Machine Learning Classification Bootcamp in Python
4.6 (506 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,966 students enrolled

Machine Learning Classification Bootcamp in Python

Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn
4.6 (506 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,966 students enrolled
Last updated 8/2020
English
English [Auto], Indonesian [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 11.5 hours on-demand video
  • 4 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • 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
Course content
Expand all 82 lectures 11:43:31
+ Introduction
6 lectures 16:05
Introduction and Welcome Message [Course Material Download]
00:00
BONUS: Learning Paths
00:33
Updates on Udemy Reviews
01:04
Get the Materials
00:06
+ What is Machine Learning? The Big Picture
2 lectures 28:25
What is Machine Learning? The Big Picture Part #1
11:01
What is Machine Learning? The Big Picture Part #2
17:24
+ Installation & Setup [Optional][Skip if you are familiar with Jupyter Notebooks]
3 lectures 22:40
What is Anaconda and How to download it?
04:12
What are Jupyter Notebooks?
03:34
How to run a Jupyter Notebook?
14:54
+ Logistic Regression
16 lectures 02:30:20
Logistic Regression Introduction and Learning Outcomes
02:23
Confusion Matrix Overview
11:32
Logistic Regression - Project #1 - Part #1
07:12
Logistic Regression - Project #1 - Part #2
06:21
Logistic Regression - Project #1 - Part #3
22:25
Logistic Regression - Project #1 - Part #4
14:53
Logistic Regression - Project #1 - Part #5
08:47
Logistic Regression - Project #1 - Part #6
04:18
Logistic Regression - Project #1 - Part #7
08:33
Logistic Regression - Project #2 Overview
07:03
Logistic Regression - Project #2 - Part #1
04:43
Logistic Regression - Project #2 - Part #2
10:38
Logistic Regression - Project #2 - Part #2
05:28
Logistic Regression - Project #2 - Part #3
11:45
Logistic Regression - Project #2 - Part #4
09:33
+ Support Vector Machines
14 lectures 02:04:28
Support Vector Machines Intro and Learning Outcomes
01:54
Support Vector Machines - Project #1 - Part #1
04:30
Support Vector Machines - Project #1 - Part #2
13:56
Support Vector Machines - Project #1 - Part #3
12:05
Support Vector Machines - Project #1 - Part #4
09:43
Support Vector Machines - Project #1 - Part #5
06:28
Support Vector Machines - Project #1 - Part #6
10:59
Support Vector Machines - Project #1 - Part #7
19:33
Project #2 Overview
03:24
Support Vector Machines - Project #2 - Part #2
08:22
Support Vector Machines - Project #2 - Part #3
07:19
Support Vector Machines - Project #2 - Part #4
07:20
+ K-Nearest Neighbors
10 lectures 01:23:55
K-Nearest Neighbors Intro and Learning Outcomes
01:44
K-Nearest Neighbors - Intuition
15:38
KNN - Project #1 - Part #1
06:40
KNN - Project #1 - Part #2
08:54
KNN - Project #1 - Part #3
07:58
KNN - Project #1 - Part #4
06:54
KNN - Project #2 Overview
05:37
KNN - Project #2 - Part #1
15:15
KNN - Project #2 - Part #2
07:47
KNN - Project #2 - Part #3
07:28
+ Decision Trees and Random Forest
16 lectures 02:29:21
Decision Trees and Random Forest Intro and Learning Outcomes
02:24
Decision Trees - Intuition
15:36
Random Forest - Intuition
08:02
Decision Trees & Random Forest - Project #1 - Part #1
03:02
Decision Trees & Random Forest - Project #1 - Part #2
09:26
Decision Trees & Random Forest - Project #1 - Part #3
09:46
Decision Trees & Random Forest - Project #1 - Part #4
08:16
Decision Trees & Random Forest - Project #1 - Part #5
12:16
Decision Trees & Random Forest - Project #1 - Part #6
07:25
Decision Trees & Random Forest - Project #1 - Part #7
06:36
Decision Trees & Random Forest - Project #1 - Part #8
09:30
Decision Trees & Random Forest - Project #2 Overview
05:42
Decision Trees & Random Forest - Project #2 - Part #1
20:55
Decision Trees & Random Forest - Project #2 - Part #2
09:55
Decision Trees & Random Forest - Project #3 - Part #3
07:48
Decision Trees & Random Forest - Project #2 - Part #4
12:42
+ Naive Bayes Classifiers
14 lectures 02:07:48
Naive Bayes Intro and Learning Outcomes
01:19
Naive Bayes Intuition
16:06
Naive Bayes - Mathematics
14:55
Project #1 - Part #1
09:25
Project #1 - Part #2
09:54
Project #1 - Part #3
14:12
Project #1 - Part #4
09:01
Project #1 - Part #5
05:08
Project #1 - Part #6
07:20
Project #2 - Overview
08:41
Project #2 - Part #1
10:57
Project #2 - Part #2
06:26
Project #2 - Part #3
05:56
Project #2 - Part #4
08:28
+ Bonus Lectures
1 lecture 00:28
***YOUR SPECIAL BONUS***
00:28
Requirements
  • Basic knowledge of Python Programming
  • Experienced computer user
Description

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.

Who this course is for:
  • Data Science Enthusiasts wanting to enhance their machine learning skills
  • Python programmers curious about Machine Learning and Data Science
  • Programmers or developers who want to make a shift into the lucrative data science and machine learning career path
  • Technologists wanting to gain an understanding of how machine learning models work
  • Data analysts who want to transition into the Tech industry