💡Machine learning turns data into numbers and finds patterns.
📚Deep learning is a subset of machine learning, focused on unstructured data.
⭐Traditional machine learning algorithms, like XGBoost, are good for structured data.
🔥Deep learning algorithms, like neural networks, are good for unstructured data.
😎Deep learning models can adapt to changing environments and large datasets.
Which algorithms are recommended for structured data?
—Random Forest, Gradient Boosted Models, Native Bayes, K-Nearest Neighbor, Support Vector Machine, etc.
Which algorithms are recommended for unstructured data?
—Deep learning algorithms like neural networks.
What are the prerequisites for this course?
—Basic Python coding skills and familiarity with machine learning concepts.
Can I learn PyTorch without prior machine learning experience?
—Yes, this course is designed for beginners and covers the foundations of machine learning using PyTorch.
Is deep learning better than traditional machine learning?
—It depends on the problem and the data. Deep learning is more powerful for unstructured data, while traditional machine learning is better for structured data.
00:00This course covers the foundations of machine learning and deep learning using PyTorch.
07:45Machine learning turns data into numbers and finds patterns.
13:30Deep learning is a subset of machine learning, focused on unstructured data.
19:21Traditional machine learning algorithms are good for structured data.
19:55Deep learning algorithms are good for unstructured data.
12:48Deep learning models are adaptable to changing environments and large datasets.
18:38Neural networks are used for unstructured data.
21:11Deep learning models have multiple layers for processing data.
