PG Diploma in Artificial Intelligence and Machine Learning

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About the Course

Artificial intelligence will impact all segments of daily life by 2025 with huge implications for a range of industries such as transport and logistics, healthcare, home maintenance and customer service. Our main objective is to train the Fresh Engineering Graduates in this domain and place them in the above mentioned sectors.

Course duration

4 Months

Eligibility

Engineering graduates from ECE, E & I , Mechatronics, E & E, Computer Science Mechanical and Automobile Engineering

Course Content

In this module, you will be introduced to AI and you will also learn about Artificial are using it. In this session, we'll also introduce you to the program, help you discover the Intelligence workflow. We will also discuss on the market for enterprise AI systems and how industries services we provide we provide over the course of the journey, and show you all the incredible projects that you'll build.

In this module, you will be introduced to MATLAB and SIMULINK. You will be introduced to MATLAB technical computing environment. This Module is divided into two sections

In the first section, MATLAB Programming basics, MATLAB Programming basics, Numerical Computation, Data Analysis & visualization, Working with Functions, Concepts of data visualization, Data analysis, Visualization, Programming, Importing data, Processing data, Customizing visualizations, working with irregular data, relational-and-logical-functions,loop-statements-vectorising-codes,stringmanipulation, data-structures.

In the Second section, you will be introduced to Simulink and Model based design using Simulink, Introduction to mathematical & physical modelling, Overview of Simulink block library, Introduction to solvers, Creating and modifying Simulink models and simulating system dynamics, Modeling continuous-time, discrete-time, and hybrid systems, Modifying solver settings for simulation accuracy and speed Building hierarchy into a Simulink model, Creating reusable model components using subsystems, libraries, and model references.

In this module, you will be introduced to the concepts of Image and Computer Vision. You will be demonstrated with exercises and case studies associated to Basic and advanced topics associated to Computer Vision. Topics like Importing and exporting images, Removing noise , Aligning images and creating a panoramic scene, Detecting lines and circles in an image, Segmenting objects, Measuring and modifying object shape properties , Performing batch analysis over sets of images, Importing, displaying, and annotating images and videos, Detecting, extracting, and matching object features, Automatically aligning images using geometric transformations, Detecting objects in images and videos, Tracking objects and estimating their motion in a video, Removing lens distortion from images , Measuring planar objects. You’ll use a combination of cameras, software to perform these operations

In this module, you will be introduced to the concepts and latest trend in Internet of Things. You will demonstrated with exercises and case studies associated to IOT and its application. We will cover topics like Introduction to IoT, Hardware architecture of Microcontroller and ARM processor, developing real time IOT applications using Microcontroller and ARM, and deploying the data to cloud for data analysis. The topics in this module will be cover with hands on sessions on like arduino microcontroller, Raspberry pi, Android Mobile Phones and various interfacing sensors so on. The real time data collected from this hardware will be loaded to cloud and corresponding data will be visualized.

The module will demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results. Topics include: Importing and organizing data, Finding natural patterns in data, Building predictive models, Evaluating and improving the model, Introduction to Machine Learning for Computer Vision Applications Hands-on exercises: Image preprocessing Train and compare classifiers using Classification Learner App Feature extraction and machine learning for image recognition. Why use MATLAB for Machine Learning, Data types that we may Encounter, Importing data into MATLAB, Understanding the table data type, K-Nearest Neighbor- Building a model with subset of classes, missing values and instances weights, K-Nearest Neighbor- Dealing with scaling issue and copying a learned model, K-Nearest Neighbor- Learning KNN model with features subset and with non numeric data, K-Nearest Neighbor- Nearest Neighbor Intuition, K-Nearest Neighbor- Properties of KNN (K-Nearest Neighbor ), KNearest Neighbor- Types of Properties, Naive Bayes in MATLAB, Intuition of Naive Bayesian Classification, Naive Bayes- building a model with categorical data, Naive Bayes- a final note on Naive Bayesian Model, Decision Trees in MATLAB, Properties of the Decision Trees, Intuition of Decision Trees, Node Related Properties of Decision Trees, Properties at the Classifier Built Time, Discriminant Analysis in MATLAB, Intuition of Discriminant Analysis, Properties of the Discriminant Analysis Learned Model in MATLAB, Support Vector Machines (SVM) in MATLAB, Properties of Support Vector Machines (SVM) Learned Model in MATLAB, Intuition of Support Vector Machines (SVM) Classification

Deep Learning overview, CNN Architecture Modifying Network Layers, Setting Training Options, Training the Network, Hands on: Real time Image classification using Deep Learning. Hands on: Real time face detection, Object Classification and Image and Video Capturing. learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Classifying Images with Convolutional Networks, Get an overview of the course. Perform image classification using pretrained networks. Use transfer learning to train customized classification networks, Interpreting Network Behavior, Gain insight into how a network is operating by visualizing image data as it passes through the network. Apply this technique to different kinds of images, Extracting and Visualizing Activations, Representing Signal Data as Images, Feature Extraction for Machine Learning, Creating Networks, Build Convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.Training from Scratch, Landcover Classification, Creating Network Architectures, Understanding Neural Networks Convolutional Layers, Creating Networks, Training Networks Understand how training algorithms work. Set training options to monitor and control training, Understanding Network Training, Monitoring Training Progress, Training Networks, Improving Performance, Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance, Training Options, Augmented Datastores, Directed Acyclic Graphs, Performing Regression, Create convolutional networks that can predict continuous numeric responses.Concepts of Regression, Transfer Learning for Regression, Evaluating a Regression Network, Performing Regression, Detecting Objects in Images, Train networks to locate and label specific objects within images, Classification vs Object Detection, Ground Truth, Regions with Convolutional Neural Networks, Classifying Sequence Data with Recurrent Networks, Build and train networks to perform classification on ordered sequences of data, such as time series or sensor data,Long Short-Term Memory Networks, Classify Musical Instruments, Structuring Sequence Data, Sequence Classification, Improving LSTM Performance, Review - Classifying Sequence Data with Recurrent Networks, Classifying Categorical Sequences, Use recurrent networks to classify sequences of categorical data, such as text, Author Identification, Categorical Sequences, Classify Text Data, Classifying Categorical Sequences, Generating Sequences of Output, Use recurrent networks to create sequences of predictions, Sequence-to-Sequence Classification, Investigate Sequence Scores Sequence Forecasting, Review - Generating Sequences of Output

Neural network & fuzzy logic design, Introduction to Neural network & Fuzzy logic toolbox, Classification of cancer cells using neural network, Solving the tipping problem using fuzzy inference system, Hands on: Wine classification using neural network, Deployment of FIS in Simulink.

Control System Design Overview, Model Representation, System Identification, Parameter Estimation, System Analysis, Linearization, PID control in Simulink, Classic Control Theory, Response Estimation, Controller Implementation, Hardware Implementation, Preparing Simulink models for code generation, Generating code and testbench for a compatible Simulink model, Performing speed and area optimizations, Verifying generated HDL code using testbench and cosimulation, Prepare MATLAB code for GPU code generation Generate, test, and deploy the generated CUDA code on NVIDIA GPUs, Optimize code for performance.