English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 15m | 460 MB
Become an efficient data science practitioner by understanding Python’s key concepts
The Python Data Science Essentials video series takes you through all you need to know to succeed in data science using Python. Get insights into the core of Python data, including the latest versions of Jupyter Notebook, NumPy, Pandas and scikit-learn. In this course, you will delve into building your essential Python 3.6 data science toolbox, using a single-source approach that will allow to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and prepare for machine learning and visualization techniques.
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 your data science toolbox using a Python scientific environment
- Get data ready for your data science project
- Manipulate, fix, and explore data in order to solve data science problems
- 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
Table of Contents
The Course Overview
Introducing Data Science and Python
Getting Ready
A Glance at the Essential Packages
Introducing the Jupyter Notebook
Scikit-learn Toy Datasets
Data Loading and Preprocessing
Working with Categorical and Text Data
Creating NumPy Arrays
NumPy’s Fast Operations and Computations
Introducing EDA
Building New Features
Dimensionality Reduction
The Detection and Treatment of Outliers
Validation Metrics
Testing and Validating
Cross-Validation
Hyperparameter Optimization
Feature Selection
Wrapping Everything in a Pipeline
Preparing Tools and Datasets
Linear and Logistic Regression
Naive Bayes
K-Nearest Neighbors
An Overview of Unsupervised Learning
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