Machine Learning with Python(live bootcamp)

Date

March 2, 2020

Start Time

18:00:00

End Time

18:00:00

Description

By Popular Demand, we are providing a Live Virtual Bootcamp course:

Machine Learning with Python Bootcamp

Overview

Machine Learning (ML) is changing the world. To use ML effectively, one needs to understand the algorithms and how to utilize them. This course provides an introduction into the most popular machine learning algorithms.

This course teaches doing Machine Learning using popular SciKit-Learn package in Python language.

This course teaches Machine Learning from a practical perspective. In-depth coverage of Math / Stats is beyond the scope of this course.

Live Online Only Duration: 4 Days / 4 hours per day, evening hours (6pm to 10pm PST)

What you will learn:

Python and SciKit-Learn
ML Concepts
Regressions

– Linear Regression

– Logistic Regressions

Classifications

– Naive Bayes

– SVM

Clustering algorithms (K-Means)

Audience:

Data analysts, Software Engineers, Data scientists

Duration:

Two Days

Skill Level:

Beginner to Intermediate

Industry Use Cases Covered

We will study and solve some of most common industry use cases; listed below

Finance

– Predicting house prices

– Predicting loan defaults at Prosper

Health care

– Predicting diabetes outcome

Customer service

– Predicting customer turnover

Text analytics

– Spam classification

Travel

– Predicting Uber demand

Other

– Predicting college admissions

Prerequisites

Good programming background
familiarity with Python would be a plus, but not required
No machine learning knowledge is assumed

Lab environment

Students will need to bring a laptop with python development environment setup

Students will need the following

A reasonably modern laptop with unrestricted connection to the Internet. Laptops with overly restrictive VPNs or firewalls may not work properly
Chrome browser
Introduction to Python programming environment
Introduction to Numpy and Pandas
Labs

Detailed Course Outline

Python Basics

– Working with Jupyter notebooks

– Numpy and Pandas

Machine Learning (ML) Overview

Machine Learning landscape
Understanding Deep Learning use cases
Understanding AI / Machine Learning / Deep Learning
Data and AI
AI vocabulary
Hardware and software ecosystem
Understanding types of Machine Learning (Supervised / Unsupervised / Reinforcement)
Scikit-Learn library overview
Lab:

Python Scikit-Learn Library

– Scikit-Learn utilities

Feature Engineering and Exploratory Data Analysis (EDA)

Preparing data for ML
Statistics Primer
Data cleanup
Extracting features, enhancing data
Visualizing Data
Labs:

– Data cleanup

– Exploring data

– Visualizing data

Machine Learning Concepts

Training and Testing
Gradient Descent
Overfitting / Under-fitting
Cross validation, bootstrapping
Confusion Matrix
ROC curve, Area Under Curve (AUC)
Linear Regression
Errors, Residuals
Multiple Linear Regression
Evaluating model performance
Labs:

Linear regression

– Use case: House price estimates

Logistic Regression

Understanding Logistic Regression
Calculating Logistic Regression
Evaluating model performance
Labs:

– Credit card application

– college admissions

Classification: SVM (Supervised Vector Machines)

SVM concepts and theory
SVM with kernel
Labs: -Customer churn data
Naive Bayes theory
Running Naive Bayes algorithm
Evaluating model performance
Lab

Classification: Naive Bayes

– Spam filtering

Unsupervised Algorithms

Overview of unsupervised algorithms
Supervised vs. unsupervised
Understanding unsupervised algorithms
Theory behind K-Means
Running K-Means algorithm
Estimating the performance
Labs:

Unsupervised: Clustering: K-Means