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Artificial Intelligence Certification Training Charlotte

  • Kick start your AI career with our Artificial Intelligence training program and gain practical learnings around Deep Learning and Machine Learning, and the clean-coded & effective programming languages.
  • Get equipped with our real-world case studies to qualify the comprehensive AI Certification Charlotte and level up for the market’s demanding job roles.

Learn AI capabilities to handle the leading organization’s AI-driven infrastructure!

Next Class Begins in 18 days - 24 May 2024

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Why AI Certification Charlotte course?

Some intriguing facts to take up AI Training Austin and certification.

Managing mobile interaction

According to Gartner, by this year, smart agents will be managing 40% of mobile interactions.

14% Growth

There is a 14% growth in demand for Artificial Intelligence professionals all across the globe.

Salesforce Growth

You Should Join Our Classes If You Are:

  • Just starting off & aren’t sure where to start from
  • In an established role but need to dive deep
  • Looking to brush up your skills & master the course
  • Willing to get better in your current or new job

Huge source of employment

The University of Texas is the largest public university in the US and is also a major employment source.

6M devices

According to Gartner, in 2018, 6 billion devices approximately proactively asked for support.

Thriving Career Opportunities - What and Where!

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Learn AI capabilities to handle the leading organization’s AI-driven infrastructure!

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IT

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Healthcare & medicine

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Banking &
Finance

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Transportation/Freight

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eCommerce

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Media &
Entertainment

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NPO

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Financial
Services

AI/Machine Learning Engineer

Data Scientist

Business Intelligence Developer

Big Data Engineer/Architect

Some Hilarious & Concealed Facts About Charlotte

  • Charlotte is the largest city in the state of North Carolina.
  • It is the third fastest-growing major city in the United States.
  • Charlotte is home to the corporate headquarters of Bank of America and the east coast operations of Wells Fargo.This makes it the second-largest banking center in the United States.
The median salary in this city is more than the average salary of the U.S. It is an attractive choice for someone who is looking for work that pays well. Everyday 100+ people are moving into this city where there is space and number of jobs available.
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Instructor-led Live Online Artificial Intelligence Classes


Starting
Duration
Price

calendar-icon124 May

WEEKDAY - Filling Fast

6 Weeks

USD 1499

USD 1274

Flat 15% Off

calendar-icon105 Jul

WEEKDAY

6 Weeks

USD 1499

calendar-icon118 May

WEEKEND

8 weeks

USD 1499

calendar-icon116 Aug

WEEKDAY

6 Weeks

USD 1499

calendar-icon127 Sep

WEEKDAY

6 Weeks

USD 1499

USD 1199

Flat 20% Off

Early Bird Discount

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Detail
WEEKDAY - Filling Fast

calendar-icon124 May


6 Weeks

USD 1499

USD 1274

Flat 15% Off

Enroll Now
WEEKDAY

calendar-icon105 Jul


6 Weeks

USD 1499

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Not Sure Which Artificial Intelligence Training Charlotte Class to Join?  

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Artificial Intelligence Certification Training Course Roadmap

Want to know what all you will be learning in our online course on Artificial Intelligence?

AI Intro, Python for AI & ML, SQL

    • What is AI, Why AI?
    • Programming for Problem Solving
    • Artificial intelligence fundamentals
    • Components of AI
    • Python Overview
    • Important Python features
    • Python installation, Anaconda Python distribution
    • Python Functions and Packages
    • Scikit-Learn for Machine Learning & LNP
    • Working with Data Structures,Arrays,
    • Math Operators and Expressions
    • Vectors & Data Frames
    • Pandas, NumPy, Matplotlib
    • Numpy for Statistical Analysis
    • Matplotlib & Seaborn for Data Visualization
    • Descriptive Statistics
    • Probability & Conditional Probability
    • Probability Distributions
    • Working with Databases
    • How to create a Database instance on Cloud
    • CREATE Table Statement
    • SELECT Statement
    • COUNT, DISTINCT, LIMIT
    • INSERT, UPDATE and DELETE Statements
    • Information and Data Models
    • Types of Relationships
    • Sub-Queries and Nested Selects

