Step-by-Step Machine Learning with Python

Name: Step-by-Step Machine Learning with Python
Rating: 3.5 (3 reviews)

Put your Python skills to the test and enter the big world of data science to learn the most effective machine learning

Created byPackt Publishing

Last updated 10/2017

English

English [Auto],

What you'll learn

Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
Use Python to visualize data spread across multiple dimensions and extract useful features
Delve into the world of analytics to predict situations correctly
Implement machine learning classification and regression algorithms from scratch in Python
Be amazed to see algorithms in action
Evaluate the performance of a machine learning model and optimize it
Solve interesting, real-world problems using machine learning and Python as the journey unfolds

Course content

8 sections • 45 lectures • 4h 55m total length

The Course Overview5:03
This video gives a glimpse of what you will learn through this video.
Introduction to Machine Learning6:42
This video, will let you kick off your Python and machine learning journey with the basic, yet important concepts of machine learning.
Installing Software and Setting Up6:04
This is the first step with few practical tasks to get started. Yes! We are talking about installation and initial set up, which we’ll be doing together in this video.

Understanding NLP4:10
Natural language processing or NLP, is a significant subfield of machine learning, which deals with the interactions between machine and human natural languages. We will explore this, through this video.
Touring Powerful NLP Libraries in Python10:36
After a short list of real-world applications of NLP, we will be touring the essential stack of Python NLP libraries in this video.
Getting the Newsgroups Data3:17
Let’s get a step ahead by getting the Newsgroups data and downloading it.
Thinking about Features5:31
This video will walk you through the steps to achieve the extraction of features from the dataset
Visualization3:15
Let’s get a general idea of how the data is structured, what possible issues may arise, and if there are any irregularities that we have to take care of.
Data Preprocessing2:29
This video will let you improve the most indicative Features from visualization by using the data preprocessing Techniques.
Clustering4:04
How could you find the best division or a decent Approximation between set of data in a dataset? That’s what we are going to learn in this video.
Topic Modeling3:40
When we read a text, we expect certain words appearing in the title or the body of the text to capture the semantic context of the document. This video, will let you achieve this in Machine Learning and Python programming.

Getting Started with Classification5:02
It is a great starting point of learning classification with a real-life example-our email service providers are already doing this for us, and so can we. We will be learning the fundamental and important concepts of classification.
Exploring Naïve Bayes2:49
How can we make a prediction of probability distribution over all classes, besides the most likely class that the data sample is associated with? Let’s answer this question, through this video!
The Mechanics of Naïve Bayes4:49
This video will let you understand the magic behind the algorithm-how naive Bayes works.
The Naïve Bayes Implementation16:30
The most important task after learning a concept is to implement it. Let’s try the implementation of Naïve Bayes algorithm with some sample codes, in this video.
Classifier Performance Evaluation10:22
Beyond accuracy, there are several measurements that give us more insights and avoid class imbalance effects. Let’s see these performance measures right now!
Model Tuning and cross-validation5:11
Having learned what metrics are used to measure a classification model, we can now study how to measure it properly. Let’s do it right away!

Recap and Inverse Document Frequency4:26
This video will show you how to adopt a more comprehensive approach to extract text features, the term frequency-inverse document frequency
The Mechanics of SVM5:51
Since there can be infinite number of feasible Hyperplanes, how can we identify the optimal one? Let's do it in this video!
The Implementations of SVM5:44
Let's put into action, the fundamentals of SVM Classifier right away on news topic classification and learn to deal with more than two classes.
The Kernels of SVM3:13
What could you do if you are not able to find any linear hyperplane to separate two classes? Let’s see the solution to this problem!
Choosing Between the Linear and the RBF Kernel3:51
This video will walk you through different scenarios where the linear kernel is favored over RBF.
News topic Classification with Support Vector Machine10:26
It is finally time to build our state-of-the-art, SVM-based news topic classifier with all we just learned. Let’s do it!
Fetal State Classification with SVM5:59
After a successful application of SVM with the linear kernel, let’s look at one more example where SVM with the RBF kernel is suitable for it

Brief Overview of Advertising Click-Through Prediction4:45
How can you find whether a given ad on a given page or app will be clicked by a given user or not, with predictive features? Let’s answer this question.
Decision Tree Classifier13:43
When working on a prediction model, how can you get all of the possible decision alternatives and the corresponding outcomes? Let’s see this, now.
Learn the Construction of a decision tree
Calculate the Gini impurity
Implement the entropy calculation function
The Implementations of Decision Tree6:17
With a solid understanding of partitioning evaluation metrics, let's practice the CART tree algorithm by hand on a simulated dataset.
Click-Through Prediction with Decision Tree6:52
After several examples, it is now time to predict ad click-through with the decision tree algorithm we just thoroughly learned and practiced.
Random Forest - Feature Bagging of Decision Tree5:13
How will you reduce the high variance that a decision tree model suffers from and hence in general performs better than a single tree? Let’s see the solution for this problem now!

