Artificial Intelligence vs Machine Learning vs Deep Learning | Difference between AI, ML and DL

In this video, Vishwanath Jha, Founder, CEO of Saarthi.ai talks about Artificial Intelligence, Machine Learning & Deep Learning, the differences between AI, ML and DL. He'll give a couple of examples to demonstrate the differences between AI, ML and DL and also bust a few myths & fiction around these buzzwords.
#AI #ML #DL
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About the course

Do you want to learn essential Artificial Intelligence concepts like search, optimization, planning, and pattern recognition from top AI experts? AI and Deep learning have shown promising growth in recent years and in the near future can change the way companies operate.
After completing the Deep Learning and Artificial Intelligence online course, you’ll be able to:

- Use Tensorflow, Scikit Learn library, Keras and other machine learning and deep learning tools.
- Work on algorithms in real world problems using Image and Speech Recognition
- Develop Chatbots and work on complex data forms.
- Start career as an AI Developer, AI Engineer or a Data Scientist

Program Structure
Introduction to AI
-Sneak Peek into the future
-Applications of AI
-Enterprise use - Automotive, Manufacturing, Healthcare, Retail
-Consumer use -Games, Home Automation
-Introduction to Machine learning and Deep Learning
Machine Learning
- Types of Machine learning
- Popular ML algorithms - Linear Regression, SVM, Decision Trees etc.
- Training and Deploying models.
- ML With SKLearn using Python
Programming with TensorFlow
- Introduction to Tensorflow
- Programming structure in Tensorflow
- Regression and classification with Tensorflow

Introduction to Neural Networks
- Perceptron and Deep Neural Networks
- Training Neural networks with Tensorflow
- Types of NN- CNN, RNN, Feedforward, GAN.
- Common Tensorflow API's - KERAS, Estimator, Layers
- Introduction to Reinforcement Learning

Image recognition
- Introduction to Image processing and Computer vision.
- Convolutional Neural Networks (CNN)
- Object Detection in Images
- Object Detection in Video

Speech Recognition
- Text Analytics
- Natural Language Processing
- Recurrent Neural Networks- RNN
- Time Series Analysis with RNN
- Variations of RNN- LSTM and GRU
- Building and Training an RNN for Speech Recognition

Project
- Chatbot
- Healthcare - Diagnostics with X Ray Data & Others across different domains
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