About The Course

The Post Graduate Diploma program in Machine Learning & Artificial Intelligence is an intensive six months job oriented programme. This course is targeted towards engineers and IT professionals or any participant with mathematical background who wish to start their carrier into the domain of Machine Learning & Artificial Intelligence. The course aims to groom the students to enable them to work on current technology scenarios as well as prepare them to keep pace with the changing face of technology and the requirements of the growing IT industry. The course curriculum has been designed keeping in view the emerging trends in Machine Learning & Artificial intelligence well as contemporary and futuristic human resource requirements of the IT industry. The entire course syllabus, course ware, teaching methodology and the course delivery have been derived from the rich research and development background from VAIDEHI SOFTWARE TECHNOLOGIES. The depth of the course is unique in the industry covering a wide spectrum of requirements of the IT industry.

Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving vehicles, speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this course, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical knowledge needed to quickly and powerfully apply these techniques to new problems in the field of engineering & business.

The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision.

Machine learning is closely related to computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to produce reliable, repeatable decisions and results and uncover "hidden insights" through learning from historical relationships and trends in the data.

Ribbon

Learn from industry experts with live instructor-led training

Projects & Lab

Apply the skills you learn to solve real-world problems.

Certificate

Highlight your new skills on your resume or LinkedIn.

1:1 Mentoring

Get guidance from industry leaders and professionals.

Best-in-class Support

24×7 support and forum access to answer all your queries throughout your learning journey.

Certifications

Compatible to Machine Learning & Artifical Intelligence Certifications

Enrollment

ON-LINE MODE INSTRUCTOR LED TRAINING

17 Sept 2018
Online Instructor Based Training
6 Months
50,000/ 714 70,000

CLASS ROOM BASED INSTRUCTOR LED TRAINING

2018-08-23
Mon to Fri (24 weeks)
10 AM - 12 PM
6 Months
80,000/ 1142 1,20,000

2018-09-17
Mon to Fri (24 weeks)
10 AM - 12 PM
6 Months
80,000/ 1142 1,20,000



Course Learning Outcomes

After completion of course students will be able to acquire the following skills

01

To get expertise in Machine Learning & Artificial Intelligence technologies.

02

To master the ability to solve real time engineering & business problems independently using ML & AI.

03

To learn most effective machine learning techniques and gain practice knowledge of implementing them and getting them to work for yourself.

04

To learn about not only the theoretical underpinnings of Machine learning, but also gain the practical knowledge needed to quickly and powerfully apply these techniques to new problems in the field of engineering & business

05

To Learn & Implement popular ML & AI technologies like Data Processing , Regression, SVR, Random Forest Regression, Classification, Clustering, Association Rule Learning, Reinforcement Learning

06

To design & develop a verified portfolio with 2 projects under that will showcase the new skills acquired.

Learning Path


  • MODULE-1 FOUNDATIONS FOR MACHINE LEARNING & AI PG DIPLOMA PROGRAM
    • Data Structures, Object Oriented Programming, Data Manipulation & Data Visualization in Python, Significant Functions, Packages and Routines
    • Visualization principles and techniques
    • Inferential Statistics: Learn Probability Distribution Functions, Random Variables, Sampling Methods, Central Limit Theorem and more to draw inferences
    • Hypothesis Testing: Understand how to formulate and test hypotheses to solve business problems
    • Exploratory Data Analysis: Learn how to summarize data sets and derive initial insights Mathematical
    • Linear Algebra, Matrices, Eigen Vectors and their application for Machine Learning
  • MODULE-2 - MACHINE LEARNING & AI BASIC CONCEPTS:
    • Implementation of linear regression and predict continuous data values
    • Learn and implement algorithms like Naive Bayes and Logistic Regression
    • Learn and understand how to create segments based on similarities using K-Means and Hierarchical clustering
    • Learn and understand how to classify data points using support vectors
    • Understand Tree-based model that is simple and easy to use. Learn the fundamentals on how to implement them in real time.
  • MODULE-3- SUPERVISED LEARNING
    • Regression
    • Classification techniques
    • Decision Trees
  • MODULE-4 - UNSUPERVISED LEARNING
    • Clustering
    • Expectation Maximization
    • Ensemble Techniques – Boosting & Bagging, Random Forests.
  • MODULE-5-REINFORCEMENT LEARNING Basics
    • Understand the basics of RL and its applications in AI
    • Model processes as Markov chains, learn algorithms for solving optimisation problems
    • Write Q-learning algorithms to solve complex RL problems, Value-based methods, Policy-based methods
  • MODULE-6-DEEP LEARNING CONCEPTS & ARTIFICAL NEURAL NETWROKS
    • Understand the components and structure or schema of artificial neural networks
    • Learn the cutting-edge techniques & different algorithms used to train highly complex neural networks
    • Understand how to use CNN's to solve complex image classification problems
    • Learn LSTMs and RNN's applications in text analytic
    • Build and deploy your own neural networks on a website, learn to use the Tensorflow API and Keras
    • Building SOM & Case study
    • Building Boltzmann Machine
    • Building Auto Encoders
  • MODULE-7 - NATURAL LANGUAGE PROCESSING
    • Natural language toolkit, learn the basics of text processing in python
    • Learn & understand how to extract features from unstructured text and build machine learning models on text data
    • Learn how to parse English sentences and extract meaning from them & Conduct sentiment analysis
    • Understand the applications of text analytics in new areas and various business & engineering domains
  • MODULE-8 TENSOR FLOW 1.X
    • Tensor Flow's main data structure – tensors
    • Handling the computing workflow – Tensor Flow's data flow graph
    • Basic tensor methods
    • How Tensor Board works
    • Reading information from a disk
    • Learning from data – unsupervised learning
    • Understanding clustering
    • Mechanics of k-means
    • k-nearest neighbor
    • Univariate linear modeling function
    • Optimizer methods in TensorFlow – the train module
    • Univariate linear regression
    • Multivariate linear regression
    • Logistic function predecessor – the logit functions
    • The logistic function
    • Univariate logistic regression
    • Univariate logistic regression with Keras
    • Origin of convolutional neural networks
    • Applying convolution in TensorFlow
    • Subsampling operation – pooling
    • Improving efficiency – dropout operation
    • Convolutional type layer building methods
    • MNIST digit classification
    • Image classification with the CIFAR10 dataset
    • Optimizing TensorFlow autoencoders
    • Recurrent neural networks
    • A fundamental component – gate operation and its steps
    • TensorFlow LSTM useful classes and methods
    • Time series prediction with energy consumption data
    • TensorFlow – Keras, Pretty Tensor, TFLearn.


