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Learn Machine Learning 101 Class Bootcamp Course NYC
Rating: 4.1 out of 5(576 ratings)
19,619 students

Learn Machine Learning 101 Class Bootcamp Course NYC

Machine Learning 101 Class Bootcamp Course Intro to AI
Created byShivgan Joshi, Prakash Chandr Nandanwar, abhi verma
Last updated 2/2019
English

What you'll learn

  • Learn Terms used in Machine Learning in Python 312 285 6886
  • Learn the Basics of Model building without math or programming knowledge
  • Entry point to Data Science, Machine Learning Career in NYC New York

Course content

2 sections • 19 lectures • 1h 23m total length

  • Introduction3:11
  • Books used - Reference books0:01
  • Reference Books For Machine Learning3:52

    https://github.com/amueller/introduction_to_ml_with_python

    https://github.com/amueller/ml-training-intro

    https://github.com/dipanjanS/practical-machine-learning-with-python/tree/master/notebooks (more about terms)

    https://github.com/rasbt/python-machine-learning-book

    https://github.com/dipanjanS/practical-machine-learning-with-python

  • Books & References for Machine Learning and Pandas5:01

    Pandas Repo:

    https://github.com/PacktPublishing/Learning-Pandas-Second-Edition

    https://github.com/jakevdp/PythonDataScienceHandbook

    https://jakevdp.github.io/PythonDataScienceHandbook/

    https://github.com/saurabhpati/data-analysis-pandas

    https://github.com/cuttlefishh/python-for-data-analysis

  • Scikit Learn5:59

    Intro to Scikit Learn Library in Python

  • Supervised Unsupervised Learning5:51

    Supervised and Unsupervised Learning

  • Regression and Classification Intro3:09

    Regression vs Classification

  • Bias Variance Precision Recall Confusion Matrix9:51

    Bias Variance

    Precision Recall

    Confusion Matrix


    Best Reference Google  Free Machine Learning Course

  • Test Train Split - Cross Validation2:04

    Train Test

    Cross Validation

  • Clustering & Classification7:08

    Clustering and Classification

  • Decesion Trees2:27

    Decision Trees

    Visualization of Iris Decision Trees

  • Support Vector Machines5:26
  • Neural Networks4:15
  • Parameters HyperParameters2:47
  • PCA - Dimension Reduction2:17
  • Conclusions3:14

Requirements

  • Python 101 (3-10 hours)
  • Data Science 101 (3-10 hours)
  • Career in Data Science (3-10 hours)

Description

Machine Learning 101 Class Bootcamp Course NYC

  1. Python Scikit-learn Library

  2. Supervised vs Unsupervised Learning

  3. Regression vs Classification models

  4. Categorical vs Continuous feature spaces

  5. Modeling Fundamentals: Test-train split, Cross validation(CV), Bias–variance tradeoff, Precision and Recall, Ensemble models

  6. Interpreting Results of Regression and  Classification Models (Hands On)

  7. Parameters and Hyper Parameters

  8. SVM, K-Nearest Neighbor, Neural Networks

  9. Dimension Reduction

Hands on:

  1. Understanding and Interpreting results of Regression and Logistic Regression using Google Spreadsheets and Python

  2. Calculating R-Square, MSE, Logit manually in excel for enhanced understanding (Multiple Regression)

  3. Understanding features of Popular Datasets: Titanic, Iris (Scikit) and Housing Prices

  4. Running Logistic Regression on Titanic Data Set

  5. Running Regression, Logistic Regression, SVM and Random Forest on Iris Dataset


Who this course is for:

  • Python and Data Analytics
  • Programmers with no knowledge of Maths
  • New Entrants in Data Science Field

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