Keras & TensorFlow Training
Training Program on NLP
Training Program on Computer Vision & Deep Learning
Machine Learning using Python
Keras & TensorFlow Training
Learn Nasscom Certified Artificial Intelligence Course Online – Enroll Now.
This training is for advance studies of Neural Network library of Keras integrating with many Libraries to perform the machine learning with Tensor Flow which provides high level framework and low level framework in various tasks of APIS for building and training. Students can learn the concept of backend along with using Keras. When you are creating a model in Keras, you are actually still creating a model using Tensor flow, Keras just makes it easier to code. Both provide high-level APIs used for easily building and training models, but Keras is more user-friendly because it’s built-in Python. This Deep Learning training with Tensor Flow certification training was created by industry experts and follows the most up- to-date best practices. You’ll learn how to apply deep learning algorithms and master deep learning concepts and models using the Keras and Tensor Flow frameworks, training you for a career as a Deep Learning Engineer.
Key Takeaways
Programming Logic.
Overview of TensorFlow and Keras.
Behavioral to human Stimulation
AI/ML concept
Advance level of python programing Language
Python ML Libraries
Prediction of Data
Machine Learning Past and Pre.
Overview of API and how its forms.
Real Time based project
Lifetime Access with the Trainer
Skills Covered
AI-ML Overview
Theoretical concept to code.
Strong Level of Data Structure and Algorithm
Advance of Python Language
Machine Learning Libraries
TensorFlow with Backend
Low level and High level Framework in Keras Api
Any Backend Programing Algorithm
Training Program on NLP
This training is for advanced studies and part of Artificial Intelligence. Students can learn the interaction between computers and humans using the natural language. The main objectives of this particular course we offer is NLP or Natural Language processing is to read, decipher, understand, and make sense of the human languages in a manner that is valuable, in particular how to program computers to process and analyze large amounts of natural language data. With Combination of other Courses we offers in machine learning algorithms, NLP combines the real projects that create systems that learn to perform tasks on their own and get better through experience. You’ll be able to develop NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even construct chatbots by the end of this Specialization.
Key Takeaways
Concept of Natural Language Processing
Strong Algorithmic Concept.
Advance of Artificial Intelligence
Overview Concept of ML
Behavioral to human Stimulation
Advance level of python programing Language
Text Decipher
Python ML Libraries for AI/ML
Processing of Computer Code to Next Level.
Overview of Deep Learning
Real Time based project
Lifetime Access with the Trainer
Skills Covered
AI-ML Overview
Theoretical concept to code.
Strong Level of Data Structure and Algorithm
Python Libraries for AI/ML
Concept of Deep learning
Sentiment Analysis using LSTM
RNN with PyTorch
Machine Translation
Conference Resolution
Discourse Analysis
Speech recognition
Bidirectional RNN
Training Program on Computer Vision & Deep Learning
We’ll go over image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and image stylization, editing, and new image creation, among other topics.
Key Takeaways
Introduction to Computer Vision
Sampling Data and Convolution Neural Network
What is Computer Vision and Filters
Where To use Image Processing
What are Pixels?
Convolution and Correlation
Case Study
CNN
Pooling and Padding
CNN Architecture
Residual Neural Network
Real Time based project
Lifetime Access with the Trainer
Skills Covered
Image segmentation.
Object detection.
Classification of images.
Tracking moving objects over time.
Face detection and recognition.
Optical character recognition.
Image generation.
Machine Learning using Python
You’ll learn about supervised vs. unsupervised learning, model validation, and Machine Learning algorithms in general. In this course, you’ll get hands-on experience with real-world Machine Learning examples and learn how it impacts society in unexpected ways.
Key Takeaways
Basics of Machine Learning
What is Machine Learning
Types of Machine Learning
Reinforcement Learning
Supervised VS Unsupervised
Multiple Linear Regression
Logistic Regression
What is Logistic Regression
What is Linear Regression
What is Decision Tree
What is Random Forest
What is KNN
What is Support Vector Machine
What is Naive Bayes
Real Time-based project
Lifetime Access with the Trainer
Skills Covered
Understanding data structures
Data modeling
Quantitative analysis methods
Building out data pipelines
Statistics