Python machine learninglearning manual

Machine learning (ML) is basically the field of computer science where computer systems can provide perception of data just like humans. Simply put, ML is an artificial intelligence that can extract patterns from raw data by using algorithms or methods. ML focuses on allowing computer systems to learn from experience without explicit programming or human intervention.

This tutorial will be very useful for graduate, graduate and research students who are interested in this discipline or as part of their curriculum. Readers can be beginners or advanced learners. This tutorial has been prepared for students and professionals and can be improved quickly. This tutorial is a stepping stone to your machine learning journey.

The reader must have basic knowledge of artificial intelligence. He/she should also know Python, NumPy, Scikit-learn, Scipy, and Matplotlib. If you are not familiar with these concepts, we recommend that you take the tutorials on these topics before proceeding further.

We live in a "data age" with better computing power and more storage resources. This data or information is increasing every day, but the real challenge is to make sense of all the data. Businesses and organizations are trying to cope with it by building intelligent systems using concepts and methods from data science, data mining, and machine learning. Among them, machine learning is the most exciting field in computer science. If we call machine learning the application and science of algorithms that can provide meaning to data, that's right.

Machine learning (ML) is the field of computer science where computer systems can provide perception of data just like humans.

In simple terms, ML is an artificial intelligence that extracts patterns from raw data through the use of algorithms or methods. The main focus of ML is to allow computer systems to learn from experience without explicit programming or human intervention.

Currently, humans are the smartest and most advanced species on Earth because they can think, evaluate, and solve complex problems. On the other hand, artificial intelligence is still in its infancy and has not surpassed human intelligence in many aspects. Then the question is, what does it take to make machine learning? The most appropriate reason for this is to "make efficient and efficient decisions based on data."

Recently, organizations are investing heavily in newer technologies such as artificial intelligence, machine learning, and deep learning to derive critical information from data to perform some practical tasks and solve problems. We can call this data-driven decisions made by machines, especially those that automate processes. In problems where programming is not intrinsicable, these data-driven decisions can be used instead of programming logic. The truth is that we can't do without human intelligence, but on the other hand, we all need to solve real-world problems efficiently. That's why machine learning is needed.

We've talked about the necessity of machine learning, but another question is, under what circumstances must machine learning be made? In some cases, we need machines to make data-driven decisions efficiently and at scale. Here are some such cases that make machine learning more effective.

The first scenario where we want machine learning and performing data-driven decisions may be an area that lacks expertise. Examples can be navigation in unknown regions or on a space planet.

Some scenarios are dynamic in nature, i.e. constantly changing over time. In the case of these situations and behaviors, we want machine learning and taking data-driven decisions. Some examples can be network connectivity and infrastructure availability in your organization.

Humans can have their own expertise in various fields; However, they were unable to translate this expertise into computational tasks. In this case, we need machine learning. These examples can be in areas such as speech recognition, cognitive tasks, and more.

Before discussing machine learning models, we must understand the following formal definition of ML given by Professor Mitchell-

"It is said that a computer program can learn from experience E about a certain type of task T and performance indicator P, if the computer's performance on the task in T (measured by P) improves with experience E."

Machine learning algorithms should perform tasks and gain experience over time. The metric that measures whether an ML algorithm performs as expected is its performance (P). P is basically a quantitative metric that uses its empirical E to tell the model how to perform task T. There are many metrics that help to understand ML performance, such as accuracy score, F1 score, confusion matrix, precision, recall, sensitivity, etc.

While machine learning is advancing rapidly and making great strides in cybersecurity and autonomous vehicles, the entire field of AI still has a long way to go. The reason behind this is that ML cannot overcome many challenges. The current challenges facing ML are-

Data Quality - Getting high-quality data for ML algorithms is one of the biggest challenges. Using low-quality data can lead to problems related to data preprocessing and feature extraction.

Time-consuming tasks – Another challenge for ML models is wasted time, especially in data acquisition, feature extraction, and retrieval.

Lack of experts - Since machine learning technology is still in its infancy, access to expert resources is a daunting task.

No clear goal to solve the business problem - ML does not have another clear goal and a clear business problem goal because the technology is not yet mature.

Problems with overfitting and underfitting - If the model is overfitting or underfitting, it does not solve the problem well.

The Curse of Dimensions - Another challenge for ML models is that there are too many features of data points. This can be a real obstacle.

Difficult to deploy - The complexity of ML models makes it difficult to deploy in real life.

Machine learning is the fastest growing technology, and according to researchers, we are in the golden years of AI and ML. It is used to solve many complex problems in the real world that traditional methods cannot solve. Here are some practical applications of ML-