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agenda.txt
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agenda.txt
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Spread 10 hours of training over the main topics:
0. Basics #
1. In and Export #
2. Watson APIs #
3. Visualization #
4. Spark #
5. Machine Learning #
6. Training - Deep Learning #
Per topic, mark your top 3 interest as 1,2,3:
0. Basics\0. Basics 0. Jupyter notebook #
0. Basics\0. Basics 1. Python #
0. Basics\0. Basics 2. Numpy #
0. Basics\0. Basics 3. Pandas #
0. Basics\0. Basics 4. Jupyter notebook magics, shell and R #
1. In and Export\1. import and export 0. Object Storage #
1. In and Export\1. import and export 1. Download and upload #
1. In and Export\1. import and export 2. DashDB #
1. In and Export\1. import and export 3. Cloudant #
1. In and Export\1. import and export 4. Twitter #
1. In and Export\1. import and export 5. BigInsights #
2. Watson APIs\2. Watson 0. Weather API #
2. Watson APIs\2. Watson 1. Personality Insights #
2. Watson APIs\2. Watson 2. Alchemy News #
2. Watson APIs\2. Watson 3. Alchemy language #
2. Watson APIs\2. Watson 4. Tone analyzer #
2. Watson APIs\2. Watson 5. Natural language classifier #
3. Visualization\3. Visualization 0. Matplotlib #
3. Visualization\3. Visualization 1. Machine learning techniques #
3. Visualization\3. Visualization 2. Pixiedust #
3. Visualization\3. Visualization 3. Bokeh #
4. Spark\4. Spark 0. rdd-creation #
4. Spark\4. Spark 1. rdd-basics #
4. Spark\4. Spark 2. rdd-sampling #
4. Spark\4. Spark 3. rdd-set #
4. Spark\4. Spark 4. rdd-aggregations #
4. Spark\4. Spark 5. rdd-key-value #
4. Spark\4. Spark 6. mllib-statistics #
4. Spark\4. Spark 7. mllib-logit #
4. Spark\4. Spark 8. mllib-trees #
4. Spark\4. Spark 9. sql-dataframes #
5. Machine Learning\5. ML 0. Install requirements #
5. Machine Learning\5. ML 1. Introduction #
5. Machine Learning\5. ML 2. Data preparation #
5. Machine Learning\5. ML 3. Scikit Learn interface #
5. Machine Learning\5. ML 4. Bias and variance #
5. Machine Learning\5. ML 5. Model evaluation #
5. Machine Learning\5. ML 6. Ensemble methods #
5. Machine Learning\5. ML 7. Ensemble methods advanced #
5. Machine Learning\5. ML 8. Multi Model Ensembles #
5. Machine Learning\5. ML 9. Time series #
6. Deep Learning\6. DL 0. Keras starter kit #
6. Deep Learning\6. DL 1. Fun with activation functions #
6. Deep Learning\6. DL 2. Convolutional networks #
6. Deep Learning\6. DL 3. Embedding #
6. Deep Learning\6. DL 4. Multi-input models #
6. Deep Learning\6. DL 5. Auto encoder #
6. Deep Learning\6. DL 6. Recurrent networks #