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Building a machine learning model isn’t just about finding the right algorithm—it’s about the right process. In Machine Learning in Production, Andrew Ng breaks down the iterative loop of model development: training, error analysis, refining hyperparameters, and improving data. Getting to a high test set accuracy is one thing,... show more
45,248 views • 1 year ago •via X (Twitter)
5 Comments

DataInsta1 year ago
exactly! it’s that feedback loop that turns models into masterpieces!

Rainmaker2 years ago
Which Machine Learning model delivers stronger trading results? Check out this free Substack post where I compare several powerful models that beat the market and show yearly returns of over 20%.

Lester Smartfield1 year ago
Technical stuff indeed! But, Andrew Ng always makes it sound feasible, right? Looking forward to diving in!

QuantumQuinn Kierra1 year ago
Models seldom salute business bosses. Sounds like a data-driven adventure awaits!

Zephyr Cristo1 year ago
Absolutely, the process is key in ML. Ng's iterative approach highlights how continuous refinement drives model performance, much like how entrepreneurs must iterate on their business models to find success.
