English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 50m | 404 MB
Become an efficient data science practitioner by understanding Python’s key concepts
This course will make you look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. We start with SVM and random forest for classification and regression. We look at big data, deep learning, and language processing. Then we use graph analysis techniques for very interesting and trending social media analytics. Finally, we take a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment tools that make it easier to present your results to an audience of both data science experts and business users.
The course is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
What You Will Learn
- Set up an experimental pipeline to test your data science hypotheses
- Choose the most effective and scalable learning algorithm for your data science tasks
- Optimize your machine learning models to get the best performance
- Explore and cluster graphs, taking advantage of interconnections and links in your data
Table of Contents
Advanced Machine Learning
1 The Course Overview
2 Support Vector Machine
3 Ensemble Strategies
4 Dealing with Big Data
5 Approaching Deep Learning
6 A Peek at Natural Language Processing (NLP)
Social Network Analysis
7 Introduction to Graph Theory
8 Graph Algorithms
9 Graph Loading, Dumping, and Sampling
Visualization, Insights, and Results
10 Introducing the Basics of Matplotlib
11 Wrapping Up Matplotlib’s Commands
12 Interactive Visualizations with Bokeh
13 Advanced Data-learning Representations
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