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Publisher's Summary

Are you stuck in getting started with machine learning with Python? A Step-by-Step Guide to Learn and Master Python Machine Learning walks you through steps for getting started with machine learning with Python. 

Python is a popular and open-source programming language. In addition, it is one of the most applied languages in artificial intelligence and other scientific fields. On the other hand, machine learning is a branch of AI that applies algorithms to learn from data and create predictions. Machine learning is important in predicting the world around us. All the way from self-driving cars to predictions in the stock market, there is no place where machine learning cannot be utilized. 

Today, it is a top skill in high demand in the job market. For that reason, why not grab A Step-by-Step Guide to Learn and Master Python Machine Learning

You’ll discover the steps required to develop a successful machine-learning application using Python and Scikit-learn library. As a discipline, ML tries to design and understand computer programs for purpose of prediction. With A Step-by-Step Guide to Learn and Master Python Machine Learning, you’ll learn: 

  • The important concepts and real-world application of machine learning
  • Pros and cons of most popular machine-learning algorithms 
  • The basics of Python 
  • Learn about data preprocessing, analysis, and visualization 
  • Preprocessing techniques to use in data 
  • Regression methods 
  • Clustering 
  • Recommendation engines 
  • And many more 

If you are serious about machine learning with Python and don’t know how to get started, A Step-by-Step Guide to Learn and Master Python Machine Learning is your best tool to use.

©2018 Hein Smith (P)2018 Hein Smith

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  • Dollie Ring
  • 18-07-2020

Learn by doing

Throughout the book, the material is clearly presented and requires no previous introduction to machine learning. That said, while there are no mathematical proofs, and there is some mathematical notation, the reader will need to be comfortable with charts, graphs, and mathematical concepts and terminology.

Mr. Smith covers machine learning from the perspective of developing predictive machine learning models. He discusses many techniques for developing classification and regression models using well chosen examples and explanations. His writing style is informal and enjoyable.

I really appreciated Hein's approach to the ever present stack/tool chain issue that stands as a stumbling block to people getting started. The information is relevant, up to date and approaches many of the same data science/ML hello world type scenarios with a very deep functional perspective. To go further, I had a question about one of the external libraries...

24 people found this helpful

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  • Sidney
  • 26-07-2020

Machine learning, code, examples

Although ML is a field in computer science, it is not the same as the traditional computational methods. When you look at traditional computing, algorithms are described as a set of programmed instructions. These instructions provide solutions to a problem.

ML algorithms make computers to learn from data inputs and apply statistical analysis to display values found in a given range. Therefore, ML allows computers to create a model from a data sample so that it can permit the automation of decisions based on the type of data entered.

Nowadays, technology users hugely benefit from the idea of machine learning. For example, the facial recognition technology provides opportunities to social media networks so that their users can tag and share photos with their friends.

21 people found this helpful

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  • Williams
  • 27-07-2020

Balance between theory and practice

In this book, you’ll learn all the important topics that you need to know for you to implement machine learning with Python. You’ll learn how to download, install Python, and get the best package for machine learning in Python. You’ll also load a dataset and understand its structure using data visualization and summaries. If you are new to machine learning and looking to eventually launch a career in Python, this book was designed for you. Python is a powerful interpreted language. Unlike other languages such as R, Python is a complete language and platform where you can apply both research and development production. Still, there are many modules and libraries which you can select from and generate different ways to perform each task. Methods in machine learning are popularly used in a wide variety of fields such as engineering, sciences, physics, and computer systems. Additionally, it is also used by commercial websites for the recommendation system, advertising, and predicting the actions of a customer. Machine Learning has popped out as a major engine of most commercial applications and research endeavors. But this particular branch does not exclude large research companies. In this book, you’ll get an in-depth introduction to the field of machine learning from linear models to deep learning and reinforcement learning. You will understand the principles behind machine learning problems like regression, reinforcement learning, and classification.

19 people found this helpful

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  • Stephanie Medders
  • 28-07-2020

Helpful for AI novices

Artificial Neural Network describes a set of connected input/output where every connection is linked to a particular weight. In the learning phase, the network adjusts the weights so that it can predict the right class label of input tuples. There are a lot of network architectures present now. Some of them include the Feed-forward, Recurrent, Convolutional, etc. The correct architecture depends on the model application. In most cases, the feed-forward models provide a reasonably accurate result and mostly for image processing applications. There can be many hidden layers in a model based on the complexity of the function that is to be wrapped by the model. If you have a lot of hidden layers, it will facilitate the modeling of complex relationships like deep neural networks. However, the presence of many hidden layers increases the time it takes to train and adjust weights. Another drawback is the poor interpretability when compared to other models such as Decision Trees. Despite this, ANN has performed well in the majority of the real-world applications. It has an intensive persistence to noisy data and can categorize untrained patterns. Generally, ANN works better with continuous-valued inputs and outputs.

