Course Details
Course Outline
1 - Getting Started
Installing a Python Data Science EnvironmentUsing and understanding IPython (Jupyter) NotebooksPython basics - Part 1Understanding Python codeImporting modulesPython basics - Part 2Running Python scripts
2 - Statistics and Probability Refresher, and Python Practice
Types of dataMean, median, and modeUsing mean, median, and mode in PythonStandard deviation and varianceProbability density function and probability mass functionTypes of data distributionsPercentiles and moments
3 - Matplotlib and Advanced Probability Concepts
A crash course in MatplotlibCovariance and correlationConditional probabilityBayes' theore
4 - Algorithm Overview
Data PrepLinear AlgorithmsNon-Linear AlgorithmsEnsembles
5 - Predictive Models
Linear regressionPolynomial regressionMultivariate regression and predicting car pricesMulti-level models
6 - Applied Machine Learning with Python
Machine learning and train/testUsing train/test to prevent overfitting of a polynomial regressionBayesian methods - ConceptsImplementing a spam classifier with Naïve BayesK-Means clustering
7 - Recommender Systems
What are recommender systems?Item-based collaborative filteringHow item-based collaborative filtering works?Finding movie similaritiesImproving the results of movie similaritiesMaking movie recommendations to peopleImproving the recommendation results
8 - More Applied Machine Learning Techniques
K-nearest neighbors - conceptsUsing KNN to predict a rating for a movieDimensionality reduction and principal component analysisA PCA example with the Iris datasetData warehousing overviewReinforcement learning
9 - Dealing with Data in the Real World
Bias/variance trade-offK-fold cross-validation to avoid overfittingData cleaning and normalizationCleaning web log dataNormalizing numerical dataDetecting outliers
10 - Apache Spark - Machine Learning on Big Data
Installing SparkSpark introductionSpark and Resilient Distributed Datasets (RDD)Introducing MLlibDecision Trees in Spark with MLlibK-Means Clustering in SparkTF-IDFSearching Wikipedia with Spark MLlibUsing the Spark 2.0 DataFrame API for MLlib
11 - Testing and Experimental Design
A/B testing conceptsT-test and p-valueMeasuring t-statistics and p-values using PythonDetermining how long to run an experiment forA/B test gotchas
12 - GUIs and REST
Build a UI for your ModelsBuild a REST API for your Models
13 - What the Future Holds
Actual course outline may vary depending on offering center. Contact your sales representative for more information.
Who is it For?
Target Audience
Students attending this class should have a grounding in Enterprise computing. Students attending this course should be familiar with Enterprise IT, have a general (high-level) understanding of systems architecture, as well as some knowledge of the business drivers that might be able to take advantage of applying AI.