MACHINE LEARNING WITH PYTHON An Approach to Applied Machine Learning PDF 2023.

  MACHINE LEARNING WITH PYTHON An Approach to Applied Machine Learning PDF 2023.

What is machine learning?

Machine learning or machine learning is a sub-discipline of artificial intelligence that consists of detecting trends within a knowledge history (example: the climate) in order to derive a prediction model for the future (here, the weather). 

 How does machine learning work? Machine learning (ML) is one of the main artificial intelligence technologies. It consists in training an algorithm to recognize recurrent patterns within a learning base. This training results in a computer model designed to make predictions (recognize a sound, an image...) or automate tasks (answer a question, automate the driving of a vehicle...). 

 Where a traditional program executes instructions, a machine learning algorithm improves its performance as it learns, but also as the context evolves and successive re-trainings. 

The more data we "feed" it, the more accurate it becomes. In the 2010’s, machine learning achieved momentum with the advent of big data and the progression of computing capabilities (including the rise of GPUs). 

Indeed, big data is essential to train models on the vast volumes of data needed for automatic language processing or image recognition. What is a machine learning model? A machine learning model is a file that has been trained from a learning base in order to automate tasks, for example to recognize an emotion with regard to an expression on a face, translate a text, propose products according to an appetite profile... Once trained, the model must be able to generate results from data (texts, photos) that it has never processed before. 

 What is the value of machine learning? 

 Its most popular applications in this area include product recommendation, machine translation, autonomous vehicles and diagnostic assistance in the health sector. Within massive volumes of data, machine learning also makes it possible to detect hidden trends, which are not detectable via human analysis.

Machine learning supervised vs non-supervised, what difference?

On the supervised machine learning side, the training data are previously annotated or labeled. Objective: to use a representative learning base that makes it possible to arrive at a model capable of generalizing, that is to say to make correct predictions on data not present in the initial learning base. In the field of supervised learning, we find the algorithms of classification, linear regression, logistic regression, decision trees, or random forests. As for unsupervised learning, it decodes the contextual information of the training data and the resulting logic, without resorting to a pre-established source of knowledge. Data is not annotated or labelled. Included in this category are clustering algorithms (such as K-means) designed to share data in similar groups. For example, they can be used to group customers by type, depending on profile characteristics, similar purchasing behaviour...

Python machine learning:

Python for Deep Learning & Machine Learning: Full course on Machine Learning to master artificial intelligence, Tensorflow, and neural networks Description: Python is recognized as one of the best programming languages for its flexibility. It works in almost every field, from Web development to the development of financial applications. However, it is no secret that the best application of Python is in machine learning, deep learning and artificial intelligence tasks. Although Python facilitates the use of Machine Learning and Deep Learning, it will still be quite frustrating for someone who has no knowledge of how machine learning works. If you know the basics of Python and want to learn Deep Learning, this course is for you. This course will help you learn how to create programs that accept data entry and automate feature extraction, simplifying real-world tasks for humans. However, you may learn unnecessary lessons if you do not filter what you learn. When creating this course, we filtered everything to isolate the essential basics you will need in your in-depth learning journey. It is a basic course that is suitable for both beginners and experts. If you are looking for a course that starts with the basics and moves on to advanced topics, this is the best course for you. It only teaches what you need to get started in machine learning and deep learning without frills. While this helps keep the course fairly concise, this is all you need to start with the topic. Who is this course for? Programmers looking to add machine learning and deep learning to their skills Professional mathematicians eager to learn how to analyze data by programming Any Python programming enthusiast looking to add Deep Learning skills to their portfolio

DOWNLOAD THIS BOOK FREE PDF!