18 people found this helpful

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  • Betty Murtagh
  • 30-07-2020

Python based machine learning book out there

While both data and computational analysis cause an individual to start to think like they aren’t being objective, being biased on a given data doesn’t mean that the output from the machine learning is neutral. The human bias affects the organization of data and algorithms that determine how ML should use data. If you decide to use historical photographs of scientists in your specific computer training, a computer might fail to classify scientists. Although machine learning is continuously applied in the business, biases that go unnoticed can lead to a systematic problem that can prevent people from receiving loans and many other things. In short, human biases can negatively impact other people. This is very important to underline and work towards removing it as possible. One particular method which you can use to achieve zero biases is to ensure that several people work on a project. Since machine learning is an area which is continuously being improved, it is essential to remember that algorithms, approaches, and methods continue to change.

15 people found this helpful

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  • Anthony Reid
  • 02-08-2020

Up there with Mr Hein's Courses

The most important step when getting started with Machine Learning is to ensure that the data available is of great quality. You can collect data from genuine sources such as Kaggle, data.gov.in, and UCI dataset repository. For example, when students are getting ready to take a competitive exam, they always find the best resources to use to ensure they attain good results. Similarly, accurate and high-quality data will simplify the learning process of the model. This means that during the time of testing, the model would output the best results.

A great amount of time, capital, and resources are involved in data collection. This means that organizations and researchers have to select the correct type of data which they want to implement or research.

For instance, to work on the Facial Expression Recognition requires a lot of images that have different human expressions. A good data will make sure that the results of the model are correct and genuine.

12 people found this helpful

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  • Morris
  • 04-08-2020

Rich

Clustering is the process of gathering entities with similar characteristics together. This technique belongs to unsupervised machine learning whose target is to identify similarities in the data point and group the same data points together. By grouping similar entities in one place allows one to identify the attributes of different groups. In other words, this generates insight into the underlying patterns of various groups. There are countless application areas of grouping unlabeled data. For example, it is possible to select different groups of customers and market every group differently to take advantage of the revenue. Another example may include grouping documents together that belong to similar topics. Additionally, clustering is used to reduce the dimensionality of the data when you handle various copious variables. The phrase cluster doesn’t have an accurate definition. A cluster describes a set of points whereby any point in the cluster is close to any other point in the cluster than a point absent in the cluster. Sometimes, a threshold is used to de?ine all points in a cluster close to one another. A partitioning method will first create an original set of K-partitions where kparameter is the number of partitions to construct. Next, it applies an iterative relocation approach which tries to enhance the partitioning by shifting objects from one group to another. These clustering techniques help generate a one-level partitioning of data points. There are various partitioning-based clustering like Kmeans, fuzzy C-, means, and K-medoids. This will look at K-mean clustering.

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  • Anne Vierra
  • 09-08-2020

This Book is the Real Deal

If you want to improve the process of delivery, you’ll need to enhance it by applying drones and integrating k-means algorithm to determine the optimal number of launch locations and a genetic algorithm to compute the route of the truck.

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  • Ernest Vasquez
  • 09-08-2020

Good knowledge of machine learning

This is perspective of someone who knows a little python and had little knowledge of machine learning, but has kind of seen neural nets and regressions used in different applications

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  • Jeffery Anderson
  • 08-08-2020

Turning me feel deeply

we will look at data wrangling visualization, wet applications, and accelerated data analysis that can be done with the help of the Python language.

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  • Derek
  • 18-07-2020

ML and TF combination

Most e-commerce and retail companies are taking advantage of the massive potential of data to boost sales by implementing a Recommender system on their particular websites.

These systems focus on suggesting to the user’s items that they may like or have interest in.

The data needed for recommendation engines comes from explicit user ratings to watch a movie or listen to a song from implicit search engine purchase histories and queries. Sites such as YouTube, Spotify, and Netflix have data to use to recommend playlists.

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  • Nicole Dunigan
  • 20-07-2020

by a Master Teacher

Unlike supervised learning where data is labeled, with unsupervised learning, you deal with unlabeled data. This means that it is the task of the learning algorithm to identify similar features in the data that it is supplied. Since unlabeled data is very popular compared to labeled data, techniques of machine learning are among the most valuable in the industry. The aim of unsupervised learning is very simple.

The largest application of unsupervised learning is within the transactional data. There can be a massive data set made up of customers and the products which they purchase, but since you are a human, you can’t manage to extract meaning and similarity from customer profile and their purchase history.

The best time to apply unsupervised machine learning is when you don’t have data on expected outcomes, like defining a target market for a new product that your business has never sold before, but if you are attempting to understand your consumer base, supervised learning is the right technique.

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  • Andrew Miura
  • 21-07-2020

Intuitive and Comprehensive

Customer segments will make you understand the patterns which distinguish your customers. While you can analyze your own customer base, soon it shall be clear that there are different groups that have customized requirements. This allows you to build a deeper understanding of your customers and ?ind out what makes them tick. It is no gem that a customer is always more pro?itable compared to others. However, to be profitable, businesses should have a better understanding of the way profitability relates to customer segmentation. Discovering the difference between customers will permit one to personalize your method to the desires of the customer segments. Customer segmentation describes the practice of categorizing a customer base into different groups of individuals similar in a given way. Customer segments are often determined based on similarities such as personal characteristics, behaviors, and preferences. By understanding your customers and their differences, it becomes one of the most important stages of measuring the customers’ relationship.

