Loading...

Artificial Intelligence

Course Summary

In this artificial intelligence online course, trainees gain an in-depth understanding of the tools, techniques & frameworks used to develop AI algorithms using Machine Learning & Deep Learning. Master the principles of Supervised & Unsupervised Learning, Semi-Supervised & Reinforcement learning, Artificial Neural Networks, clustering, CNN and more with our AI course.

With hands-on training using Matplotlib & backpropagation techniques, this training will help you gain practical working knowledge of the architecture of CNNs and RNNs as well as data manipulation/visualization using Python.

You will also learn all about regression models, logistic regression, MLNN programming using Python, pooling layers & Keras.    

Upon completion of artificial intelligence training, you will have the skills required to build intelligent AI computer systems.


Course Objectives

  • At the end of our artificial intelligence training, you will be able to:
  • Use your understanding of the concepts of Artificial Intelligence and Machine Learning to develop algorithms
  • Setup Python Environment
  • Perform data manipulation with Pandas
  • Perform data visualization using Matplotlib
  • Apply the principles of Linear Regression, Logistic Regression and Artificial Neural Networks in your projects
  • Program for K Means using Python
  • Work with Deep Networks
  • Use Numpy, Matplotlib, Pandas, Theano, Scikit-learn, Opencv, TensorFlow and Keras

Course Outline

  • Introduction to Artificial Intelligence
    • Introduction to Artificial Intelligence
    • Applications, Industries, and growth
    • Techniques used for AI
    • AI for everything
    • Different methods used for AI
    • Tradition Methods & New Methods
    • AI Agents
  • Python: Environment Setup and Essentials
    • Introduction to Anaconda
    • Installation of Anaconda Python Distribution : For Windows, Mac OS, and Linux
    • Jupyter Notebook Installation
    • Jupyter Notebook Introduction
    • Variable Assignment
    • Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
    • Creating, accessing, and slicing tuples
    • Creating, accessing, and slicing lists
    • Creating, viewing, accessing, and modifying dicts
    • Creating and using operations on sets
    • Basic Operators: 'in', '+', '*'
    • Functions
    • Control Flow
  • Mathematical Computing with Python (NumPy)
    • NumPy Overview
    • Properties, Purpose, and Types of ndarray
    • Class and Attributes of ndarray Object
    • Basic Operations: Concept and Examples
    • Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
    • Copy and Views
    • Universal Functions (ufunc)
    • Shape Manipulation
    • Broadcasting
    • Linear Algebra
  • Data Manipulation with Python (Pandas)
    • Introduction to Pandas
    • Data Structures
    • Series
    • DataFrame
    • Missing Values
    • Data Operations
    • Data Standardization
    • Pandas File Read and Write Support
    • Data Acquisition (Import & Export)
    • Selection, Filtering, Combining and Merging Data Frames, Normalization method
    • Removing Duplicates & String Manipulatio
  • Data Visualization in Python using Matplotlib
    • Introduction to Data Visualization
    • Python Libraries
    • Plots
    • Matplotlib Features
    • Line Properties Plot with (x, y)
    • Controlling Line Patterns and Colors
    • Set Axis, Labels, and Legend Properties
    • Alpha and Annotation
    • Multiple Plots
    • Subplots, Seabo
  • Linear Regression
    • Regression Problem Analysis
    • Mathematical modeling of Regression Model
    • Gradient Descent Algorithm
    • Programming Process Flow
    • Use cases
    • Programming Using python
    • Building simple Univariate Linear Regression Model
    • Multivariate Regression Model
    • Boston Housing Prizes Prediction
    • Cancer Detection Predictive Analysis
    • Best Fit Line and Linear Regressio
  • Logistic Regression
    • Problem Analysis
    • Cost Function Formation
    • Mathematical Modelling
    • Use Cases
    • Digit Recognition using Logistic Regressio
  • Artificial Neural Networks
    • Neurons, ANN & Working
    • Single Layer Perceptron Model
    • Multilayer Neural Network
    • Feed Forward Neural Network
    • Cost Function Formation
    • Applying Gradient Descent Algorithm
    • Backpropagation Algorithm & Mathematical Modelling
    • Programming Flow for backpropagation algorithm
    • Use Cases of ANN
    • Programming SLNN using Python
    • Programming MLNN using Python
    • Digit Recognition using MLNN
    • XOR Logic using MLNN & Backpropagation
    • Diabetes Data Predictive Analysis using ANN
  • Clustering
    • Hierarchical Clustering
    • K Means Clustering
    • Use Cases for K Means Clustering
    • Programming for K Means using Python
    • Image Color Quantization using K Means Clustering Technique
  • Principle Component Analysis
    • Dimensionality Reduction, Data Compression
    • Concept and Mathematical modeling
    • Use Cases
    • Programming using Python
    • IRIS Data Analysis using PCA
  • Deep Dive into Neural Networks
    • Understand limitations of A Single Perceptron
    • Understand Neural Networks in Detail
    • Backpropagation : Learning Algorithm
    • Understand Backpropagation : Using Neural Network Example
  • Master Deep Networks
    • Why Deep Learning?
    • SONAR Dataset Classification
    • What is Deep Learning?
    • Feature Extraction
    • Working of a Deep Network
    • Training using Backpropagation
    • Variants of Gradient Descent
    • Types of Deep Networks
  • Convolutional Neural Networks (CNN)
    • Introduction to CNNs
    • CNNs Application
    • Architecture of a CNN
    • Convolution and Pooling layers in a CNN
    • Understanding and Visualizing a CNN
    • Transfer Learning and Fine-tuning Convolutional Neural Networks
    • Image classification using Keras deep learning library
  • Recurrent Neural Networks (RNN)
    • Intro to RNN Model
    • Application use cases of RNN
    • Modelling sequences
    • Training RNNs with Backpropagation
    • Long Short-Term memory (LSTM)
    • Recursive Neural Tensor Network Theory
    • Recurrent Neural Network Model
    • NLP Example using Keras library
    • Time-Series Analysis
  • Python Libraries
    • Numpy
    • Matplotlib
    • Pandas
    • Theano
    • Scikit-learn
    • Opencv
    • TensorFlow
    • Keras

