4.47 out of 5
4.47
182 reviews on Udemy

[2019] Machine Learning Classification Bootcamp in Python

Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn
Instructor:
Dr. Ryan Ahmed, Ph.D., MBA
2,041 students enrolled
English [Auto-generated] More
Apply advanced machine learning models to perform sentiment analysis and classify customer reviews such as Amazon Alexa products reviews
Understand the theory and intuition behind several machine learning algorithms such as K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
Implement classification algorithms in Scikit-Learn for K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
Build an e-mail spam classifier using Naive Bayes classification Technique
Apply machine learning models to Healthcare applications such as Cancer and Kyphosis diseases classification
Develop Models to predict customer behavior towards targeted Facebook Ads
Classify data using K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
Build an in-store feature to predict customer's size using their features
Develop a fraud detection classifier using Machine Learning Techniques
Master Python Seaborn library for statistical plots
Understand the difference between Machine Learning, Deep Learning and Artificial Intelligence
Perform feature engineering and clean your training and testing data to remove outliers
Master Python and Scikit-Learn for Data Science and Machine Learning
Learn to use Python Matplotlib library for data Plotting

Are you ready to master Machine Learning techniques and Kick-off your career as a Data Scientist?!

You came to the right place!

Machine Learning skill is one of the top skills to acquire in 2019 with an average salary of over $114,000 in the United States according to PayScale! The total number of ML jobs over the past two years has grown around 600 percent and expected to grow even more by 2020.

This course provides students with knowledge, hands-on experience of state-of-the-art machine learning classification techniques such as

  • Logistic Regression

  • Decision Trees

  • Random Forest

  • Naïve Bayes

  • Support Vector Machines (SVM)

In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques. We are going to build 10 projects from scratch using real world dataset, here’s a sample of the projects we will be working on:

  • Build an e-mail spam classifier.

  • Perform sentiment analysis and analyze customer reviews for Amazon Alexa products.

  • Predict the survival rates of the titanic based on the passenger features.

  • Predict customer behavior towards targeted marketing ads on Facebook.

  • Predicting bank client’s eligibility to retire given their features such as age and 401K savings.

  • Predict cancer and Kyphosis diseases.

  • Detect fraud in credit card transactions.

Key Course Highlights:

  • This comprehensive machine learning course includes over 75 HD video lectures with over 11 hours of video content.

  • The course contains 10 practical hands-on python coding projects that students can add to their portfolio of projects.

  • No intimidating mathematics, we will cover the theory and intuition in clear, simple and easy way.

  • All Jupyter noteboooks (codes) and slides are provided

  • 10+ years of experience in machine learning and deep learning in both academic and industrial settings have been compiled in this course. 

Students who enroll in this course will master machine learning classification models and can directly apply these skills to solve real world challenging problems.

Introduction

1
Introduction and Welcome Message
2
Introduction and Welcome Message [Course Material Download]
3
BONUS: Learning Paths
4
Course Overview

What is Machine Learning? The Big Picture

1
What is Machine Learning? The Big Picture Part #1
2
What is Machine Learning? The Big Picture Part #2

Installation & Setup [Optional][Skip if you are familiar with Jupyter Notebooks]

1
What is Anaconda and How to download it?
2
What are Jupyter Notebooks?
3
How to run a Jupyter Notebook?

Logistic Regression

1
Logistic Regression Introduction and Learning Outcomes
2
Logistic Regression Intuition
3
Confusion Matrix Overview
4
Logistic Regression - Project #1 - Part #1
5
Logistic Regression - Project #1 - Part #2
6
Logistic Regression - Project #1 - Part #3
7
Logistic Regression - Project #1 - Part #4
8
Logistic Regression - Project #1 - Part #5
9
Logistic Regression - Project #1 - Part #6
10
Logistic Regression - Project #1 - Part #7
11
Logistic Regression - Project #2 Overview
12
Logistic Regression - Project #2 - Part #1
13
Logistic Regression - Project #2 - Part #2
14
Logistic Regression - Project #2 - Part #2
15
Logistic Regression - Project #2 - Part #3
16
Logistic Regression - Project #2 - Part #4

Support Vector Machines

1
Support Vector Machines Intro and Learning Outcomes
2
Support Vector Machines - Intuition
3
Support Vector Machines - Project #1 - Part #1
4
Support Vector Machines - Project #1 - Part #2
5
Support Vector Machines - Project #1 - Part #3
6
Support Vector Machines - Project #1 - Part #4
7
Support Vector Machines - Project #1 - Part #5
8
Support Vector Machines - Project #1 - Part #6
9
Support Vector Machines - Project #1 - Part #7
10
Project #2 Overview
11
Support Vector Machines - Project #2 - Part #1
12
Support Vector Machines - Project #2 - Part #2
13
Support Vector Machines - Project #2 - Part #3
14
Support Vector Machines - Project #2 - Part #4

K-Nearest Neighbors

1
K-Nearest Neighbors Intro and Learning Outcomes
2
K-Nearest Neighbors - Intuition
3
KNN - Project #1 - Part #1
4
KNN - Project #1 - Part #2
5
KNN - Project #1 - Part #3
6
KNN - Project #1 - Part #4
7
KNN - Project #2 Overview
8
KNN - Project #2 - Part #1
9
KNN - Project #2 - Part #2
10
KNN - Project #2 - Part #3

Decision Trees and Random Forest

1
Decision Trees and Random Forest Intro and Learning Outcomes
2
Decision Trees - Intuition
3
Random Forest - Intuition
4
Decision Trees & Random Forest - Project #1 - Part #1
5
Decision Trees & Random Forest - Project #1 - Part #2
6
Decision Trees & Random Forest - Project #1 - Part #3
7
Decision Trees & Random Forest - Project #1 - Part #4
8
Decision Trees & Random Forest - Project #1 - Part #5
9
Decision Trees & Random Forest - Project #1 - Part #6
10
Decision Trees & Random Forest - Project #1 - Part #7
11
Decision Trees & Random Forest - Project #1 - Part #8
12
Decision Trees & Random Forest - Project #2 Overview
13
Decision Trees & Random Forest - Project #2 - Part #1
14
Decision Trees & Random Forest - Project #2 - Part #2
15
Decision Trees & Random Forest - Project #3 - Part #3
16
Decision Trees & Random Forest - Project #2 - Part #4

Naive Bayes Classifiers

1
Naive Bayes Intro and Learning Outcomes
2
Naive Bayes Intuition
3
Naive Bayes - Mathematics
4
Project #1 - Part #1
5
Project #1 - Part #2
6
Project #1 - Part #3
7
Project #1 - Part #4
8
Project #1 - Part #5
9
Project #1 - Part #6
10
Project #2 - Overview
11
Project #2 - Part #1
12
Project #2 - Part #2
13
Project #2 - Part #3
14
Project #2 - Part #4
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Includes

12 hours on-demand video
2 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion