Artificial Intelligence & Machine Learning with Deep Learning Training Logo

Artificial Intelligence & Machine Learning with Deep Learning Training

Self-Paced, Live Online & Classroom Enterprise Training

Learn the fundamental concepts of Artificial Intelligence using Machine Learning & get introduced to advanced topics of 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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Prof. Dr. James G. Shanahan
Program Architect

Prof. Dr. James G. Shanahan

Dr. James has spent the past 25 years developing and researching cutting-edge artificial intelligent systems. He has (co)founded several companies, advises high-tech startups and held appointments at AT&T (Executive Director of Research), Turn Inc., Xerox Research, Mitsubishi Research, and at Clairvoyance Corp. He teaches at UC Berkeley and has published six books, more than 50 research publications, and has over 20 patents in the areas of machine learning and information processing.

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

Who is Artificial Intelligence Course for?

  • Developers
  • Managers
  • Tech Leads
  • Team Getting started on AI& ML Projects

What are the prerequisites for Artificial Intelligence Course?


  • Basic knowledge of statistics and mathematics

Available Training Modes

Live Online Training

18 Hours

Classroom Training

3 Days

Course Outline Expand All

Expand All

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Problem Analysis
  • Cost Function Formation
  • Mathematical Modelling
  • Use Cases
  • Digit Recognition using Logistic Regressio
  • 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
  • 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
  • Dimensionality Reduction, Data Compression
  • Concept and Mathematical modeling
  • Use Cases
  • Programming using Python
  • IRIS Data Analysis using PCA
  • Understand limitations of A Single Perceptron
  • Understand Neural Networks in Detail
  • Backpropagation : Learning Algorithm
  • Understand Backpropagation : Using Neural Network Example
  • 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
  • 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
  • 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
  • Numpy
  • Matplotlib
  • Pandas
  • Theano
  • Scikit-learn
  • Opencv
  • TensorFlow
  • Keras

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