Projects

  • ML&AI Applications for the Healthcare Industry

Certificate

Earn your certificate

The certificate rewarded by us is proof that you have taken a big leap in Machine Learning domain.


Our Specialization is exhaustive and the certificate rewarded by us is proof that you have taken a big leap in Machine Learning domain.


Differentiate yourself

The knowledge you have gained from working on projects, videos, quizzes, hands-on assessments and case studies gives you a competitive edge.


Share your achievement

Highlight your new skills on your resume, LinkedIn, Facebook and Twitter. Tell your friends and colleagues about it.



Trainers

  • Created by team of both industry & academic experts having 20+ years of rich R&D experiance


Eligibility Criteria

  • Any Graduate with mathematical background/ Engineering or equivalent (e.g. BE / BTech / 4-year BSc / AMIE, etc.) in Computer Science / IT / Electronics / Electrical / Mechanical / CIVIL / Electronics / Computer Science/ IT / BCA / MCA / MSC / MBA or related areas.
  • Post Graduate in Engineering Sciences (e.g. MSc in Computer Science, IT, Electronics, etc
  • Graduate in any Discipline of Engineering or equivalent Sciences (e.g. MSc in Computer Science, IT, Electronics, etc
  • MCA/MCM
  • Post Graduate in Physics/ Computational Sciences/ Mathematics or allied areas.
  • Post Graduate in Management with graduation degree in Science/ IT/ Computers
  • The candidates must have secured a minimum of 50% marks in their qualifying examination.


Course Fee Structure



ONLINE TRAINING FEE for PG Diploma courses

Price : Rs 50,000/-( Including Tax) / 714

Duration : 6 Months Mon - Fri 1 Hr

CLASS ROOM TRAINING FEE for PG Diploma courses

Price : 80,000/-( Including Tax) / 1142

Duration : 6 Months Mon - Fri 1 Hr


Financial Aid

Financial Aid

Selected students can contact the Admissions Office for assistance in applying for loans after receiving the offer of admission. Our education loan lending partners include HDFC, Axis Bank, Tata Capital, Capital First and many more.


Placement Assistance


Up on successful completion of PGDP course & the participants who are very serious about their carrier & who clear the IT company standard certification exam @ our campus we are offering 100% placement assistance with our very strong placement team. Vaidehi Software, will use its strong HR corporate network to help candidates in the program make the transition to career to IT industry. For all qualifying candidates the Placement assistance will be extended till they get placed even after post completion of program.

Note :-

  • Only candidates who pass the respective IT standard certification exam will be eligible for outsourcing for client location or for placement assistance.
  • Placement is strictly depends up on the candidate dedication, efforts, commitment, performance in the internal tests, skills.
  • Vaidehi Software strives hard to place its students by conducting rigorous placement activities like mock interviews, soft skills from day one of the course.


Reviews

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FAQ


  • 1. What is the difference between online training and class room learning?

    In Online training, you will get

    • Access to live instructor-led training as per your enrolled batch
    • Learn from industry experts over online meeting tools like zoom
    • 24x7 support by the trainers.

    In Class room training, you will get

    • Intensive class room 1 to 1 training by the real time experts as per your enrolled batch
    • Learn from industry experts having rich 20+ years of experience in R&D.
    • 24x7 support by the trainers.

  • 2. What are the prerequisites and requirements for this course?

    No prerequisites

  • 3. Who will be the course instructors?

    Top industry experts with rich 20+ years of R&D experience in mentoring students across the world.

  • 4. What is the validity of course material?

    Soft copy of the course material will be mailed to you.

  • 5. How does online instructor-led training work?

    In online instructor-led training, team of experts will train you with a group of our course learners for 25+ hours over online conferencing software like Zoom & Webminar. Online Classes will happen every day from Monday to Friday.

  • 6. What is the certification process?

    At the end, of course, you will work on a real-time project. Once you are done with the project (it will be reviewed by an expert), you will be awarded a certificate which you can share on LinkedIn.

  • 7. How will be the practical or hands-on be conducted?

    Enrollment into course entails 30 days of free access to labs depending on date of enrollment. Can be extended based on permission.

  • 8. Can I renew my lab subscription?

    Yes, you can renew your subscription anytime. Please choose your desired plan for the lab and make payment to renew your subscription

  • 9. For instant help whom to contact directly?

    Mail our most dynamic & ever active director through email director@vaidehisoftware.com