Content gap: Documentation for the Built-in algorithms is incomplete

[mchoi8739]

What did you find confusing? Please describe.

I had a look at our current docs and found gaps in the Algorithm documentations between the AWS dev guide and readthedocs.io.

The AWS dev guide lists all algos that are officially released (left column below), but some of them are not documented in readthedocs.io (right column below).

AWS dev guide	pySDK in First-Party Algorithms
BlazingText	(missing)
DeepAR Forecasting	(missing)
Factorization Machines	FactorizationMachines
Image Classification Algorithm	(missing)
IP Insights	IP Insights
K-Means Algorithm	K-means
K-Nearest Neighbors (k-NN) Algorithm	K-Nearest Neighbors
Latent Dirichlet Allocation (LDA)	LDA
Linear learner algorithm	LinearLearner
Neural Topic Model (NTM) Algorithm	NTM
Object2Vec	Object2Vec
Object Detection Algorithm	(missing)
Principal Component Analysis (PCA) Algorithm	PCA
Random Cut Forest (RCF) Algorithm	Random Cut Forest
Semantic Segmentation	(missing)
Sequence to Sequence (seq2seq)	(missing)
XGBoost	XGBoost (is under the Framework node)

In case of XGBoost, I suggest to list it with the other algos and provide a link to the exisiting XGBoost in the Framework node with a short explanation of why XGBoost is under Frameworks.

Describe how documentation can be improved

• Add the 6 missing algorithms to the readthedoc.io 1p algorithm section.
• Add XGBoost to the 1p algorithm section as well, and link to the existing XGBoost documentation under the Frameworks node.

[IvyBazan]

List of Built-in algorithms in pySDK documentation includes all algorithms in the AWS dev guide