Supervised and Unsupervised Learning with Python
4.1 (5 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.
27 students enrolled

Supervised and Unsupervised Learning with Python

Hop on the wonderful journey of machine learning and data analysis
4.1 (5 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.
27 students enrolled
Created by Packt Publishing
Last updated 12/2017
English
English [Auto]
Current price: $74.99 Original price: $124.99 Discount: 40% off
2 days left at this price!
30-Day Money-Back Guarantee
This course includes
  • 2 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll learn
  • Get to know various classification and regression techniques
  • Understand the concept of clustering and how to use it to automatically segment data
  • See how to build an intelligent recommender system
Course content
Expand all 38 lectures 02:08:20
+ Introduction to Artificial Intelligence 7
9 lectures 26:19

This video gives overview of the entire course.

Preview 03:01

Artificial Intelligence (AI) is a way to make machines think and behave intelligently. We will learn about AI and then its uses

Artificial Intelligence and Its Need
03:46

AI manifests itself in various different forms across multiple fields, so it's important to understand how it's useful in various domains.

Applications and Branches of AI
04:48

The legendary computer scientist and mathematician, Alan Turing, proposed the Turing Test to provide a definition of intelligence.

Defining Intelligence Using Turing Test
01:56

For decades, we have been trying to get the machine to think like a human. So we will see how to make machines think like human.

Making Machines Think Like Humans
03:55

The General Problem Solver (GPS) was the first useful computer program that came into existence in the AI world.

General Problem Solver
02:20

There are many ways to impart intelligence to an agent. In this video, we will focus on machine learning.

Building an Intelligent Agent
02:11

We will learn to install python and other required packages.

Installing Python 3 and Packages
02:12

In order to build a learning model, we need data that's representative of the world. We will see how to use the packages to interact with data

Loading Data
02:10

Now that you are done with the videos of section 1, let’s assess your learning. Here, are a few questions, followed by 4 options, out of which 1 is the correct option. Select the right option and validate your learning! The answers are provided in a separate sheet

Introduction to Artificial Intelligence
3 questions
+ Classification and Regression Using Supervised Learning
11 lectures 35:08

The world of machine learning is broadly divided into supervised and unsupervised learning. Let’s learn about the difference between both.

Preview 02:57

The process of classification is one such technique where we classify data into a given number of classes. In this video, we will learn about classification.

What is Classification?
02:09

Machine learning algorithms expect data to be formatted in a certain way before they start the training process. In order to prepare the data for ingestion by machine learning algorithms, we have to preprocess it.

Preprocessing Data
04:14

Label encoding refers to the process of transforming the word labels into numerical form. This enables the algorithms to operate on our data.

Label Encoding
01:39

Logistic regression is a technique that is used to explain the relationship between input variables and output variables. Naïve Bayes is a technique used to build classifiers using Bayes theorem. Let’s learn all about them in this video.

Logistic Regression and Naïve Bayes Classifier
07:15

A Confusion matrix is a figure or a table that is used to describe the performance of a classifier. So it is important to know how it works!

Confusion Matrix
02:56

A Support Vector Machine (SVM) is a classifier that is defined using a separating hyperplane between the classes. Given labeled training data and a binary classification problem, the SVM finds the optimal hyperplane that separates the training data into two classes. Let’s learn more in the video.

Support Vector Machines
01:46

In this video, we will build a Support Vector Machine classifier to predict the income bracket of a given person based on 14 attributes. Our goal is to see where the income is higher or lower than $50,000 per year.

Classifying Income Data
03:34

Regression is the process of estimating the relationship between input and output variables. This is an important concept in machine learning.

What is Regression?
02:09

In this video, we will build a single and multivariable regressor and learn where to use each of them.

Building a Single and Multivariable Regressor
03:45

In this video, we will use SVM to build a regressor that will estimate housing prices.

Estimating Housing Prices
02:44

Now that you are done with the videos of section 2, let’s assess your learning. Here, are a few questions, followed by 4 options, out of which 1 is the correct option. Select the right option and validate your learning! The answers are provided in a separate sheet

Classification and Regression Using Supervised Learning
4 questions
+ Predictive Analytics with Ensemble Learning
7 lectures 26:18

Ensemble Learning refers to the process of building multiple models and then combining them in a way that can produce better results than individual models.

Preview 03:17

A Decision Tree is a structure that allows us to split the dataset into branches and then make simple decisions at each level. This will allow us to arrive at the final decision by walking down the tree.

What Are Decision Trees
04:24

Random forests are an instance of ensemble learning. They have certain advantages over other classifiers. Lets know them in detail.

