Introduction to Machine Learning

• Machine Learning is a way to use some set of algorithms to derive predictive analytics from data. It is different than Business Intelligence and Data Analytics in a sense that In BI and Data analytics Businesses make decision based on historical data, but In case of Machine Learning , Businesses predict the future based on the historical data. Example, It’s a difference between what happened to the business vs what will happen to the business.Its like making BI much smarter and scalable so that it can predict future rather than just showing the state of the business.
• **ML is based on Standard algorithms which are used to create use case specific model based on the data **. For example we can build the model to predict delivery time of the food, or we can build the model to predict the Delinquency rate in Finance business , but to build these model algorithm might be similar but the training would be different.Model training requires tones of examples (data).
• Basically you train your standard algorithm with your Input data. So algorithms are always same but trained models are different based on use cases. Your trained model will be as good as your data.

ML, AI , Deep learning ? What is the difference?

ML is type of AI

AI is a discipline , Machine Learning is tool set to achieve AI. DL is type of ML when data is unstructured like image, speech , video etc.

Barrier to Entry Has Fallen

AI & ML was daunting and with high barrier to entry until cloud become more robust and natural AI platform. Entry barrier to AI & ML has fallen significantly due to

• Increasing availability in data (big data).
• Increase in sophistication in algorithm.
• And availability of hardware and software due to cloud computing.

GCP Machine Learning Spectrum

• For Data scientist and ML experts , TensorFlow on AI platform is more natural choice since they will build their own custom ML models.
• But for the users who are not experts will potentially use Cloud AutoML or Pre-trained ready to go model.
• In case of AutoML we can trained our custom model with Google taking care of much of the operational tasks.
• Pre-trained models are the one which are already trained with tones of data and ready to be used by users to predict on their test data.

Prebuilt ML Models (No ML Expertise Needed)

• As discuss earlier , GCP has lot of Prebuilt models that are ready to use to solve common ML task . Such as image classification, Sentiment analysis.
• Most of the businesses are having many unstructured data sources such as e-mail, logs, web pages, ppt, documents, chat, comments etc.( 90% or more as per various studies)
• Now to process these unstructured data in the form of text, we should use Cloud Natural Language API.
• Similarly For common ML problems in the form of speech, video, vision we should use respective Prebuilt models.

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