Key reasons to enroll for Python training

Here’re the most important reasons you should learn this high-level, general-purpose programming language.

• Use in data science: You’re probably aware of the fact that data science is being considered one of the most respected, prominent, and attractive career paths in the tech field these days. By completing a Python course you’ll be able to fuel your data science career as the language comes with a great number of frameworks and libraries such as NumPy, PyBrain, PyMySQL, SymPy etc that would make your job as a data science professional relatively easier.
• Use in artificial intelligence and machine learning: Similar to data science, Python is also being heavily used in machine learning as it helps to build algorithms utilizing statistics in order to enable computers to perform various actions. Some of the major Python modules that help in machine learning include Scikit-learn, Theano, Tensorflow, among others. With the help of libraries like Keras, you’ll be able to manage neural network experimentation as well.
• Simplicity: One of the biggest advantages of learning Python is its high simplicity. When you’re a beginner in coding and programming, generally you don’t want to begin with a language that comes with weird rules and tough syntax. Python isn’t only simple and readable, but it easier to setup as well as you don’t have to handle any compiler issues like C++ or classpath problems like Java. All you need to do is install Python and start exploring it.
• Robust community: Regardless of the language, a community always plays a highly crucial role when it comes to learning it. Python comes with a massive community that’ll always be there to lend you a helping hand whenever you get stuck with one or multiple issues. Today, you can easily find the solution of any of your Python related problems within minutes by doing a simple Google search.
• High prospect: As the language is growing steadily and is being adopted by a significant number of tech giants as their primary language, it makes lots of sense to obtain a Python certification in order to make your career future-proof. It’d not only make job-ready quickly but will accelerate the career growth as well.

Conclusion

Put simply, by completing a Python course you’ll never have to face a shortage of options to utilize your skills. And since a lot of tech giants rely on the language, you’ll always be in a position to make good money as a Python certification holder.

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What can data science do in the future?

Data science is almost an indefinite pool of diverse data operations by leveraging the power of which a data scientist should be able to accomplish the following in the future.

• AI will become omnipresent: Most of us have seen the power of AI applications in the fields of automation and robotics to some extent. But in the next few years, data science professionals are expected to take AI to such a level that robots will be able to do almost everything – from cleaning the houses to running a business. Probably you’ve seen that Google Assistant has already reached a level that it can make phone calls and book reservations in restaurants and in the upcoming future, its activities will become more specialized and broaden even more.
• Advanced personalization: With billions of users across the globe using smartphones, the amount of data being generated seems to be even more massive in the next few years. Therefore, businesses, with the help of data scientists, will be able to maximize the value of data in an even better way that’ll help them address their user-base more thoroughly.
• More accurate predictions: We can expect big data analytics tools to become so advanced that data science professionals will be able to help in real-time decision making. As a result, many key functions could be predicted with much higher accuracy than they’re being done today.

Apart from the above, we can expect to see more specialized career paths evolve. With advancements in the field, the overall status of data literacy will likely to improve across the workforce where employees other than data science professionals will obtain a better understanding of the usage of data. And thus, the future of data scientists would probably become even more specialized, handling the most complex and business-critical challenges which will help their companies become even more successful in their respective fields.

Conclusion

Today, it can be safely said that data scientists will have a prominent future and the field will stay for years to come. If you’re thinking of pursuing a data scientist career, perhaps this is the best time to start your journey. Magnimind Academy’s data science bootcamp in Silicon Valley helps students to become future-proof data scientists with unique combination skills which will be always be in great demand.

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1- What is generalization?

The term ‘generalization’ refers to the model’s capability to adapt and react properly to previously unseen, new data, which has been drawn from the same distribution as the one used to build the model. In other words, generalization examines how well a model can digest new data and make correct predictions after getting trained on a training set.

How well a model is able to generalize is the key to its success. If you train a model too well on training data, it will be incapable of generalizing. In such cases, it will end up making erroneous predictions when it’s given new data. This would make the model ineffective even though it’s capable of making correct predictions for the training data set. This is known as overfitting. The inverse (underfitting) is also true, which happens when you train a model with inadequate data. In cases of underfitting, your model would fail to make accurate predictions even with the training data. This would make the model just as useless as overfitting.

2- The ideal solution

You would ideally want to choose a model that stands at the sweet spot between overfitting and underfitting. To achieve this goal, you can track the performance of a machine learning algorithm over time as it’s working with a set of training data. You can plot both the skill on the training data and the skill on a test dataset that you’ve held back from the training process. As the algorithm learns over time, the level of error for the model on the training data would decrease and so would the error on the test dataset. Training the model for too long would cause a continual decrease in the performance on the training dataset due to overfitting. At the same time, due to the model’s decreasing ability for generalization, the error for the test set would start to increase again. The sweet spot is the point just before the error on the test dataset begins to rise where the model shows good skill on both the training dataset as well as the unseen test dataset.

