Machine Learning 101 for Non Programmers and Non Stat Audience $99 [3 hrs]

sam. 17 novembre à 20:30 — informations

Fuseau horaire : Paris (GMT+01:00)

Mathmatter Tutoring
Jackson Heights

Chaque semaine le samedi

If you have attended a class before with us then you might classify for a discount! Machine learning is going to disrupt a lot of industries in the next decade. Whether it be driverless cars,cashierless shops, personal assistant or AI physicians, the effect of machine learning will be pervasive. Prepare for the next big disruption. This class assumes you don’t have any programming background. However, it is recommended to have a basic understanding in Python. Understanding of Pandas Python Library will help a lot. You will know when to run supervised or unsupervised learning for your data, whether to use classification or regression model, how to handle categorical vs continuous data. After the data is ready you will learn how to split the data and analyze the final results. We will use a lot of images to delineate different terms and topics used in Machine Learning. Although we would use classical datasets like IRIS, Titanic, etc but you will be scale and use your data for the models learned in the session. Takeaways include developing basic vocabulary for:

Run machine learning models on your data using the setup learTopics covered:

Supervised vs Unsupervised Learning

Regression vs Classification models

Categorical vs Continuous feature spaces

Python Scikit-learn Library

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

Interpreting Results of Regression and Classification Models

Parameters and Hyper Parameters

Dimension Reduction


K-Nearest Neighbor

Neural Networks Projects for the session (Python):

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

Calculating R-Square, MSE, Logit manually for enhanced understanding

Understanding features of Popular Datasets: Titanic, Iris and Housing Prices

Running Logistic Regression on Titanic Data Set

Running Regression, Logistic Regression, SVM and Random Forest on Iris Dataset Post Session Assessment:

Top 20 machine learning interview question

ned in class

Make data ready, choose and configure the correct model for your data

Interpret results of your machine learning algorithm


Mathmatter Tutoring
Jackson Heights

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