes
  • Predicting traffic using Extremely Random Forest regressor
    30 minutes
  • Tests
    10 Questions
Module 4
Total Session: 3
Detecting Patterns with Unsupervised Learning
  • What is unsupervised learning?
    30 minutes
  • Clustering data with K-Means algorithm
    30 minutes
  • Gaussian Mixture Models
    30 minutes
  • Tests
    10 Questions
Module 5
Total Session: 4
Building Recommender Systems
  • Creating a training pipeline
    30 minutes
  • Building a K-Nearest Neighbors classifier
    30 minutes
  • Finding similar users using collaborative filtering
    30 minutes
  • Building a movie recommendation system
    30 minutes
  • Tests
    10 Questions
Module 6
Total Session: 7
Logic Programming
  • What is logic programming?
    30 minutes
  • Understanding the building blocks of logic programming
    30 minutes
  • Solving problems using logic programming
    30 minutes
  • Matching mathematical expressions
    30 minutes
  • Parsing a family tree
    30 minutes
  • Analyzing geography
    30 minutes
  • Building a puzzle solver
    30 minutes
  • Tests
    10 Questions
Module 7
Total Session: 10
Heuristic Search Techniques
  • What is heuristic search?
    30 minutes
  • Uninformed versus Informed search
    30 minutes
  • Constraint Satisfaction Problems
    30 minutes
  • Local search techniques
    30 minutes
  • Simulated Annealing
    30 minutes
  • Constructing a string using greedy search
    30 minutes
  • Solving a problem with constraints
    30 minutes
  • Solving the region-coloring problem
    30 minutes
  • Building an 8-puzzle solver
    30 minutes
  • Building a maze solver
    30 minutes
  • Tests
    10 Questions
Module 8
Total Session: 5
Genetic Algorithms
  • Fundamental concepts in genetic algorithms
    30 minutes
  • Generating a bit pattern with predefined parameters
    30 minutes
  • Visualizing the evolution
    30 minutes
  • Solving the symbol regression problem
    30 minutes
  • Building an intelligent robot controller
    30 minutes
  • Tests
    10 Questions
Module 9
Total Session: 10
Building Games With Artificial Intelligence
  • Using search algorithms in games
    30 minutes
  • Combinatorial search
    30 minutes
  • Minimax algorithm
    30 minutes
  • Alpha-Beta pruning
    30 minutes
  • Negamax algorithm
    30 minutes
  • Installing easyAI library
    30 minutes
  • Building a bot to play Last Coin Standing
    30 minutes
  • Building a bot to play Tic-Tac-Toe
    30 minutes
  • Building two bots to play Connect Four™ against each other
    30 minutes
  • Building two bots to play Hexapawn against each other
    30 minutes
  • Tests
    10 Questions
Module 10
Total Session: 8
Natural Language Processing
  • Tokenizing text data
    30 minutes
  • Converting words to their base forms using stemming and lemmatization
    30 minutes
  • Dividing text data into chunks
    30 minutes
  • Extracting the frequency of terms using a Bag of Words model
    30 minutes
  • Building a category predictor
    30 minutes
  • Constructing a gender identifier
    30 minutes
  • Building a sentiment analyzer
    30 minutes
  • Topic modeling using Latent Dirichlet Allocation
    30 minutes
  • Tests
    10 Questions
Module 11
Total Session: 7
Probabilistic Reasoning for Sequential Data
  • Understanding sequential data
    30 minutes
  • Handling time-series data with Pandas
    30 minutes
  • Slicing and Operating on time-series data
    30 minutes
  • Extracting statistics from time-series data
    30 minutes
  • Generating data using Hidden Markov Models
    30 minutes
  • Identifying alphabet sequences with Conditional Random Fields
    30 minutes
  • Stock market analysis
    30 minutes
  • Tests
    10 Questions
Module 12
Total Session: 7
Building A Speech Recognizer
  • Working with speech signals
    30 minutes
  • Visualizing audio signals
    30 minutes
  • Transforming audio signals to the frequency domain
    30 minutes
  • Generating audio signals
    30 minutes
  • Synthesizing tones to generate music
    30 minutes
  • Extracting speech features
    30 minutes
  • Recognizing spoken words
    30 minutes
  • Tests
    10 Questions
Module 13
Total Session: 10
Object Detection and Tracking
  • Installing OpenCV
    30 minutes
  • Frame differencing
    30 minutes
  • Tracking objects using colorspaces
    30 minutes
  • Object tracking using background subtraction
    30 minutes
  • Building an interactive object tracker using the CAMShift algorithm
    30 minutes
  • Optical flow based tracking
    30 minutes
  • Face detection and tracking
    30 minutes
  • Using Haar cascades for object detection
    30 minutes
  • Using integral images for feature extraction
    30 minutes
  • Eye detection and tracking
    30 minutes
  • Tests
    10 Questions
Module 14
Total Session: 8
Artificial Neural Networks
  • Introduction to artificial neural networks
    30 minutes
  • Building and Training a neural network
    30 minutes
  • Building a Perceptron based classifier
    30 minutes
  • Constructing single and multilayer neural network
    30 minutes
  • Building a vector quantizer
    30 minutes
  • Analyzing sequential data using recurrent neural networks
    30 minutes
  • Visualizing characters in an Optical Character Recognition database
    30 minutes
  • Building an Optical Character Recognition engine
    30 minutes
  • Tests
    10 Questions
Module 15
Total Session: 5
Reinforcement Learning
  • Reinforcement learning versus supervised learning
    30 minutes
  • Real world examples of reinforcement learning
    30 minutes
  • Building blocks of reinforcement learning
    30 minutes
  • Creating an environment
    30 minutes
  • Building a learning agent
    30 minutes
  • Tests
    10 Questions
Module 16
Total Session: 5
Deep Learning with Convolutional Neural Networks
  • Introduction and Architecture of CNNs
    30 minutes
  • Types of layers in a CNN
    30 minutes
  • Building a perceptron-based linear regressor
    30 minutes
  • Building an image classifier using a single layer neural network
    30 minutes
  • Building an image classifier using a Convolutional Neural Network
    30 minutes
  • Tests
    10 Questions
Reviews
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Sep 29, 2017 at 9:48 am
Sep 29, 2017 at 9:48 am
Sep 29, 2017 at 9:48 am