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Data Science
Artificial Intelligence
Artificial Intelligence: Advanced Machine Learning
3.6 (21 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.
432 students enrolled

Artificial Intelligence: Advanced Machine Learning

Learn all the advanced skills you need to perform various real-world machine learning tasks in different environments.
3.6 (21 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.
432 students enrolled
Created by Eduero Academy, Inc.
Last updated 1/2020
English
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Artificial Intelligence: Advanced Machine Learning
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This course includes
  • 3.5 hours on-demand video
  • 2 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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
Course content
Expand all 46 lectures 03:41:08
+ – Getting Started With This Course
9 lectures 42:21
Set up the environment
02:16
Machine Learning - Classification
08:32
Machine Learning - Regression
02:56
Machine Learning - Transformers
02:14
Machine Learning - Clustering
06:07
Machine Learning - Manifold Learning
03:35
Machine Learning - Scikit-learn's estimator interface
04:04
Machine Learning - Cross-Validation
06:21
Machine Learning - Grid Searches
06:16
+ – Machine Learning - Model Complexity
7 lectures 39:41
Introduction
03:01
Linear models for regression
11:08
Support Vector Machines
Preview 07:43
Trees and Forests
06:05
Learning Curves
03:56
Validation Curves
02:33
EstimatorCV Objects for Efficient Parameter Search
05:15
+ – Understanding 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
+ – Machine Learning - Imbalanced Classes & 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
+ – Machine Learning - 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
+ – Machine Learning - 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
+ – Machine Learning - 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
+ – Machine Learning - 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
Requirements
  • 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 this course is for:
  • The course is intended for both professionals and students.
  • Anyone who wants to learn advanced machine learning skills