Who Needs This Course

  • Anyone who wants to add Artificial Intelligence skills to their profile
  • Teams getting started on Artificial Intelligence projects

Course Objectives

  • At the end of our artificial intelligence training, you will be able to:
  • Use your understanding of the concepts of Artificial Intelligence and Machine Learning to develop algorithms
  • Setup Python Environment
  • Perform data manipulation with Pandas
  • Perform data visualization using Matplotlib
  • Apply the principles of Linear Regression, Logistic Regression and Artificial Neural Networks in your projects
  • Program for K Means using Python
  • Work with Deep Networks
  • Use Numpy, Matplotlib, Pandas, Theano, Scikit-learn, Opencv, TensorFlow and Keras

Course Outline

  • Introduction to Artificial Intelligence
    • Introduction to Artificial Intelligence
    • Applications, Industries, and growth
    • Techniques used for AI
    • AI for everything
    • Different methods used for AI
    • Tradition Methods & New Methods
    • AI Agents
  • Python: Environment Setup and Essentials
    • Introduction to Anaconda
    • Installation of Anaconda Python Distribution : For Windows, Mac OS, and Linux
    • Jupyter Notebook Installation
    • Jupyter Notebook Introduction
    • Variable Assignment
    • Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
    • Creating, accessing, and slicing tuples
    • Creating, accessing, and slicing lists
    • Creating, viewing, accessing, and modifying dicts
    • Creating and using operations on sets
    • Basic Operators: 'in', '+', '*'
    • Functions
    • Control Flow
  • Mathematical Computing with Python (NumPy)
    • NumPy Overview
    • Properties, Purpose, and Types of ndarray
    • Class and Attributes of ndarray Object
    • Basic Operations: Concept and Examples
    • Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
    • Copy and Views
    • Universal Functions (ufunc)
    • Shape Manipulation
    • Broadcasting
    • Linear Algebra
  • Data Manipulation with Python (Pandas)
    • Introduction to Pandas
    • Data Structures
    • Series
    • DataFrame
    • Missing Values
    • Data Operations
    • Data Standardization
    • Pandas File Read and Write Support
    • Data Acquisition (Import & Export)
    • Selection, Filtering, Combining and Merging Data Frames, Normalization method
    • Removing Duplicates & String Manipulatio
  • Data Visualization in Python using Matplotlib
    • Introduction to Data Visualization
    • Python Libraries
    • Plots
    • Matplotlib Features
    • Line Properties Plot with (x, y)
    • Controlling Line Patterns and Colors
    • Set Axis, Labels, and Legend Properties
    • Alpha and Annotation
    • Multiple Plots
    • Subplots, Seabo
  • Linear Regression
    • Regression Problem Analysis
    • Mathematical modeling of Regression Model
    • Gradient Descent Algorithm
    • Programming Process Flow
    • Use cases
    • Programming Using python
    • Building simple Univariate Linear Regression Model
    • Multivariate Regression Model
    • Boston Housing Prizes Prediction
    • Cancer Detection Predictive Analysis
    • Best Fit Line and Linear Regressio
  • Logistic Regression
