Module I: Intro to Data Science (4 classes)

Dataset Loading Utilities

  General dataset API

  Toy datasets

  Sample images

  Sample generators

  Datasets in svmlight / libsvm format

  The Olivetti faces dataset

  The 20 newsgroups text dataset

  Downloading datasets from the mldata.org repository

NumPy Introduction

  The NumPy array object

  Numerical operations on arrays

  More elaborate arrays

  Advanced operations

  Some exercises

Matplotlib Introduction

  Simple plot

  Figures, Subplots, Axes and Ticks

  Other Types of Plots: examples and exercises

  Beyond this tutorial

  Quick references

  Full code examples

Preprocessing Data

  Standardization, or mean removal and variance scaling

  Normalization

  Binarization

  Encoding categorical features

  Imputation of missing values

  Generating polynomial features

  Custom transformers

Dataset transformations

  Pipeline and Feature Union: combining estimators

  Feature extraction

  Preprocessing data

  Unsupervised dimensionality reduction

  Random Projection

  Kernel Approximation

  Pairwise metrics, Affinities and Kernels

  Transforming the prediction target (y)

 

 Module II: Predictive Modeling (4 classes)

 Pandas: Statistical Modeling

  Data representation and interaction

  Hypothesis testing: comparing two groups

  Linear models, multiple factors, and analysis of variance

  Multiple Regression: including multiple factors

  Post-hoc hypothesis testing: analysis of variance (ANOVA)

  More visualization: seaborn for statistical exploration

  Testing for interactions

  Full code examples

Machine Learning - Intro To

  Loading an example dataset

  Classification

  Clustering: grouping observations together

  Dimension Reduction with Principal Component Analysis

  Putting it all together: face recognition

  Linear model: from regression to sparsity

  Model selection: choosing estimators and their parameters

Model selection:

  Choosing estimators and their parameters

 Score, and cross-validated scores

  Cross-validation generators

  Grid-search and cross-validated estimators

Moving Your Model to Production

  Finalize Your Machine Learning Model

  Make Predictions On New Data

  Create A Standalone Model

  Save and Load Your Model

A. Hands On Lab

Capstone Project

Includes Motivation, Dataset Selection thru to Project Presentation, Peer Review & 1 on 1 with your Mentor 

In addition to the lectures and hands-on sessions , you will be guided with the selection of a business problem to solve, guidance and support in applying the lessons to your project, mentoring your approach to solve the challenges, 1-On-1 follow up with your Mentor, and deployment of your solution on Anova’s proprietary platform where you can showcase to your employers or colleagues

Python Boot Camp Program Details

  • Pre-Requisites +

    • The Python boot camp assumes you already have at least an intermediate understanding of Python and sta tisti cs.   
    • Coding –For the basic boot camp (the first 4 weeks), you need to have basic programming skills and have used Python at a basic level, perhaps in an entry level

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  • What You'll Learn +

       

    Module I:  Intro to Data Science   (4 classes)

    Dataset Loading Utilities

      General dataset API

      Toy datasets

      Sample images

      Sample generators

      Datasets in svmlight / libsvm format

      The Olivetti faces dataset

      The 20 newsgroups text dataset

      Downloading datasets from the mldata.org

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  • Course Logistics +

    Dates:  Every Saturday starting 9th of September 2017 (Excluding Holidays)

      - 2 Modules of 4 Saturday sessions of each, and a Capstone Project, 1 on 1 with Mentors

      - Each session is broken into 3-4 hours of topical concepts and presentations, followed by 1-2 hours of lab sessions. Financing may

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  • Other +

    INCLUDES : ( In addition to enhancing your skills & knowledge with real world data product experience )

    ♦  Access to a virtual machine  ready to use with Python, JupyterHub, all the data set files and all the packages you will need for the boot camp

    ♦ A generous  list of resources and instruc

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