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IT: Programming & Software Development
Applied Machine Learning and Data Engineering
Curriculum
1 Section
27 Lessons
30 hours
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Unit 3 — Applied Machine Learning and Data Engineering
27
1.1
Understanding Some Applications
20 mins
1.2
Discovering New Groups with DBScan
20 mins
1.3
Developing a Multivariate Approach
20 mins
1.4
Making Things as Simple as Naïve Bayes
20 mins
1.5
Selecting Variables Like a Pro
20 mins
1.6
Fighting with Big Data Chunk by Chunk
20 mins
1.7
Boosting Predictions
20 mins
1.8
Locating Free Learning Resources with Quora
20 mins
1.9
Working with a Multiple-Data-Source Problem
20 mins
1.10
Understanding Python’s Role in Data Science
20 mins
1.11
Visualizing Power
20 mins
1.12
Installing Anaconda on Mac OS X
20 mins
1.13
Using Hardware Acceleration
20 mins
1.14
Managing Data from Relational Databases
20 mins
1.15
Dealing with Dates in Your Data
20 mins
1.16
Using Labels, Annotations, and Legends
20 mins
1.17
Plotting Geographical Data
20 mins
1.18
Running in Parallel on Multiple Cores
20 mins
1.19
Creating Applied Visualization for EDA
20 mins
1.20
Learning Lazily with Nearest Neighbors
20 mins
1.21
Pumping Up Your Hyperparameters
20 mins
1.22
Understanding Support Vector Machines
20 mins
1.23
Gaining Insights with Oracle’s AI & Data Science Blog
20 mins
1.24
Honing Your Overfit Strategies
20 mins
1.25
Learning to Use Python Fast
20 mins
1.26
Using the Python Ecosystem for Data Science
20 mins
1.27
Downloading the Datasets and Example Code
20 mins
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