How to Build Machine Learning Solutions with Python

 

Implementing Machine Learning with Python

In 2018, developers all around the world claimed that Python is the fastest growing programming language. According to a survey by Jetbrains, 69% of the developers use Python as their main language for machine learning. Due to its less complex behavior and ease of development, Python is rapidly stepping up the ladder in the tech world.

The coding simplicity and readability have led python to become the preferred choice of programmers. With the development of several modules like numpy, scipy & scikit-learn, among countless others, Python programming language has become the go-to language for developers when it comes to machine learning. A software development agency builds high-quality solutions in Python.

Leveraging Python for Machine Learning

In basic terms, machine learning refers to the ability of programs to learn by adapting to the environment. It is a form of Artificial Intelligence that portrays dynamic behavior when the dataset is changed. Due to Python’s easily understandable modules, developers prefer to implement machine learning with this language. The following points analyze how Python can be utilized for machine learning.

• Understanding Python basics Having a basic understanding of python skills is enough to skyrocket your machine learning implementation. Installing a Python implementation like Anaconda can help you with both scientific computing & machine learning. There are several tutorials available that teach python like Google developers python course, for the beginners in machine learning.

• Understanding data Machine learning involves experimenting with data. The developer must understand the dimensions, statistical attributes & breakdown of data according to the environment in which it will operate. Gaining a clear understanding of data sets will allow programmers to code software development solutions effectively.

• Evaluating scientific python packages A wide array of open source modules & python packages are available for programmers to integrate with machine learning. Depending on the operation, they can select the best module from these packages. Scikit-learn (for data mining), Pandas(for data frames), and numpy (for N-dimensional array) are some widely popular packages.

• Executing Python Using the Python application development and its standard machine learning library, the programmer can execute python. They also utilize iPython that gives an interactive environment to execute the language. Experimenting with different machine learning algorithms like k-means clustering and decision trees are a good step to kick-start your python journey.

• Taking Advanced Steps To make machine learning effective, you can dive deeper into the python environment. Giving attention to support vector machines, kaggle titanic competition & dimensionality reduction will augment your foundational machine learning skills. At later stages, learning neural networks can dramatically increase your accomplishments in machine learning solutions.

Why Agile Development Teams Tend To Be More Productive?

 Agile development and productivity for teams

Here’s an interesting fact from a study conducted by PwC – Agile projects are 28% more successful as compared to the traditional projects. Integrating an agile approach to workflow is at the top of every project manager’s priority list. Agile adoption not only results in increased productivity but also leads to happier employees. While working under agile teams, a developer feels less pressure, a greater synergy of efforts, and adapts easily to the fast-paced work environment in a custom software development company.

But What Exactly Is The Agile Approach To Workflow?

Agile development teams that provide software development services adopt a collaborative approach to project completion by recognizing the importance of cross-functional efforts on a specific project. From developers to designers & testers, everyone in agile methodology carries out more tasks than their responsibilities. Focusing all the teams’ efforts on a single project allows the team to meet deadlines with the maximum output. Employees welcome the agile approach wholeheartedly because it reduces the work pressure from their shoulders and allows them to complete sequential tasks. In fact, 50% of team members are motivated more by the success of their team rather than organizational or personal success, which are 27% and 23%, respectively.

Why Agile Adoption Leads To Higher Productivity?

Under agile, software development teams that provide It consulting services aren’t overwhelmed by the varied components of a project. They work on a specific task, unlike the waterfall development method, and then move on to the next. Distributing project components & focusing on singular task completion results in achieving faster & better performance.

• Improvement in quality The breakdown of the project into specific units leads to high-quality development, design & testing. Since the focus is largely on a single component, errors can be quickly fixed & the development team can make iterations with flexibility.

• Early project delivery With agile, timelines can be estimated for each task of the project. This allows accuracy in meeting deadlines and sometimes, results in the early delivery of the project. If companies use SCRUM, they can utilize fixed sprints to predict the timelines and work efficiently resulting in meeting their deadlines.

• A collaborative culture Agile developer teams work in unison to achieve a specific goal. This creates a culture of knowledge sharing & coordinated efforts. The SCRUM guide suggested no titles for team members & addressing everyone as developers. It frees every one of the specific responsibilities and stimulates them to adopt various roles for moving in a particular direction. Whether it’s Ruby on Rails consulting or Python development, collaboration is inevitable. 

• Prioritize projects By knowing accurate timelines with the agile method, the team cannot ignore the task at hand. They will work with determination & prioritize the project completion because there are no long-term projects that distract the team by demanding focus on varied components.

• Improved client satisfaction When the development team meets deadlines, delivers high-quality output & prioritizes the client’s needs, the result is utmost client satisfaction. The team focuses its efforts on creating a product that provides tremendous opportunities to the client in the market.

Thus, it’s not surprising that 71% of organizations sometimes, often or always use the agile approach to workflow management. Agile adoption ensures that the tasks become easier for the development team and the output becomes better for the clients. Measure your current productivity and give it a boost with agile development teams. Why Agile Development Teams Tend To Be More Productive?

Top Enterprise App Development Trends Using Java in 2021

In today’s times, who does not have a smartphone? Well! Maybe, just about a handful. And, even from amongst those handful, every day, there are those who are continuously giving up the resistance and becoming smartphone fans. It would be alright to say that smartphones are no longer merely phones, they can be compared to the call bell buttons that bring the assistants running to the caller.

This rising demand for mobile apps is responsible for new enterprise development trends that will majorly influence app development in the coming years. It must be kept in mind that what looks easy to us--- requires a lot of hard work and brain. Whether it’s Java application development or React native mobile development, the trends rise in every technology.

For these enterprise application development services come to fore. Enterprise application development services aim to build low cost and high performance enterprise applications.

What Do They Do?

These services help the customers in various everyday chores that include managing the content, managing workflow, managing documentation and business intelligence. Together with these, application development also involves modifications and upkeep of the existing apps. Java development services help to create such applications.

Life Made Easy with Java Enterprise Apps

Life surely is easy with apps. Whether it is a small shop or a big enterprise—all can make use of their customized apps. Say for example Java enterprise application development is specifically used by the developers to create apps for big enterprises. The key characteristics of Java enterprise application development is that it provides a pathway to secure app development and provides reduction in the intricacies of enterprise application. A Java software development company can help enterprises build interactive Java apps.

Likewise, enterprise application development can provide proper flow of communication within a company. Thus, the data required by the company of all its employees can be made available at one single place with custom enterprise application development.

Java Enterprise Apps to Watch Out for in 2021

Stringent Security As technology rises, so will the security threats. Until now several security issues have come up that have not just been related to stealing of data but also issues that have hindered a company’s enactment. Thus, now the focus will shift towards making the apps as secure as possible. Important files can no longer be stored in servers, where anyone can access them. The apps will become more stringent with security and will have enhanced user-friendly features.

Rapid Mobile App Development RMAD is a model that helps in developing high- quality products by prototyping and reusable software components. RMAD which is equivalent to RAD is faster and safer. Its code- free programming ensures that the enterprise users are able to build strong apps fast.

Citizen centric development Most of the present apps are complicated and difficult to use. Similarly, industries are loaded with complicated enterprise systems that are slow. Citizen centric development, also known as self- service development will make the industries self- sufficient and the reliance on IT will be limited to a large extent.

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