Python is a powerful programming language for carrying out research work and development of production systems. It is an interpreted language which allows you to perform multiple tasks.
Machine learning is an artificial intelligence tool that enables computers to learn. It studies the design of algorithms, identify patterns in the observed data and make predictions.
Machine learning concept involves predictive analysis, clustering and coming up with data patterns. These tasks are learned through observation and data models are built to explain the analysis without any programmed rules or instructions.
It focuses on program developments to access data and use the data to learn for itself.
The learning is automatic. No involvement of humans in the ultimate goal of the project.
How machine learning is used
- Supervised algorithms
This relies on data learned in the past and use it to predict the future events. It involves analysis of found datasets. The learning algorithm uses inferred function to predict the output values. This makes it easy for the system to set targets of the input data after thorough training.
The learning algorithm compares the actual output and the targeted output to identify any errors and use the information to modify the system.
- Unsupervised algorithms
This learning algorithm is used when the training information is neither classified nor labeled. It involves learning how the system develops a model to determine any hidden structure from the unlabeled data.
The system is required to predict the desired output, explore data and draw some inferences from the datasets. This method helps to identify any hidden structures from all the unlabeled data.
- Semi-supervised algorithms
This involves features of both supervised and unsupervised learning models. Both labeled data and unlabeled data are used for training. Systems using both methods are characterized by increased learning accuracy.
To use semi-supervised machine learning algorithm, the already labeled data need skilled and relevant resources in order to train or learn from it.
- Reinforcement algorithm
This learning algorithm interacts with the environment to identify any errors and take action. It uses trial and error search and a delayed reward in its operation.
The technique is ideal for machine and software agents who automatically use it to determine the system behavior and maximize the system performance. The agent needs a reward feedback in order to learn the best action thus the reinforcement signal.
Machine learning in Python increases effectiveness in the analysis of large quantities of data. It delivers fast and accurate data which identifies any profitable opportunity or risk in a project.
If you’re a beginner in python programming, it can be overwhelming to work with different modules and libraries in the python environment.
Python language and machine learning tools are very useful in completing a project. They give you the confidence to handle small projects, install and start the python interpreter.
Machine learning use step by step analysis to design a new project.
These steps include;
- Problem definition
- Preparation of required data
- Evaluating the algorithms
- Improving results
- Presenting result
Covering the above key steps and making the predictions will result in datasets that are used in developing a new project.
In python machine learning, you need to know;
- How to use python libraries to explore data
- How to preprocess the data using normalization techniques
- How to split data into various data sets
- How to use K-means algorithms in the construction of unsupervised models and use it to predict values.
- How to use Support Vector Machines (SVM) to construct a model for classifying data.
Machine learning in python
Install the Python and the SciPy library
To use python in your machine, you need to install the Python program and SciPy libraries to use in the development of projects. Once the application is up and running in your machine then you can load your datasets.
Loading the dataset
This the initial step in machine learning which involves loading the collected data. This data is collected through observation or going through saved documents to obtain the datasets.
If you don’t know how to go about collecting the data, you can browse through the UCI machine learning repository or Kaggle website to find a good dataset.
Once you find the datasets, you can load the data or import the module dataset into the script.py library.
Summarizing data set
This involves exploring the dataset by going through the data description to see what you can learn. Imported data comes with data presentation and you can use this information to gain more insights about the data.
Once you understand the data type, its attributes, and dimension, you can generate a statistical summary of all the data attributes. You can also break down data by its class variable for better analysis.
Visualizing the data set
To increase data exploration, you can visualize the images to work with using Matplotlib; a python library tool for visualizing data as an image. This gives insights to data and aid in data analysis.
Evaluating some algorithms
The data gathered through visualization is used to create models of data and estimate their accuracy. To evaluate the algorithms, you need to validate the datasets and build models for future predictions. Algorithms used to build the models used include K-Nearest Neighbor, logistic Regression among others. Each algorithm is evaluated then the best model is selected.
Further examination of the models helps in the evaluation of the model’s performance.
The algorithm used in the best model is used to determine the accuracy of data. It summarizes the results of the predictions.
When using machine learning, your goal is to learn how to configure the machine learning algorithm. You don’t have to worry about how they work. You don’t have to be an expert in python programming or an expert in machine learning in order to be accurate in predictions.
To complete you python project, loading the data and making predictions delivers desired results on your new python project.