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Complete Data Science Training with Python for Data Analysis, Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & More
Created by Minerva Singh
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What Will I Learn?
- Install Anaconda & Work Within The iPytjhon/Jupyter Environment, A Powerful Framework For Data Science Analysis
- Become Proficient In Using The Most Common Python Data Science Packages Including Numpy, Pandas, Scikit & Matplotlib
- Be Able To Read In Data From Different Sources (Including Webpage Data) & Clean The Data
- Carry Out Data Exploratory & Pre-processing Tasks Such As Tabulation, Pivoting & Data Summarizing In Python
- Become Proficient In Working With Real Life Data Collected From Different Sources
- Carry Out Data Visualization & Understand Which Techniques To Apply When
- Carry Out The Most Common Statistical Data Analysis Techniques In Python Including T-Tests & Linear Regression
- Understand The Difference Between Machine Learning & Statistical Data Analysis
- Implement Different Unsupervised Learning Techniques On Real Life Data
- Implement Supervised Learning (Both In The Form Of Classification & Regression) Techniques On Real Data
- Evaluate The Accuracy & Generality Of Machine Learning Models
- Build Basic Neural Networks & Deep Learning Algorithms
- Use The Powerful H2o Framework For Implementing Deep Neural Networks
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