Artificial Intelligence & Machine Learning
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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
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
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
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
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