What are Random and Extremely Random Forests?
06:20

One of the most common problems we face in the real world is the quality of data. For a classifier to perform well, it needs to see equal number of points for each class. Hence we need to make sure that we account for this imbalance algorithmically.

Dealing with Class Imbalance
03:31

When you are working with classifiers, you do not always know what the best parameters are. This is where grid search becomes useful. Let's see how to find optimal training parameters using grid search.

Finding Optimal Training Parameters
02:26

Not all features are equally important while working with dataset. To find importance of specific features, we have to perform some operations.

Computing Relative Feature Importance
02:42

In this video, we will apply the concepts we learned in previous videoto a real world problem, predicting traffic.

Predicting Traffic
03:38

Now that you are done with the videos of section 3, let’s assess your learning. Here, are a few questions, followed by 4 options, out of which 1 is the correct option. Select the right option and validate your learning! The answers are provided in a separate sheet

Predictive Analytics with Ensemble Learning
4 questions
+ Detecting Patterns with Unsupervised Learning
5 lectures 19:35

Clustering is one of the most popular unsupervised learning techniques. This technique is used to analyze data and find clusters within that data and K-Means algorithm is a well-known algorithm for clustering data.

Clustering Data with K-Means Algorithm
05:54

In this video, we will estimate the number of clusters with Mean Shift algorithm.

Estimating the Number of Clusters
03:08

Here we will Estimate the quality of clustering with silhouette scores.

Estimating the Quality of Clustering
03:19

In this video, we will build a classifier based on a Gaussian Mixture Model.

Building a Classifier
04:57

In this video we will segment the market based on shopping patterns

Segmenting the Market
02:17

Now that you are done with the videos of section 4, let’s assess your learning. Here, are a few questions, followed by 4 options, out of which 1 is the correct option. Select the right option and validate your learning! The answers are provided in a separate sheet

Detecting Patterns with Unsupervised Learning
4 questions
+ Building Recommender Systems
6 lectures 21:00

In this video, we will see how to build a pipeline to select the top K features from an input data point and then classify them using an Extremely Random Forest classifier.

Preview 03:50

Nearest neighbors refers to the process of finding the closest points to the input point from the given dataset. This is frequently used to build classification systems that classify a datapoint based on the proximity of the input data point to various classes.

Extracting the Nearest Neighbors
02:16

A K-Nearest Neighbors classifier is a classification model that uses the nearest neighbors algorithm to classify a given data point. The algorithm finds the K closest data points in the training dataset to identify the category of the input data point. 

Building a K-Nearest Neighbors Classifier
03:40

In order to build a recommendation system, it is important to understand how to compare various objects in our dataset. The similarity score gives us an idea of how similar two objects are.

Computing similarity scores
04:56

Collaborative filtering refers to the process of identifying patterns among the objects in a dataset in order to make a decision about a new object.

Finding Similar Users
02:54

In this video, we will build a movie recommendation system based on the data provided in the file ratings.json.

Building a Movie Recommendation System
03:24

Now that you are done with the videos of section 5, let’s assess your learning. Here, are a few questions, followed by 4 options, out of which 1 is the correct option. Select the right option and validate your learning! The answers are provided in a separate sheet

Building Recommender Systems
2 questions
Requirements
  • This course takes a concept-based, explanation-focused approach. Each concept is explained and then the exercise or example is implemented.
Description

Build real-world Artificial Intelligence (AI) applications to intelligently interact with the world around you, explore real-world scenarios, and learn about the various algorithms that can be used to build AI applications. Packed with insightful examples and topics such as predictive analytics and deep learning, this course is a must-have for Python developers.

About the Author

Prateek Joshi is an artificial intelligence researcher, published author of five books, and TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley start-up that builds analytics platforms for smart water management powered by deep learning. His work in this field has led to patents, tech demos, and research papers at major IEEE conferences. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open Silicon Valley. Prateek has also been featured as a guest author in prominent tech magazines.

His tech blog has received more than 1.2-million page views from 200 over countries and has over 6,600+ followers. He frequently writes on topics such as artificial intelligence, Python programming, and abstract mathematics. He is an avid coder and has won many hackathons utilizing a wide variety of technologies. He graduated from University of Southern California with a master’s degree specializing in artificial intelligence. He has worked at companies such as Nvidia and Microsoft Research. You can learn more about him on his personal website.

Who this course is for:
  • Ideal for Python developers who want to build real-world Artificial Intelligence applications, this video is ideal for Python beginners, and is also suitable for experienced Python programmers looking to use AI techniques in their existing technology stacks.