Introduction To Machine Learning using Python:

Python is a high-level general-purpose programming language that is increasingly being used in data science and in the design of machine learning algorithms. Introduction to machine learning and the Python language and shows how to configure Python and its packages. Python has libraries that allow developers to use optimized algorithms. Popular machine learning techniques are implemented, such as recommendation, classification and grouping.

Setting the environment:

The Python community has developed several modules to help programmers implement machine learning. In this article, we will use numpy, scipy and scikit-learn modules. We can install them using the cmd command:

pip install numpy scipy scikit-learn

Machine Learning Overview:

Machine learning involves the computer to be trained using a given data set and use that training to predict the properties of a given new die. For example, we can train the computer by feeding it with 1000 images of cats and more than 1000 images that are not of a cat, and we inform the computer each time if an image is of a cat or not. So, if we show the computer a new image, then, from the previous training, the computer must be able to tell if that new image is cat or not. The training and prediction process involves the use of specialized algorithms. We feed the training data for an algorithm and the algorithm uses this training data to provide forecasts on new test data. One of these algorithms is the K-Nearest-Neighbor classification (KNN classification). Take a test die and find the data values closest to this data from the test data set. Next, select the maximum frequency neighbor and provide its properties as a result of the forecast.

Machine Learning Algorithm Applications:

The machine learning algorithms developed are used in several applications, such as –

• vision processing
• vision processing
• languages
• Forecasting things such as stock market trends, climate
• Pattern recognition
• Games
• Data mining
• Specialized systems
• RoboticsMeltos

Python libraries for Machine Learning:

Machine Learning, as the name suggests, is the science of programming a computer so they are able to learn with different types of data. People performed machine learning tasks by manually coding all algorithms and mathematical formulas and statistics. This made the process long, tedious and inefficient. But in modern times, it became very easy and efficient compared to the old days by several python libraries, frameworks and modules. Today, Python is one of the most popular programming languages for this task and has replaced many languages in the industry, one of the reasons is its extensive library collection. The Python libraries used in Machine Learning are:

• Numpy
• Scipy
• Scikit-learn
• Theano
• TensorFlow
• Keras
• PyTorch
• Pandas
• Matplotlib

Learning Concepts:

Learning is the process of turning experience into knowledge or experience. Learning can be widely

• Supervised Learning
• Unsupervised Learning
• Semi-supervised Learning

In the same way, there are four categories of machine learning lgorithms, which are classified into three categories, as mentioned below, based on the nature of the learning data and interaction between the student and the environment. , as shown below-

• Supervised learning algorithm
• Unsupervised learning algorithm
• Semi-supervised learning algorithm
• Reinforcement learning algorithm

However, the most commonly used are supervised and unsupervised learning and reinforcement learning.

1.)Supervised Learning:

How it works: This algorithm consists of a variable of destination / result (or dependent variable) that must be predicted from a determined set of predictors (independent variables). Using this set of variables, we generate a function that assigns the inputs to the desired outputs. The training process continues until the model reaches a desired level of accuracy in the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression, etc.

2.)Unsupervised learning:

How it works: In this algorithm, we do not have any target variable or result to predict / estimate. It is used to group the population into different groups, which is widely used to segment clients into different groups for a specific intervention. Examples of unsupervised learning: apriori algorithm, K-means.

3.)Learning by Reinforcement:

How it works: Using this algorithm, the machine is trained to make specific decisions. It works like this: the machine is exposed to an environment in which it is continuously trained using intent and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Learning by Reinforcement: Markov Decision Process.

Become a Expert in Python with Certification

Machine Learning with Python – Applications:

Artificial Intelligence (AI) and Machine Learning are everywhere. The possibilities are that you use them and are not aware of it. In Machine Learning (ML), computers, software and devices work through cognition similar to the human brain. Typical successful machine learning applications include programs that decode handwritten text, facial recognition, speech recognition , voice recognition, voice recognition, standards recognition, spam detection programs, weather forecasting, stock market analysis and forecasts and so on. This chapter discusses these applications in detail.

Virtual Personal Assistants:

Siri, Google Now, Alexa are some common examples of virtual personal assistants. These applications help to find information when asked about the voice. You can even instruct these attendees to perform certain tasks such as “Set an alarm at 5:30 the next morning”, “Remember to visit the Passport office tomorrow at 10.30”.

Traffic congestion analysis and forecasts:

GPS navigation services monitor the user’s location and speeds and use them to create a map of current traffic. This helps prevent traffic congestion. Machine learning in these scenarios helps estimate the regions where congestion can be found based on previous records.

Automated video surveillance:

Video surveillance systems are now powered by AI and machine learning is the technology behind it that allows detecting and preventing crimes before they occur. In the event of an accident or a traffic accident, Internet users will ensure that Internet users can not access them. or a group that you share with someone, etc. Based on continuous learning, a list of Facebook users is given as suggestions from friends.

Facial recognition:

You upload a picture of yourself with a friend and Facebook instantly recognizes that friend. Machine learning works in the core of Computer Vision, which is a technique for extracting useful information from images and videos. The Pinterest uses the computational vision to identify objects or pins in the images and recommend similar pins to its users.

E-mail spam and malware filtration:

Machine learning is being widely used in the detection of spam and malware filtering, and the data banks of spam and malwares are still updated so that they are treated efficiently.

On-line customer support:

Currently, in several sites, there is the option of talking with the customer support representative while users browse through the website. the place . In most cases, instead of a real executive, you talk to a chatbot. These bots extract information from the site and provide it to customers to help them.

Readjusting search engine results:

Google and similar search engines are using machine learning to improve search results for their users. Each time a search is executed, the algorithms in the backend monitor how users respond to the results.

Product recommendations:

If a user buys or searches for a product online, they will continue to receive emails for shopping suggestions and advertisements about that product.