Powerful AI for Real-World applications can be created by combining the strengths of data science, machine learning.
10 Modules in Total.
1. Build An AI
2. Make a virtual self driving car
3. solve real world problems with AI
4. Q-learning
5. Deep Convolutional Q- Learning
6. Understand the theory behind artificial intelligence
7. Make an AI to beat games
8. Master the State of the ART AI models
9. Deep Q-Learning
10. A3C
-In-depth understanding of Machine Learning: Learners will acquire a deep understanding of machine learning concepts, such as supervised and unsupervised learning, decision trees, random forests, support vector machines, k-means clustering, and principal component analysis. They will also learn about the different types of machine learning, including supervised, unsupervised, semi-supervised and reinforcement learning.
-Hands-on experience with Neural Networks and Deep Learning: Learners will gain hands-on experience with neural networks and deep learning, including feed forward neural networks, back propagation, convolutional neural networks, and recurrent neural networks. They will also learn about popular deep learning frameworks such as TensorFlow, Keras, and PyTorch.
-Natural Language Processing and Computer Vision: Learners will gain an understanding of natural language processing and computer vision concepts, including tokenization, stemming, lemmatization, part-of-speech tagging, sentiment analysis, and named entity recognition. They will also learn about computer vision techniques such as edge detection, feature extraction, and object detection.
-Expertise in AI Applications: Learners will gain expertise in AI applications, including speech recognition, computer vision, natural language processing, and robotics. They will learn about the latest developments in these areas and how to apply them to real-world problems.
-Understanding of the ethical and societal implications of Artificial Intelligence: Learners will gain an understanding of the ethical and societal implications of AI, including issues related to privacy, security, and bias. They will also learn about the impact of AI on society and the economy, and the potential for AI to be used for both positive and negative purposes.
-Skills in building and deploying AI systems: Learners will learn the practical skills of building and deploying AI systems including data pre-processing, model training, evaluation, and deployment. They will also learn about the different tools and technologies used in building and deploying AI systems.
-Familiarity with the state-of-the-art AI models and techniques: Learners will gain knowledge of the current state-of-the-art AI models and techniques such as deep reinforcement learning, generative models, transfer learning, and meta-learning. They will also learn about the latest research and developments in the field of AI and be able to apply these techniques to their own projects.