To limit overfitting in a machine learning algorithm, two additional techniques that you can use are:

• Using a resampling method to estimate the accuracy of the model
• Holding back a validation dataset

So, during your machine learning training, keep an eye on generalization when estimating your model accuracy on unseen data.

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If you’re planning to get enrolled in a Python course or online Python training program, knowing some real-world applications of Python would help you choose which specific field you want to focus on for your career. Here are the top three applications of Python:

1- Web and Internet Development

Using Python, you can easily develop web applications rapidly. Python has a number of libraries for internet protocols like XML and HTML, e-mail processing, JSON, IMAP, FTP, etc. You can even use the following libraries for a range of functions:

• BeautifulSoup – for parsing XML and HTML  documents
• Requests – a client-side HTTP library
• Paramiko – For executing the SSH2 protocol
• Feedparser – for downloading and parsing Atom/RSS feeds
• Twisted Python – for asynchronous network programming

With widely used Python frameworks like Django, Pyramid, and Flask, you can enjoy unparalleled convenience, scalability, and security when working on web development compared to starting website development from scratch. With Python, you can also get microframeworks like Bottle and Flask. You can even write CGI scripts and get advanced CMSes (content management systems) such as Django and Plone CMS.

2- Machine Learning and Artificial Intelligence

Those eyeing a career in data science, machine learning (ML), and AI (Artificial Intelligence) often take up a Python certification program to fast-track their career. Apart from being extremely user-friendly and easy to earn, Python has multiple libraries that offer support for these domains. Some of these are:

• TensorFlow – an end-to-end Python ML library for performing high-end numerical calculations.
• Scikit-learn – a well-known open-source Python ML library with a wide range of clustering, classification, and regression algorithms.
• Keras – a leading open-source Python library for building neural networks and machine learning projects.
• Theano – a renowned scientific computing library that lets you classify, optimize, and assess mathematical expressions that deal with multidimensional arrays.
• PyTorch – a production-ready Python ML library with outstanding applications, examples, and use cases supported by a robust community.

NumPy, Seaborn, and Python Pandas are some other libraries that you can use.

3- Game development

Several interactive games (like Disney’s Toontown Online, Civilization-IV, Vega Strike, etc.) and 3D graphics have been developed using Python. Libraries such as PyGame (that provides functionality via computer graphics and sound libraries) and PySoy (a 3D game engine that supports Python 3) as well as frameworks for game development like PyGame and PyKyra make game development an easy and quick job.

Final words

Education, business (e-commerce and ERP systems), software development, desktop applications, and database access are some other domains where you’ll find the use of Python. So, take your pick from these fields of Python applications when you enroll for a Python certification program. 

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Types of blockchain technologies

Originally created for the digital currency Bitcoin, the blockchain technology has now opened up a new era for the tech community with its lots of other potential uses. Major blockchain technologies are distributed into three categories – private blockchain, public blockchain, and hybrid blockchain. Let’s have a detailed look at each of them.

1- Private blockchain

These are permissioned blockchains that don’t let any user to join the network freely and read/write to the ledger. It comes with an access control mechanism that remains between the users’ list connected to the network. Private blockchain is widely popular among companies that are likely to record transactions securely and interchange critical information between one another. It’s important to note that in this type of blockhains everyone is aware of the users’ identity but only those with appropriate permission can see the transactions. Additionally, as the consensus process doesn’t involve every user, a private blockchain offers higher throughput than a public blockchain.

2- Public blockchain

This is an open-source blockchain and allows everyone to participate as users, developers, miners, or community members. All transactions taking place on this blockchain remain fully transparent, which means anybody can review the transaction details. These blockchains are designed to be completely decentralized, which means transactions that are being recorded in the blockchain or their processing orders can’t be controlled by any single entity or individual. A public blockchain offers anonymity that promotes users’ privacy. A user isn’t supposed to disclose any type of personal information before submitting smart contracts and transactions.

3- Hybrid blockchain

You can think of a hybrid blockchain as a unique type of blockchain that attempts to utilize the best parts of both public and private blockchain solutions. The biggest distinguishable feature of this blockchain is that this isn’t open to everybody but still comes with major characteristics like transparency, security, and integrity. Here, the members can decide which transactions are to be made public or who can join the blockchain. This enables organizations to work with their stakeholders in the best way possible.

Final Words

The blockchain technology has dramatically improved the fidelity and confidence between parties involved in a blockchain, while breaking down the workflow. The job market in this field, ranging from established organizations to startups, looks bright for blockchain enthusiasts and we can expect it to grow exponentially in the next few years. So, if you’re looking for a field that would help you kick-start your tech career, you can think of learning the blockchain technology.

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1- Guiding actions based on trends

One of the key responsibilities of a data scientist is to examine and explore the data captured by the organization. Once these processes are done, he/she can recommend different types of actions which bring a huge scope of improvement in the business performance of the company. And after these improvements are made, it can leave a significant impact on the organization in terms of increased profit.

