Data science AI/Ml basics to learn .pdf
- 1. AI, ML, and Deep Learning: Demystifying the Buzzwords Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning are transforming industries. This presentation defines these concepts and highlights their distinctions. The global AI market is projected to reach $1.59 trillion by 2030. by Khushi Deo
- 2. What is Artificial Intelligence (AI)? Definition AI is the concept of machines mimicking human intelligence. Examples Rule-based systems Natural Language Processing Robotics The term "Artificial Intelligence" was coined in 1956 at the Dartmouth Workshop. Google Translate handles 100+ languages. Tesla Autopilot is also an example of AI.
- 3. Diving into Machine Learning (ML) Definition ML learns from data without explicit programming. Key Idea Algorithms improve their performance with data. Types Supervised, unsupervised, and reinforcement learning. Netflix's recommendation system uses collaborative filtering. Recommendations drive 75% of Netflix user activity. Spam detection can reach 99% accuracy with ML.
- 4. Introduction to Deep Learning (DL) Definition DL uses deep neural networks to analyze data. Inspiration Inspired by the human brain. Core Component Artificial Neural Networks with many layers. Deep Learning is a subset of ML. Deep Learning models can have thousands of layers.
- 5. How Deep Learning Works: Neural Networks Neurons Weights Activation Functions Neurons process information. Weights adjust connection strength. Activation functions introduce non-linearity. Image recognition models can have 50+ layers.
- 6. Key Differences: ML vs. Deep Learning Feature Machine Learning Deep Learning Data Dependency Small datasets Large datasets Feature Extraction Manual extraction Automatic learning Hardware Standard CPUs Powerful GPUs Training Time Faster Slower Complexity Less complex More complex ImageNet contains over 14 million images. ML performs well with smaller datasets, while DL needs large amounts of data.
- 7. Deep Learning Examples: Image Recognition 1 Object Detection Identifying multiple objects. 2 Facial Recognition Identifying individuals. 3 Medical Imaging Detecting diseases. Models have surpassed human performance in image recognition. CNNs are used for image recognition. Performance on COCO dataset exceeds 80%.
- 8. Deep Learning Examples: NLP 1 2 3 4 GPT-3 has 175 billion parameters. RNNs and Transformers are used in NLP. Google Translate utilizes deep learning for language translation. Language Translation Text Summarization Chatbots Content Generation
- 9. Deep Learning Examples: Other Applications 1 Autonomous Vehicles 2 Recommender Systems 3 Fraud Detection Deep learning is used for perception and control in vehicles. It is also used for personalization in recommender systems like Netflix. It helps in identifying fraudulent transactions.
- 10. The Future of AI, ML, and Deep Learning 1.59T Market Size AI market expected to grow by 2030 OPEN Accessibility Democratizing AI through open source ETHICAL Ethics Fairness and transparency in AI AI, ML, and Deep Learning are transforming industries and shaping the future. New algorithms are constantly being developed. It's important to address bias in AI.