Mastering AI – An Advanced Course on Artificial Intelligence

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About Course

The Mastering AI course is designed for those who want to take their AI skills to the next level. This course covers the latest advancements in AI, including Neural Networks, Computer Vision, Natural Language Processing, Reinforcement Learning, and Generative Adversarial Networks (GANs). With real-world examples and hands-on experience with TensorFlow and Keras, you’ll develop a deep understanding of the most advanced AI algorithms and architectures. This course prepares you for high-demand AI job roles and equips you with the skills to develop AI applications that have a real impact on the world.

What Will You Learn?

  • MODULE 1
  • 1. Introduction to Neural Networks and Deep Learning
  • a. Introduction to Perceptron & Neural Networks
  • b. Activation and Loss functions
  • c. Gradient Descent
  • d. Batch Normalization
  • e. TensorFlow & Keras for Neural Networks
  • f. Hyper Parameter Tuning
  • MODULE 2
  • 2. Computer Vision
  • a. Introduction to Convolutional Neural Networks
  • b. Introduction to Images
  • c. Convolution, Pooling, Padding & its Mechanisms
  • d. Forward Propagation & Backpropagation for CNNs
  • e. CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
  • f. Transfer Learning
  • g. Object Detection
  • h. YOLO, R-CNN, SSD
  • i. Semantic Segmentation
  • j. U-Net
  • k. Face Recognition using Siamese Networks
  • l. Instance Segmentation
  • MODULE 3
  • 3. NLP (Natural Language Processing)
  • a. Introduction to NLP
  • b. Stop Words
  • c. Tokenization
  • d. Stemming and Lemmatization
  • e. Bag of Words Model
  • f. Word Vectorizer
  • g. TF-IDF
  • h. POS Tagging
  • i. Named Entity Recognition
  • j. Introduction to Sequential data
  • k. RNNs and its Mechanisms
  • l. Vanishing & Exploding gradients in RNNs
  • m. LSTMs - Long short-term memory
  • n. GRUs - Gated Recurrent Unit
  • o. LSTMs Applications
  • p. Time Series Analysis
  • q. LSTMs with Attention Mechanism
  • r. Neural Machine Translation
  • s. Advanced Language Models: Transformers, BERT, XLNet
  • Module 4
  • 4. Introduction to Reinforcement Learning (RL)
  • a. RL Framework
  • b. Component of RL Framework
  • c. Examples of RL Systems
  • d. Types of RL Systems
  • e. Q-learning
  • MODULE 5
  • 5. Introduction to GANs (Generative Adversarial Networks)
  • a. Introduction to GANs
  • b. Generative Networks
  • c. Adversarial Networks
  • d. How do GANs work?
  • e. DCGANs - Deep Convolution GANs
  • f. Applications of GANs

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