One-Hot Encoding - Converting Categorical Features to Numerical6:00
This video will show you how one-hot encoding converts the categorical feature to k binary features
Logistic Regression Classifier12:09
Let’s turn to a new algorithm with high scalability to large datasets. Yes! We are talking about Logistic regression which is one of the most scalable classification algorithms.
Click-Through Prediction with Logistic Regression by Gradient Descent21:30
Let’s deploy the algorithm that we just developed and test it in our click-through prediction project
Feature Selection via Random Forest4:29
How can we rank the importance of features based on their occurrences in nodes among all trees, and select the top most important ones? Let’s see this right now!
Fit the random forest model
Look at the bottom 10 features and the corresponding 10 least important features
Select the top 500 features

Brief Overview of the Stock Market And Stock Price3:48
Let’s explore the relationships between the observations and the targets, and to output a continuous value based on the input features of an unknown sample.
Predicting Stock Price with Regression Algorithms6:41
This video will walk you through some essential steps to apply regression techniques in predicting prices of a particular stock.
Data Acquisition and Feature Generation3:30
Let’s get started with obtaining the dataset we need for our project.
Linear Regression8:27
Since all features and driving factors are available, let’s focus on regression algorithms that estimate the continuous target variables from these predictive features.
Decision Tree Regression7:36
After linear regression, the next regression algorithm need to learn is decision tree regression. Let’s start exploring it right away!
Support Vector Regression2:57
Let’s get started with the third regression algorithm which is support vector regression (SVR).
Regression Performance Evaluation3:27
So far, we have covered several popular regression algorithms in-depth and implemented them from scratch by using existing libraries. Let’s step ahead and evaluate the performance of these regression algorithms.
Stock Price Prediction with Regression Algorithms9:49
Now that we have learned three commonly used and powerful regression algorithms and performance evaluation metrics, why don't we utilize all of these in solving our stock price prediction problem? Let’s do it in this video.

Best Practices in Data Preparation Stage11:17
Apparently, no machine learning system can be built without data. Data collection should be our first focus. Let’s learn the best practices for the data preparation stage.
Best Practices in the Training Sets Generation Stage8:45
With well-prepared data, it is safe to move on with the training sets generation stage. Let’s move ahead and explore this.
Best Practices in the Model Training, Evaluation, and Selection Stage3:40
Given a machine learning problem, the first question many people ask is usually: what is the best classification/regression algorithm to solve it? This video will bring to you an answer for this question.
Best Practices in the Deployment and Monitoring Stage5:39
After all the processes in the previous three stages, we now have a well established data preprocessing pipeline and a correctly trained prediction model. The last stage of a machine learning system involves saving those resulting models from previous stages and deploying them on new data, Let’s see how to do this.
Save, load, and reuse models
Monitor model performance
Update models regularly

Requirements

This course is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem and involves hands-on work, giving you a deep insight into the world of machine learning. With this simple yet rich language—Python—you will understand and be able to implement the examples with ease.

Description

Data science and machine learning are some of the top buzzwords in the technical world today. The resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This video is your entry point to machine learning. It starts with an introduction to machine learning and the Python language and shows you how to complete the necessary setup. Moving ahead, you will learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. With the help of the various projects included, you will acquire the mechanics of several important machine learning algorithms, which will no longer seem obscure. Also, you will be guided step-by-step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and master best practices for applying machine learning techniques. Throughout this course, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple Python language. Interesting and easy-to-follow examples—including news topic classification, spam email detection, online ad click-through prediction, and stock prices forecasts—will keep you glued to the screen till you reach your goal.

About the Author :

Yuxi (Hayden) Liu is currently a data scientist working on messaging app optimization at a multinational online media corporation in Toronto. He focuses on social graph mining, user demographics, interest prediction, spam detection, and recommendation systems. He has worked for several years as a data scientist in real-time bidding programmatic advertising, where he applied his machine learning expertise in ad optimization, click-through rate and conversion prediction, and click fraud detection. Hayden earned his degree from the University of Toronto, and published five IEEE transactions and conference papers during his master's research. He is also a machine learning education enthusiast, and has authored the Python Machine Learning By Example book.

Who this course is for:

This course is for anyone interested in entering the data science stream with machine learning. a basic familiarity with Python is assumed.

Step-by-Step Machine Learning with Python

What you'll learn

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Course content

Getting Started with Python and Machine Learning3 lectures • 18min

Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms8 lectures • 37min

Spam Email Detection with Naïve Bayes6 lectures • 45min

News Topic Classification with Support Vector Machine7 lectures • 40min

lick-Through Prediction with Tree-Based Algorithms5 lectures • 37min

Click-Through Prediction with Logistic Regression4 lectures • 44min

Stock Price Prediction with Regression Algorithms8 lectures • 46min

Best Practices4 lectures • 29min

Requirements

Description

Who this course is for: