Artificial Intelligence: Machine Learning with Python
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Artificial Intelligence: Machine Learning with Python

Learn all the skills you need to perform various real-world machine learning tasks in different environments.
5.0 (1 rating)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
22 students enrolled
Last updated 8/2017
English
Current price: $10 Original price: $195 Discount: 95% off
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Includes:
  • 3.5 hours on-demand video
  • 1 Article
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Extract features from categorical variables, text, and images
  • Solve real-world problems using machine learning techniques
  • Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
  • Implement machine learning classification and regression algorithms from scratch in Python
  • Dive deep into the world of analytics to predict situations correctly
  • Predict the values of continuous variables
  • Classify documents and images using logistic regression and support vector machines
  • Create ensembles of estimators using bagging and boosting techniques
  • Evaluate the performance of machine learning systems in common tasks
View Curriculum
Requirements
  • No prerequisites, knowledge of some undergraduate level mathematics would be an added advantage
Description

Data science and machine learning are some of the top buzzwords in the technical world today. Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. 

Python is one of the most popular languages used for machine learning and arguably, the best entry point to the fascinating world of machine learning (ML). If you're interested to explore both the programming and machine learning world with python, then go for this course.

In this course, you will work through various examples on advanced algorithms, and focus a bit more on some visualization options. We’ll show you how to use random forest to predict what type of insurance a patient has based on their treatment and you will get an overview of how to use random forest/decision tree and examine the model. And then, we’ll walk you through the next example on letter recognition, where you will train a program to recognize letters using a support Vector machine, examine the results, and plot a confusion matrix. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance.

At the end of this course, you will master all required concepts of machine learning to build efficient models at work to carry out advanced tasks with the practical approach.

Who is the target audience?
  • The course is intended for both professionals and students. Specifically anyone with none or minimal prior experience with programming.
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Curriculum For This Course
45 Lectures
03:41:03
+
Introduction
10 Lectures 45:54

Set up the environment
02:16

Classification
08:32

Regression
02:56

Transformers
02:14

Clustering
06:07

Manifold Learning
03:35

Scikit-learn's estimator interface
04:04

Cross-Validation
06:21

Grid Searches
06:16
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Model Complexity
7 Lectures 39:41
Introduction
03:01

Linear models for regression
11:08


Trees and Forests
06:05

Learning Curves
03:56

Validation Curves
02:33

EstimatorCV Objects for Efficient Parameter Search
05:15
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Pipelines
4 Lectures 16:57
Pipelines - Motivation
03:12

Pipeline Baiscs
06:32

Cross Validation With Pipelines
02:34

Using Pipelines with Grid-Search
04:39
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Imbalanced Classes And Metrics
4 Lectures 24:53
Default metrics
07:06

Classification Metrics
05:19

Precision - Recall tradeoff and Area Under the Curve
06:47

Built-In and custom scoring functions
05:41
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Model Selection For Unsupervised Learning
3 Lectures 17:37
How to evaluate unsupervised models?
06:54

Kernel Density Estimation
05:56

Model Selection For Clustering
04:47
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Handling Real Data
4 Lectures 19:15
Dealing with Real Data
06:26

OneHotEncoder
06:27

Encoding Features from Dictionaries
02:04

Handling missing values
04:18
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Dealing with Text Data
6 Lectures 27:25
Text Data Motivation
02:54

Text Feature Extraction with Bag-of-Words
06:51

Text Classification of Movie Reviews
07:28

Text Classification continuation
04:03

Text Feature Extraction Hashing Trick
03:28

Vector Representations
02:41
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Out Of Core Learning
5 Lectures 25:48
Out of Core and Online Learning
04:46

The Partial Fit Interface
05:15

Kernel Approximations
05:09

Subsampling for supervised transformations
05:38

Out of core text classification with the Hashing Vectorizer
05:00
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Course Summary
1 Lecture 03:32
Course Summary
03:32
+
Code Files
1 Lecture 00:01

Source Code for this course.

Source Code
00:01
About the Instructor
Eduero Academy, Inc.
5.0 Average rating
2 Reviews
46 Students
2 Courses
Learn Web Development, AI and Data Science

Eduero specializes in technical training via on-demand streaming. We seek to inform and inspire independent instructors with knowledge that goes beyond just technical skills. Each course undergoes a rigorous planning, review and an internal quality check phase - to ensure that the teaching is of highest standards available online. At the end of each section, you will be challenged to work through hands-on exercises to demonstrate mastery of the material.