Adds Python Support

- Adds PyObject overload to `ScheduleManager.TrainingNow` and `ScheduleManager.Training`
- Adds `QCAlgorithm.Train` helper method
- Adds Python algorithm showing how to use the helper method.

Algorithm.Python/TrainingExampleAlgorithm.py

@@ -0,0 +1,52 @@
+# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from clr import AddReference
+AddReference("System")
+AddReference("QuantConnect.Algorithm")
+AddReference("QuantConnect.Common")
+
+from System import *
+from QuantConnect import *
+from QuantConnect.Algorithm import *
+from time import sleep
+
+### <summary>
+### Example algorithm showing how to use QCAlgorithm.Train method
+### </summary>
+### <meta name="tag" content="using quantconnect" />
+### <meta name="tag" content="training" />
+class TrainingExampleAlgorithm(QCAlgorithm):
+    '''Example algorithm showing how to use QCAlgorithm.Train method'''
+
+    def Initialize(self):
+
+        self.SetStartDate(2013, 10, 7)
+        self.SetEndDate(2013, 10, 14)
+
+        self.AddEquity("SPY", Resolution.Daily)
+
+        # Set TrainingMethod to be executed immediately
+        self.Train(self.TrainingMethod)
+
+        # Set TrainingMethod to be executed at 8:00 am every Sunday
+        self.Train(self.DateRules.Every(DayOfWeek.Sunday), self.TimeRules.At(8 , 0), self.TrainingMethod)
+
+    def TrainingMethod(self):
+
+        self.Log(f'Start training at {self.Time}')
+        # Use the historical data to train the machine learning model
+        history = self.History(["SPY"], 200, Resolution.Daily)
+
+        # ML code:
+        pass