Artificial Intelligence

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Artificial Intelligence Course Overview

Artificial Intelligence course is designed for the target audience who are keen to learn deeply about the techniques of AI and its applications. Artificial Intelligence course covers all the key concepts of AI and helps you to understand with case studies and project work. Student will learn AI by mastering natural language processing, deep neural networks, predictive analytics, reinforcement learning, and Python programming language from scratch.

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Course Fees: Rs.15000

  • Duration : 2 months
  • No Cost EMI : INR 7500 x 2 month

NEFT Payment Bank account details: Account Name – KLMS Hands-On Systems Private Limited, Account No – 50200042627525, IFSC – HDFC000027

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Course Description!

Artificial Intelligence Course Curriculum

This covers Introduction to Artificial Intelligence, Introduction to python, Basics of python, Conditional statement & Loop, Function, Numeric Type & String, List & Tuple, Dictionary & Set, Module and Packages, Introduction to Machine Learning, Data prepossessing, Dimensionality Reduction, Artificial Neural Network (ANN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Reinforcement Learning, Natural Language Processing (NLP)

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Course content

  • Introduction to Machine Learning
  • Types of Machine learning
  • Why data science and machine learning is future
  • Different between Data Science Big data and data analysis
  • Data understanding: real life example
  • Why data science and machine learning is future
  • Analytics vs. Data warehousing
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • Process of model creation
  • Which skills are required for Machine learning?
  • Introduction of python
  • History of Python
  • Why python is so famous
  • Installation of Python and Anaconda
  • Compiler & Interpreter
  • Variable, Keywords
  • Comments & Indentation
  • Write your first program
  • Data types, Input and output function
  • Arithmetic operator, Relational Operator
  • Assignment Operator
  • Logical operator, Bitwise operator
  • Membership Operator, Identity Operator
  • If statement, if-else, if-elif-else, Nested if else
  • While loop, For loop
  • Nested while loop, Nested for loop
  • Break, Continue and Pass
  • Basics Defining function
  • Function call Return statement
  • Function with parameter and without parameter
  • local and global variable
  • Recursion, Anonymous (lambda) function
  • User define functions
  • Numeric type basics
  • Hexadecimal, Octal and Binary Notation
  • Complex Numbers, Type casting Numeric Functions
  • Random number generation(Using Random Modules)
  • Defining a string
  • Different ways to create string
  • Escape sequence, Raw string String methods
  • String formatting Expressions
  • Assignments
  • Defining & Creating list, Accessing list elements of list
  • List methods, Functions used with list
  • Implementation of stack and queue using list
  • Matrix & Cube implementation
  • List comprehension: Questions
  • Defining & Creating a tuple
  • Accessing elements of tuple, What is Immutability
  • Tuple Methods, Functions used with tuple
  • Defining & Creating a dictionary
  • Accessing elements of dictionary
  • Dictionary methods
  • Dictionary Comprehension
  • Defining & Creating set
  • Set operations & methods
  • Set comprehension
  • Compares of all python data type
  • Defining module, Importing module
  • Dir(), Module search path, Sys module, Os module
  • Namespace
  • Defining and create package
  • Installing third party packages
  • Assignments
  • Types of Machine Learning Algorithm
  • Different between Machine Learning & Artificial Intelligence
  • How Deep Learning is differ from Machine Learning
  • Linear Regression & Polynomial Regression
  • Logistic Regression
  • Brif idea about Decesion Tree & Random Forest
  • Processing CSV data
  • Correlation
  • Data cleaning techniques
  • Confusion Matrix, ROC & AUC Curve
  • Type-1 and Type-2 Error
  • Precesion & Recall
  • Why we need dimensionality reduction
  • Linear Discriminant Analysis (LDA)
  • Principle component Analysis (PCA)
  • Practical approach in python
  • Appliction of Neural Network
  • Plan of attack
  • Activation function
  • Gradient descent
  • Stochastic Gradient Descent
  • Backpropagation
  • Connectionism
  • Practical approach with python
  • Introduction of Convolution Neural Network
  • How a computer read an image
  • Plan of attack
  • Convolution Operation
  • ReLU layers
  • Pooling layers
  • Flattening
  • Different layers
  • Idea behind Recurrent Neural Network
  •  How To Train Recurrent Neural Networks?
  • Why Not Feedforward Networks?
  • The Vanishing Gradient Problem
  • Issues with Recurrent Neural Networks
  • Long Short Term Memory (LSTM) Algorithm
  • LSTM Use case
  • What is reinforcement learning
  • Bellman equation
  • Markov Decision process
  • Agent environment problem
  • Reinforcement process
  • Reward Maximization
  • Q-learning algorithm
  • Practical approach with python
  • Introduction of NLP
  • Application of Natural Language Processing
  • Regular expression
    • Characters
    • Method and function
    • Sets
    • Example
  • Feature Extraction
  • Text mining
  • NLP component
  • NLTK: Tokanizer, CountVectorizer, Stopwords, NER etc.
  • Practical approach with python
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