Machine Learning: Supervised Learning

    • Introduction to Machine Learning
    • Understanding Supervised Learning.
    • Linear Regression Theory
    • Supervised Learning Regression
    • Linear Regression
    • Multiple Linear Regression
    • Bias-Variance Trade-Off
    • Logistic Regression
    • K-Nearest Neighbors (KNN)
    • Simple Vector Machine (SVM)
    • Decision Trees
    • Bagging
    • Boosting, AdaBoost & XGBoost
    • NaÃŊve Bayes Classifier

Machine Learning: Unsupervised Learning

    • K-Means Clustering
    • Hierarchical Clustering
    • Dimensionality Reduction
    • Linear Discriminant Analysis
    • Time Series Modelling
    • Principal Component Analysis (PCA)
    • Reinforcement Learning
    • Model Comparisons
    • Analysis Considerations
    • Clustering Animals
    • Customer Segmentation
    • Optimal Number of Clusters
    • Cluster Based Incentivization
    • Image Segmentation

Deep Learning with Tensorflow, Keras

    • What is Neural Networks
    • Gradient Descent
    • Perceptron & Neural Networks
    • Batch Normalization
    • Activation and Loss functions
    • Hyper parameter tuning
    • Deep Neural Networks
    • Tensor Flow & Keras for Neural Networks & Deep Learning
    • Understand Neural Networks in Detail
    • PyTorch Tensors
    • PyTorch Autograd
    • Illustrate Multi-Layer Perceptron
    • Backpropagation – Learning Algorithm
    • Understand Backpropagation – Using Neural Network Example
    • MLP Digit-Classifier using TensorFlow
    • Building a multi-layered perceptron for classification

Neural Networks, Convolutional Neural Network,Recurrent Neural Networks

    • Artificial neural networks in Deep Learning
    • Linear & Logistic Regression With Tensorflow
    • Activation Functions
    • Illustrate Perceptron
    • Important Parameters of Perceptron
    • Shallow, Deep neural networks
    • Optimization algorithms
    • Hyper-parameter tuning
    • Batch Normalization and Programming Frameworks
    • Single layer NN & Multilayer NN
    • Back propagation, Dynamic Programming
    • Mathematical Take on NN
    • Function Approximator
    • Kernels, Padding & Strides
    • Convolutional Layer, Max-Pooling
    • CNN Architectures – VGGNet, AlexNet, Inception Network
    • Rnns with back propagation
    • Long short-term memory (lstm)
    • Link with Linear Regression
    • Recurrent Neural Network
    • Backpropagation in RNN.
    • Types of RNN, Architecture of RNN

Natural Language Processing

    • Understanding NLP
    • Stop Words, Tokenization
    • Stemming and lemmatization
    • Bag of Words Model
    • Word Vectorizer
    • POS Tagging
    • Named Entity Recognition
    • Text Pre-processing
    • Topic Modelling
    • Noise Removal
    • Lexicon Normalization, Lemmatization, Stemming
    • Object Standardization
    • Text Classification, Text Matching
    • Levenshtein Distance
    • Phonetic Matching, Flexible String Matching

Artificial Intelligence Certification Training Course Roadmap

  • Want to know what all you be learning? Check it out Now!

