Change docs theme to pydata-sphinx theme (#4985)

This PR:

- [x] Switches the python docs theme to `pydata-sphinx-theme` to be uniform with rest of the `rapids` python docs.
- [x] Fixes typos
- [x] Fixes a number of formatting and alignment issues in docstrings.
- [x] Discuss with cuml team on how(if any) and where things should be placed.

Authors:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)
  - AJ Schmidt (https://github.com/ajschmidt8)

URL: #4985

.gitignore

@@ -35,6 +35,8 @@ _skbuild/
 
 ## files pickled in notebook when ran during python docstring generation
 docs/source/*.model
+docs/source/*.pkl
+docs/source/*.tl
 
 ## eclipse
 .project

cpp/include/cuml/manifold/tsne.h

@@ -85,7 +85,7 @@ struct TSNEParams {
   // The momentum used after the exaggeration phase.
   float post_momentum = 0.8;
 
-  // Set this to -1 for pure random intializations or >= 0 for
+  // Set this to -1 for pure random initializations or >= 0 for
   // reproducible outputs. This sets random seed correctly, but there
   // may still be some variance due to the parallel nature of this algorithm.
   long long random_state = -1;

docs/source/api.rst

@@ -1,6 +1,6 @@
-~~~~~~~~~~~~~~~~~~~
-cuML API Reference
-~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~
+API Reference
+~~~~~~~~~~~~~
 
 .. role:: py(code)
    :language: python
@@ -187,7 +187,7 @@ Array Wrappers (Internal API)
     :members:
 
 Metrics (regression, classification, and distance)
----------------------------------------------------
+--------------------------------------------------
 
   .. automodule:: cuml.metrics.regression
     :members:
@@ -239,7 +239,7 @@ Metrics (clustering and manifold learning)
     :members:
 
 Benchmarking
--------------
+------------
 
   .. automodule:: cuml.benchmark.algorithms
     :members:
@@ -309,7 +309,7 @@ Multiclass Classification
     :members:
 
 Naive Bayes
-----------------------
+-----------
 
 .. autoclass:: cuml.naive_bayes.MultinomialNB
     :members:
@@ -399,13 +399,13 @@ Clustering
 ==========
 
 K-Means Clustering
---------------------
+------------------
 
 .. autoclass:: cuml.KMeans
     :members:
 
 DBSCAN
--------
+------
 
 .. autoclass:: cuml.DBSCAN
     :members:
@@ -446,7 +446,7 @@ Truncated SVD
     :members:
 
 UMAP
--------------
+----
 
 .. autoclass:: cuml.UMAP
     :members:
@@ -464,7 +464,7 @@ Random Projections
 
 
 TSNE
--------------
+----
 
 .. autoclass:: cuml.TSNE
     :members:
@@ -485,22 +485,22 @@ Nearest Neighbors Classification
     :members:
 
 Nearest Neighbors Regression
---------------------------------
+----------------------------
 
 .. autoclass:: cuml.neighbors.KNeighborsRegressor
     :members:
 
 Kernel Density Estimation
---------------------------------
+-------------------------
 
 .. autoclass:: cuml.neighbors.KernelDensity
     :members:
 
 Time Series
-============
+===========
 
 HoltWinters
--------------
+-----------
 
 .. autoclass:: cuml.ExponentialSmoothing
     :members:
@@ -534,13 +534,13 @@ Multi-Node, Multi-GPU Algorithms
 ================================
 
 DBSCAN Clustering
---------------------
+-----------------
 
 .. autoclass:: cuml.dask.cluster.DBSCAN
     :members:
 
 K-Means Clustering
---------------------
+------------------
 
 .. autoclass:: cuml.dask.cluster.KMeans
     :members:
@@ -559,7 +559,7 @@ Nearest Neighbors
 
 
 Principal Component Analysis
------------------------------
+----------------------------
 .. autoclass:: cuml.dask.decomposition.PCA
     :members:
 
@@ -573,7 +573,7 @@ Random Forest
     :members:
 
 Truncated SVD
---------------
+-------------
 
 .. autoclass:: cuml.dask.decomposition.TruncatedSVD
     :members: