Artificial Intelligence using Machine Learning & Deep Learning Training

Live Online & Classroom Enterprise Training

Master the core concepts of Artificial Intelligence using Machine Learning & Deep Learning in our artificial intelligence course. Learn the principles behind neural networks using frameworks like TensorFlow, Keras, Scikit-learn, RNN, Backpropagation, Semi-Supervised & Reinforcement Learning.

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Key Features
  • Lifetime Access

  • CloudLabs

  • 24x7 Support

  • Real-time code analysis and feedback

  • 100% Money Back Guarantee

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What is Artificial Intelligence Course about?

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.


What are the objectives of Artificial Intelligence Course ?

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

Available Training Modes

Live Online Training

18 Hours

Classroom Training


3 Days

Who is Artificial Intelligence Course for?

  • Anyone who wants to add Artificial Intelligence skills to their profile
  • Teams getting started on Artificial Intelligence projects
  • What are the prerequisites for Artificial Intelligence Course?

    • Basic knowledge of statistics and mathematics

    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 is the instructor for this training?

    The trainer for this Artificial Intelligence using Machine Learning & Deep Learning Training has extensive experience in this domain, including years of experience training & mentoring professionals.


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