    • Problem Analysis
    • Cost Function Formation
    • Mathematical Modelling
    • Use Cases
    • Digit Recognition using Logistic Regressio
  • Artificial Neural Networks
    • Neurons, ANN & Working
    • Single Layer Perceptron Model
    • Multilayer Neural Network
    • Feed Forward Neural Network
    • Cost Function Formation
    • Applying Gradient Descent Algorithm
    • Backpropagation Algorithm & Mathematical Modelling
    • Programming Flow for backpropagation algorithm
    • Use Cases of ANN
    • Programming SLNN using Python
    • Programming MLNN using Python
    • Digit Recognition using MLNN
    • XOR Logic using MLNN & Backpropagation
    • Diabetes Data Predictive Analysis using ANN
  • Clustering
    • Hierarchical Clustering
    • K Means Clustering
    • Use Cases for K Means Clustering
    • Programming for K Means using Python
    • Image Color Quantization using K Means Clustering Technique
  • Principle Component Analysis
    • Dimensionality Reduction, Data Compression
    • Concept and Mathematical modeling
    • Use Cases
    • Programming using Python
    • IRIS Data Analysis using PCA
  • Deep Dive into Neural Networks
    • Understand limitations of A Single Perceptron
    • Understand Neural Networks in Detail
    • Backpropagation : Learning Algorithm
    • Understand Backpropagation : Using Neural Network Example
  • Master Deep Networks
    • Why Deep Learning?
    • SONAR Dataset Classification
    • What is Deep Learning?
    • Feature Extraction
    • Working of a Deep Network
    • Training using Backpropagation
    • Variants of Gradient Descent
    • Types of Deep Networks
  • Convolutional Neural Networks (CNN)
    • Introduction to CNNs
    • CNNs Application
    • Architecture of a CNN
    • Convolution and Pooling layers in a CNN
    • Understanding and Visualizing a CNN
    • Transfer Learning and Fine-tuning Convolutional Neural Networks
    • Image classification using Keras deep learning library
  • Recurrent Neural Networks (RNN)
    • Intro to RNN Model
    • Application use cases of RNN
    • Modelling sequences
    • Training RNNs with Backpropagation
    • Long Short-Term memory (LSTM)
    • Recursive Neural Tensor Network Theory
    • Recurrent Neural Network Model
    • NLP Example using Keras library
    • Time-Series Analysis
  • Python Libraries
    • Numpy
    • Matplotlib
    • Pandas
    • Theano
    • Scikit-learn
    • Opencv
    • TensorFlow
    • Keras

Who Needs This Course

  • Anyone who wants to add Artificial Intelligence skills to their profile
  • Teams getting started on Artificial Intelligence projects

Contact Us

Suite #101, AL-Tawhidi 1 Building, Next to ADCB Bank, Bank Street, Khaled Bin Waleed Road, Bur Dubai - Dubai. U.A.E. P.O.Box: 94743 Dubai, UAE.

metro learnerspoint

Direction from Metro: Al Fahidi Metro, Exit 1 elevator - walk approx 1 minute towards Burjuman.

Follow Us  

News & Events

LearnersPoint is proud to announce that they have been associated with...

Read More

Join now the advance level courses and get trained with our industry e...

Read More

Do you have desire to work, study or migrate to English Speaking Count...

Read More

Locate Us

Map online payment

Make secure payment here