Learn Machine Learning 101 Class Bootcamp Course NYC

Learn Machine Learning 101 Class Bootcamp Course NYC, available at Free, has an average rating of 3.95, with 19 lectures, based on 525 reviews, and has 18446 subscribers.

You will learn about 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 This course is ideal for individuals who are Python and Data Analytics or Programmers with no knowledge of Maths or New Entrants in Data Science Field It is particularly useful for Python and Data Analytics or Programmers with no knowledge of Maths or New Entrants in Data Science Field.

Enroll now: Learn Machine Learning 101 Class Bootcamp Course NYC

Summary

Title: Learn Machine Learning 101 Class Bootcamp Course NYC

Price: Free

Average Rating: 3.95

Number of Lectures: 19

Number of Published Lectures: 19

Number of Curriculum Items: 19

Number of Published Curriculum Objects: 19

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

Machine Learning 101 Class Bootcamp Course NYC

Python Scikit-learn Library
Supervised vs Unsupervised Learning
Regression vs Classification models
Categorical vs Continuous feature spaces
Modeling Fundamentals: Test-train split, Cross validation(CV), Bias–variance tradeoff, Precision and Recall, Ensemble models
Interpreting Results of Regression and Classification Models (Hands On)
Parameters and Hyper Parameters
SVM, K-Nearest Neighbor, Neural Networks
Dimension Reduction

Hands on:

Understanding and Interpreting results of Regression and Logistic Regression using Google Spreadsheets and Python
Calculating R-Square, MSE, Logit manually in excel for enhanced understanding (Multiple Regression)
Understanding features of Popular Datasets: Titanic, Iris (Scikit) and Housing Prices
Running Logistic Regression on Titanic Data Set
Running Regression, Logistic Regression, SVM and Random Forest on Iris Dataset

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Books used – Reference books

Lecture 3: Reference Books For Machine Learning

Lecture 4: Books & References for Machine Learning and Pandas

Lecture 5: Scikit Learn

Lecture 6: Supervised Unsupervised Learning

Lecture 7: Regression and Classification Intro

Lecture 8: Bias Variance Precision Recall Confusion Matrix

Lecture 9: Test Train Split – Cross Validation

Lecture 10: Clustering & Classification

Lecture 11: Decesion Trees

Lecture 12: Support Vector Machines

Lecture 13: Neural Networks

Lecture 14: Parameters HyperParameters

Lecture 15: PCA – Dimension Reduction

Lecture 16: Conclusions

Chapter 2: Hands on Jupyter Notebook

Lecture 1: Logistic Regression Notebook

Lecture 2: Notebook – Logistic, Decesion Tree, SVM, Random Fores, NN

Lecture 3: Decesion Tree K Means on Iris K Means on Digits

Instructors

Shivgan Joshi
Free Python Class Bootcamp Big Data Science NYC 312 285 6886

Rating Distribution

1 stars: 43 votes
2 stars: 46 votes
3 stars: 155 votes
4 stars: 150 votes
5 stars: 131 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu