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Artificial Intelligence Course Content

Description

AI is any technique, code or algorithm that enables machines to develop, demonstrate and mimic human cognitive behavior or intelligence and hence the name “Artificial Intelligence”. Some of the most successful applications of AI around us can be seen in Robotics, Computer Vision,

Virtual Reality, Speech Recognition, Automation, Gaming and so on…

WHY YOU SHOULD TAKE THIS TRAINING COURSE?

Artificial Intelligence is constantly pushing the boundaries of what machines are capable of. The Main purpose of training real time smart machine is to use their speed and capability. Most importantly machine can think and perform task like humans. By this course student will be able to design and develop an advance AI System.

Learning Outcomes
  • Python Programming for ML
  • Supervised based algo implementation
  • Matplotlib for graph plots with linear regression
  • Live Image Processing
  • Image Recognition
  • NLP and Cloud Connectivity
  • Secured AI with ML and IoT

Introduction

Artificial Intelligence
  • Introduction to Artificial Intelligence (AI)
  • History of AI
  • Importance and other Philosophies about AI
  • General Approaches and Goals of AI
  • Components of AI
  • Working Domains/Companies/Products in Current Market
  • Programming Languages Used for AI
  • Python Programming
Python programming
  • Basic of python and why python for machine learning
  • Installation of software on different OS.
  • Understanding basic syntax with data types
  • Number, String, List, Tuple, Dictionary
  • Extracting data from a file
  • Committing your code to GIT
More about python programming
  • Conditional statement and loops
  • Function and modules
  • File handling
  • Creating own modules / library
  • Web scraping with urllib2
  • Grabbing system information from Popen and os library
  • Scanning Network IP & MAC address with loops
Libraries Used
  • Introduction to Numpy & Matplotlib
  • Managing arrary with numpy
  • Multidimensional array with numpy
  • Unit matrix handling & creating
  • Deleting indexes from matrix
  • Deep dive with Matplotlib
  • Drawing general purpose graphs
  • Graphs with mathematics
Machine learning techniques
  • Types of learning
  • Advice of applying machine learning
  • Machine learning System Design
  • Decision Tree Classifier
  • Training your machine with real time datasets
  • Deep dive with UCI
  • Lab session for loading data from different APIs
  • Detecting data from numpy and converting for training and testing data
  • testing data
  • Exercise with ML and others framework
  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with scikit-learn
  • Separating data with numpy
  • Training classifier
  • Algo data process view
  • Decision Tree understanding
Linear Regression
  • Using House Price Prediction
  • Simple Linear Regression
  • Polynomial Linear Regression
  • Cost Function of Linear Regression
  • Understanding linear regression using matrix
Logistic Regression
  • Using Iris dataset to understand logistic regression
  • Concept of linearly separable data
  • Cost Function & Mathematical Foundation
  • Using Iris dataset to understand logistic regression
  • Concept of linearly separable data
  • Cost Function & Mathematical Foundation
Neural Networks analysis
  • Introduction to Neural Network
  • Understanding neural networks
  • Data learning and machine predictions
  • Neural networks real understanding
  • Neural network implementation with real datasets
  • Natural Language Processing
  • 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
More About ANN
  • Perception
  • Back Propagation/Training Algo’s
  • Convolutional & Recurrent and Artificial Neural Networks
  • Deep Neural Network
Natural Language Processing (NLP)
  • Introduction to NLP
  • Word Representation Model
  • Sentence Classification
  • Language Modeling

Project:- Building AI based ChatBot ussing Tensorflow

Feature Engineering
  • Categorical Features
  • Text Features
  • Image Features
  • Derived Features
  • Imputation of Missing Data
  • Feature Pipelines - Transformer & Estimator
Naive Bayes Classification
  • Bayesian Classification
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • When to Use Naive Bayes
  • Application : Identify category from text
k-Means Clustering
  • Introducing k-Means
  • Understanding cost function for unsupervised algorithms
  • Elbow rule to decide number of clusters
  • Application : Image compression
  • Application : Detection of number of characters in arabic
Decision Trees and Random Forests
  • Understaning Decision trees
  • Printing tree
  • Motivating Random Forests: Decision Trees
  • Ensembles of Estimators: Random Forests
  • Random Forest Regression
  • Application: Random Forest for Classifying Digits
  • Other Boosting techniques - AdaBoost, Gradient Tree Boosting
Genetic Algorithms
  • Fundamental concepts in genetic algorithms
  • Generating a bit pattern with predefined parameters
  • Visualising the evolution
  • Solving the symbol regression problem
  • Building an intelligent robot controller

Project : Handwriting recognition with Neural Network

Building Recommender Systems
  • Extracting the nearest neighbors
  • Building a K-Nearest Neighbors classifier
  • Computing similarity scores
  • Finding similar users using collaborative filtering
  • Building a movie recommendation system
Web-scraping
  • Understanding BeautifulSoup
  • Scraping for text, images
  • Searching Links,Data
  • web data extraction: extracting data from websites
Image Processing and ML
  • Introduction to Image Processing
  • How image search is going to work
  • Taking pictures with python for image processing
  • Loading and registering images
  • Object detection

Project: AI Based Face Detection and Recognition

Project: Employee Exit Prediction

Project: Whether Prediction for Auto Irrigation System

Project: Ecommerce Product Recommendation

Project: An AI Robo Prototype Like Sofia

Courses Features

  • Language
    English
  • Lectures
    02
  • Certification
    Yes
  • Project
    02
  • Duration
    40 hrs
  • Max-Students
    20
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