18 people found this helpful

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  • Tegan
  • 26-07-2020

Part of my core library

Before you can get started with Machine Learning with Python, you must have Python installed on your computer, however, you might not need to download it. So, the first thing to do is to confirm that Python is not installed by typing “Python” in a command window. When you see a response from a Python interpreter, it will consist of a version number in its original display. In general, any recent version will work because Python tries to maintain backward compatibility. Companies which apply the Recommender system concentrate on raising the sales due to the personalized offers and improved customer experience. Recommendations usually increase searches and make it easy for users to access content which they are interested in, and surprise them with offers that they have never searched before. What is interesting is that companies can now gain and retain customers by sending out email links to new offers that fulfill the interests of their profiles. By creating an added advantage to users through suggesting products and systems, it creates a great feeling among buyers. This is a great thing because it will allow companies to stay ahead of their competitors.

16 people found this helpful

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  • Cynthia Johnson
  • 29-07-2020

Set to launch your first ML project.

With some knowledge of basic Python, machine learning skills, and Python libraries, you are now set to launch your first machine learning project with Python. Consider learning the open-source Python libraries. The scientific Python libraries will help you complete easy machine learning tasks. However, the choice of some of these libraries can be completely subjective and highly debatable by many people in the industry.

All in all, we recommend you to start by exploring Scikit-learn library. Make sure you are familiar with its concepts and how to use it. Once you are done with it, you can dive deep into advanced machine learning topics such as complex data transformation and vector machines.

Just like how a child learns how to walk is the same with learning Machine Learning with Python. You need to practice many times before you can become better. Practice different algorithms and use different datasets to improve your knowledge and overall problem-solving skills.

11 people found this helpful

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  • Ronal
  • 02-08-2020

Generative deep learning

The content of a particular item is abstract and provides more options. You can use many different variables. For instance, for a book, you can include the genre, author, the text of the book and many other factors. Once you know which content you will factor. You need to convert all the data into a vector space model, which is an algebraic representation of text documents. You perform this using a Bag of Words model which represents documents disregarding the sequence of words. In this particular model, every document appears like a bag with some words. Therefore, this method will permit word modeling with respect to dictionaries, where every bag has some words from the dictionary. An exact implementation of a Bag of Words is the TF-IDF representation. In full, TF stands for Term Frequency and IDF stands for Inverse Document Frequency. This particular model combines the significance of the word in the document with the significance of the world in the corpus. This was just a general aspect of Content-based recommendation engines. It is important to recognize that a Bag of Words representation doesn’t factor in the context of words. If it is necessary to include that, Semantic Content Representation becomes useful.

7 people found this helpful

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  • Ruth Hudson
  • 08-08-2020

Fascinating Read into Computer Learning Using Pyth

For supervised learning, the computer has an example of input data to work on. The aim of this method is to allow the algorithm to “learn” by comparing actual output using a trained output to discover errors and alter the model. In other words, this method contains patterns which assist in predicting label values on extra data that is unlabeled. For example, in supervised learning, you can feed an algorithm data with shark images and label them as fish. Also, you can feed it with images of oceans and label it like water. After the algorithm is trained several times with this particular data, the algorithm must be able to differentiate unlabeled fish images and unlabeled ocean images. One of the most popular use cases of supervised learning is the application of historical data to help forecast the statistical chance of an event to happen. It can use historical stock market data to predict future changes in the market. Additionally, supervised learning can help in the filtering of spam emails. Supervised learning makes it possible to classify untagged photos of dogs by using photos of dogs that have been tagged already.

4 people found this helpful

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  • Dennis
  • 10-08-2020

Gold Medal Winner

Clustering of documents in numerous categories depends on topics, tags, and the content of the document. This is a normal classification problem and k-means is a great algorithm for this function. The original document processing is important when you want to replace every document as a sector and applies the frequency term to use terms which classify the document. The vectors of the document have to be clustered so that they can select similarity in document groups.

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  • Jackson
  • 09-08-2020

Perfect for the class that i'm taking

There are many different kinds of classification algorithms developed, however, it is hard to pick on one which is better than the other. This is because of a few factors such as the application and nature of the existing data set. For instance, if you have linearly separable classes, the linear classifiers such as Logistic regression, Fisher’s linear discriminant can execute complex models.

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  • James
  • 08-08-2020

Fun, interactive reading

If you want a great model in regression, then it is important to take into consideration the type of variables which you want to test as well as other variables which can affect the response.

In the spirit of reconciliation, Audible Australia acknowledges the Traditional Custodians of country throughout Australia and their connections to land, sea and community. We pay our respect to their elders past and present and extend that respect to all Aboriginal and Torres Strait Islander peoples today.