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AI Online Training

Artificial Intelligence Training

overview

ITCourCes.com Artificial Intelligence online training help you master the concepts of AI such as TensorFlow, backpropagation, convolutional neural networks (CNN) and perceptron in CNN, graph visualization, Deep Learning libraries, recurrent neural networks (RNN), hyperparameters and TFLearn APIs. Enroll this Artificial Intelligence course to become a successful AI Engineer.

What are the Artificial Intelligence Training course objectives?

At the end of the Artificial Intelligence online training, you will be able to:

  • Understand concepts of TensorFlow.
  • Work with operations, functions and the execution pipeline
  • Know the concept of machine learning in Al
  • Implement deep learning algorithms in TensorFlow and interpret results.
  • Learn the mathematical aspects required for ML.
  • Analyze and utilize data using neural networks
  • Implementing AI techniques for problem-solving.

Why should you learn an Artificial Intelligence course?

  • AI is the future.
  • According to Paysa the demand for certified AI Engineers is increasing exponentially
  • A certified AI engineer earn an average salary of $172,000 per year.

Who should attend Artificial Intelligence Training? 

The following job roles benefit from taking this course:

  • Data scientists
  • Robotics engineers
  • Python developers
  • Business analysts
  • Final year students who are willing to build their career in the field of AI.

What are the prerequisites for learning Artificial Intelligence Training?

The following are the prerequisite to learn AI course: 

Basic knowledge of:

  • Statistics
  • Working experience in Python programming is required to learn Artificial Intelligence online training.

 

Curriculum

  • Why do we need Python?
  • Program structure

Execution steps

  • Interactive Shell
  • Executable or script files

User Interface or IDE

Memory management and Garbage collections

  • Object creation and deletion
  • Object properties

Data Types and Operations

  • Numbers
  • Strings
  • List
  • Tuple
  • Dictionary
  • Other Core Types

Statements and Syntax

  • Assignments, Expressions, and prints
  • If tests and Syntax Rules
  • While and For Loops
  • Iterations and Comprehensions

File Operations

  • Opening a file
  • Using Files
  • Find and replace

Functions

  • Function definition and call
  • Function Scope
  • Arguments
  • Function Objects
  • Anonymous Functions

Exception Handling

  • Default Exception Handler
  • Catching Exceptions
  • Raise an exception
  • User-defined exception

Advanced Concepts

Pandas Section

  • Python Pandas – Introduction
  • Introduction to Data Structures
  • Python Pandas – Series
  • Python Pandas – Data Frame
  • Python Pandas – Basic Functionality
  • Python Pandas – Descriptive Statistics
  • Python Pandas – Indexing and Selecting Data
  • Python Pandas – Function Application
  • Python Pandas – Reindexing
  • Python Pandas – Iteration
  • Python Pandas – Sorting
  • Python Pandas – Working with Text Data
  • Python Pandas – Options and Customization
  • Python Pandas – Missing Data
  • Python Pandas – GroupBy
  • Python Pandas – Merging/Joining
  • Python Pandas – Concatenation
  • Python Pandas – IO Tools
  • Python Pandas – Comparison with SQL

Python Pandas – Dates Conversion

Data Science and AI

All the topics in data science will covered with following concept:

  • Mathematics beside of each model
  • Which scenario we want to use particular algorithm
  • How to apply it in tool
  • Inferential thing of each model
  • Difference between each model
    • Introduction to Machine Learning & Predictive Modelling
    • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning

Statistics

  • Standard Deviation
  • Variance
  • Concept of hypothesis testing
  • T-test
  • Chi-square
  • Anova
  • Correlation
  • Probability
  • Outliers
  • Drop highly correlated features

Machine Learning

Supervised Learning – Classification

  • Support vector machines classifier
  • Naïve Bayes
  • Nearest Neighbour
  • Logistic Classification
  • Decision tree Classifier
  • Random forest classifier

Supervised learning -Regression

  • Linear Regression
  • Multiple Regression
  • Polynomial Regression
  • Exponential Regression
  • Decision tree Regression
  • Random forest Regression
  • Feature selection using regression

Unsupervised Learning -Clustering

  • K means clustering
  • Hierarchical clustering

Ensemble method Techniques

Introduction to Natural Language Processing

Pre-processing Text

  • Get Synonyms from words
  • Bag of Words
  • Remove Punctuation
  • Remove Stop Words
  • Replace Characters
  • Stemming Words
  • Lemmatization with Python
  • Gender finder
  • Strip Whitespace
  • Tokenize Text
  • speech tagging
  • Sentence Segmentation

Convert Text to Speech in PythonDeep Learning

A subset of ML, Deep Learning (DL) is re-branding of neural networks- a class of models inspired by biological neurons in our brain.

Overview of the neural network, ANN

 

1 Review

Rajendra P
5

It was my first online training, it was awesome and the trainer provided me was a professional and had a very good grip on the course. I learned a lot from this training and looking forward to work on this platform. Thanks to itcources to provides me the platform, especially the trainer who helped me all the way. Thanks.

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