    • What is AI, Why AI?
    • History of Artificial Intelligence
    • Future and Market Trends in Artificial Intelligence
    • Programming for Problem Solving
    • Artificial intelligence fundamentals
    • Components of AI
    • Computational mathematics for learning and data analysis
    • Machine learning
    • Human Level Performance
    • Parallel and distributed systems: paradigms and models
    • Intelligent Systems for pattern recognition
    • Python Overview
    • History of Python?
    • Important Python features
    • Python-2 and Python-3 differences
    • Install Python and Environment Setup
    • First Python Program
    • Python Identifiers, Keywords, and Indentation
    • Python Functions and Packages
    • Vectors & Data Frames
    • Pandas, NumPy, Matplotlib
    • Numpy for Statistical Analysis
    • Matplotlib & Seaborn for Data Visualization
    • Descriptive Statistics
    • Probability & Conditional Probability
    • Probability Distributions
    • Data Types, Variables and Keywords
    • Control flow
    • Decision Making
    • Branching and Looping
    • String functions
    • List, Tuple, Dictionary
    • Functions
    • Modules
    • File operations
    • Exception Handling
    • List Comprehension
    • Lambda Functions
    • Generators
    • Iterators
    • Concepts of Numpy
    • ndArrays
    • Basic & Matrix Operations
    • Increment and Decrement Operations
    • Indexing, Slicing
    • Iterating Array
    • Conditional Operations
    • Shape & Array Manipulation
    • Universal Functions
    • General Concepts and Broadcasting
    • Loading and saving files
    • Reading and Writing Array Data on Files
    • Assignments & Problem Statements
    • What is Pandas
    • Pandas Series
    • Pandas DataFrame
    • DataFrame Indexing and Loading
    • Querying a DataFrame
    • Indexing Dataframes
    • Missing Values
    • Assignments & Problem Statements
    • Working with Databases
    • How to create a Database instance on Cloud
    • CREATE Table Statement
    • SELECT Statement
    • COUNT, DISTINCT, LIMIT
    • INSERT, UPDATE and DELETE Statements
    • Information and Data Models
    • Types of Relationships
    • Sub-Queries and Nested Selects
    • Introduction to Machine Learning
    • Understanding Supervised Learning.
    • Linear Regression Theory
    • Supervised Learning Regression
    • Linear Regression
    • Multiple Linear Regression
    • Bias-Variance Trade-Off
    • Logistic Regression
    • K-Nearest Neighbors (KNN)
    • Simple Vector Machine (SVM)
    • Decision Trees
    • Bagging
    • Boosting, AdaBoost & XGBoost
    • NaÃŊve Bayes Classifier
    • K-Means Clustering
    • Hierarchical Clustering
    • Dimensionality Reduction
    • Linear Discriminant Analysis
    • Time Series Modelling
    • Principal Component Analysis (PCA)
    • Reinforcement Learning
    • Model Comparisons
    • Analysis Considerations
    • Clustering Animals
    • Customer Segmentation
    • Optimal Number of Clusters
    • Cluster Based Incentivization
    • Image Segmentation
    • What is Neural Networks
    • Gradient Descent
    • Perceptron & Neural Networks
    • Batch Normalization
    • Activation and Loss functions
    • Hyper parameter tuning
    • Deep Neural Networks
    • Tensor Flow & Keras for Neural Networks & Deep Learning
    • Understand Neural Networks in Detail
    • PyTorch Tensors
    • PyTorch Autograd
    • Illustrate Multi-Layer Perceptron
    • Backpropagation – Learning Algorithm
    • Understand Backpropagation – Using Neural Network Example
    • MLP Digit-Classifier using TensorFlow
    • Building a multi-layered perceptron for classification
    • Overview of Neural Network
    • Bias-Variance Trade-off
    • TensorFlow & Keras
    • Multi-layered Perception(MLP)
    • Feed-forward & Backpropagation
    • Activation Functions
    • Optimization techniques
    • Dropout
    • Gradient Descent & Stochastic Gradient Descent
    • Kernels
    • Padding & Strides
    • Convolutional Layer
    • Max-Pooling
    • CNN Architectures – VGGNet, AlexNet, Inception Network
    • Assignments & Problem Statements
    • Project on CNN
    • RNN & LSTM:
    • Recurrent
    • Recurrent Neural Network
    • Backpropagation in RNN.
    • Types of RNN
    • Architecture of RNN
    • LSTM (Long short term memory)
    • Architecture of LSTM
    • Link with Linear Regression
    • Recurrent Neural Network
    • Backpropagation in RNN.
    • Why Sequence Model
    • RNN Model
    • Backpropagation through time
    • Different Type of RNNs
    • GRU
    • LSTM
    • Bidirectional LSTM
    • Deep RNN
    • Word Embedding
    • Understanding NLP
    • Stop Words, Tokenization
    • Stemming and lemmatization
    • Bag of Words Model
    • Word Vectorizer
    • POS Tagging
    • ANN Intuition
    • Plan of Attack
    • Studying the Neuron
    • The Activation Function
    • Working of Neural Networks

Course Curriculum

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