Predictive Analytics with TensorFlow 1.2: A Basic Introduction to Predictive Analytics

The video introduces predictive analytics and its application in business intelligence, emphasizing the importance of machine learning. It explains the principles of predictive models, including convex optimization and loss functions. The stages of machine learning, such as learning and inference, are discussed, highlighting feature engineering and data preprocessing. The video also covers linear algebra concepts and programming tools like Scipy and NumPy for implementing these concepts.

What is the definition of machine learning according to Tom Mitchell?

What are the typical tasks involved in machine learning?

Explain the process of predictive analytics.

How does the learning stage differ from the inference stage in machine learning?

Describe the importance of feature engineering in predictive modeling.

What is the curse of dimensionality in machine learning?

What are some programming options available for linear algebra in Python?