Deep Learning Foundations Career Pathway

Description

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

A form of artificial intelligence, machine learning is revolutionizing the world of computing as well as all people’s digital interactions and experiences. By making it possible to quickly, cheaply and automatically process and analyze huge volumes of complex data. Innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars are just a few applications out of many.

A course which is a subset of Machine Learning, Deep Learning enables to create an artificial Neural Network which can make Adequate and Intelligent Decision on it’s own. This course will introduce you the deep learning methodologies using Tensor flow which is believed to be the best software library to implement deep learning.

The course curriculum includes everything which is imperative to impart you the knowledge of Deep Learning, it includes introduction to TensorFlow, Artificial Neural Networks etc with video tutorials from the experts and mocks to put your skills to test. This course provides everything you are looking to elevate your skills.
We will provide you project based internship, job assistance the best in industry and certificates from MSME.


  • Training

    2 months Training, 6 months access, Industry-Oriented, Self-Paced.

  • Certifications

    Small & basic Objective MCQ type online exams. Microsoft Technology Associate & Foxmula Certification.

  • Internship

    45 days Internship Completion letter post project submission on our GitLab. Projects are Industrial, Small and based on your training.


How It Works

Curriculum

  • Perform data operations using Data Types and Operators
  • Control Flow with Decision and Loops
  • Perform Input and Output Operations
  • Document and Structure Code
  • Perform Troubleshooting and Error Handling
  • Perform Operations using Modules and Tools
  • Data Pre processing: Missing Data, Categorical Data & Feature Scaling
  • Regression I: Linear Regression, Multiple Regression, Polynomial Regression
  • Regression II: Logistic Regression, K-Nearest Neighbors
  • Support Vector Machines (SVM) & Kernel SVM
  • Clustering: K-Means and Hierarchical
  • Natural Language Processing (NLP)
  • Enterprise Application of Machine Learning
  • Introduction to TensorFlow
  • Artificial Neural Networks
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks with LSTM (Long Short Term Memory)
  • Restricted Boltzmann Machines
  • Auto-encoders
  • Usage of Deep Learning in Real World Applications