2- Informed decision making

With the help of a data scientist, it has become possible for business owners to predict effective measures and different trends for the success of their businesses. One of the biggest data science benefits is that it has eliminated the possibilities of upper-level risks. By reviewing different types of models created by data scientists based on already existing data, business owners can easily understand which road will lead them to success.

3- Testing the decisions

Once you’ve implemented the changes based on the insights discovered by a data scientist, it’s time to observe how these changes are impacting your business. And this is exactly where the expertise of a data scientist becomes evident again. He/she would be able to measure the key metrics which are related to those changes and quantify their true impact.

4- Real-time data

Once there was a time when marketers used to collect the info about their consumers in bulk after every campaign and analyze that information to track the progress of the campaign. But the emergence of data science has opened up a whole new field of digital marketing. Now you can build your present and future digital marketing campaigns based on real-time data, which means you don’t need to analyze distant past behavior anymore. Instead, you can focus on the present market patterns to make your campaigns highly effective. A data scientist can tell you everything about your target market trends, customer response, their buying patterns, the effectiveness of timing, and much more, helping you target your consumer base at the right time.

Parting Thoughts

These are only some of the major data science benefits that any business would be able to experience by hiring a data scientist. It’s also safe to say that the importance of these professionals will only increase over time, thanks to the increasingly connected world. If you’re an aspiring data scientist and looking for a great start, this data science bootcamp in Silicon Valley offered by Magnimind Academy would be worth checking out.

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1- Machine learning in Java

There’s a misconception that without learning Python or R, you can’t succeed in machine learning. However, the truth is that if you’ve got a Java development background, you can do without learning these popular programming languages. You should remember that Java gives support for development in any field you want, and data science is no different. By using third-party open source libraries, you can leverage your expertise as a Java developer to implement a data science algorithm and get things done. Though there’s no denying that Python or R come with their own set of advantages, you won’t need to learn them specifically to execute machine learning- or data science-related algorithms.

2- Leading machine learning libraries for Java

If you’re looking for some of the best machine learning libraries for Java, you’ll find Weka to be the most popular choice. Weka is suitable for data mining tasks, where algorithms can either be called from your own Java code or applied directly to a dataset. Weka contains tools for functions like clustering, classification, regression, association rules, and visualization.

Apache Mahout is another machine learning library for Java, which is designed to be enterprise-ready. This scalable and flexible ML framework comes with in-built algorithms to help you create your own algorithm implementations. Mahout’s distributed linear algebra framework allows statisticians, mathematicians, analytics professionals, and data scientists to implement their own algorithms.

ADAMS (Advanced Data mining And Machine learning System) is a flexible workflow engine that uses a tree-like structure to manage how data flows in the workflow. This means there exist no explicit connections that are essential. Using ADAMS, you can quickly build and maintain real-world workflows that are generally complex in nature.

Some other machine learning libraries for Java are ELKI (Environment for Developing KDD-Applications Supported by Index Structures), Deeplearning4j, JavaML, MALLET (MAchine Learning for LanguagE Toolkit), JSAT (Java Statistical Analysis Tool), and RapidMiner, to name a few.

If you’re a Java programmer or are adept in Java, the fastest route to a career in machine learning is enrolling in a machine learning bootcamp. Taught by industry experts and having ample hands-on training, such a bootcamp will help you fast-track your machine learning career dreams.

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1- What’re neural networks?

In simple words, neural networks can be considered mathematical models loosely modeled on the human brain. Neural networks engage in two distinguished phases. First, comes the learning phase where a model is trained to perform certain tasks. These could be how to perform language translations or how to describe images to the blind. And second comes the application stage where the trained model is utilized. You can think of Spotify sending you a weekly-playlist created by analyzing your music taste. Neural networks come with some fundamental building blocks that include neurons, input, outputs, weights, and biases. Here, each neuron comes with one or multiple inputs together with a single output.

You can use this output as an input to one or multiple neurons or as the entire network’s output. The most intelligent thing about neural networks is the self-learning during the training period of the models. Here, a neural network is given a dataset of inputs (could be text, speech, or images – but everything has to be translated to numbers) and a true answer accompanying every observation set. Now the model learns to find out the true answer based on the inputs it has been presented with. Throughout the learning process, the model would estimate second-hand-values continuously and compare those to the true values. If there’s a large difference, the model parameters get automatically updated to push those estimates closer to true second-hand-values. This process gets repeated until the average difference between true and assigned values becomes adequately small.

2- What’s deep learning?

You can think of deep learning as the absolute cutting edge of AI (artificial intelligence). Here, the machine trains itself to process, as well as, learn from data. With deep learning, you don’t need to teach machines to process and learn from data, which is the working method of machine learning.

Parting Thoughts

The difference between deep learning and neural networks remains in the model’s depth where the former phrase is used to mention complex neural networks. A deep learning system is simply a self-teaching one that keeps on learning by filtering information via multiple hidden layers, much like the way the human brain works. It’s being assumed by some people that deep learning will automate a significant number of tasks and might replace many human workers in the future. But it’s also important to understand that implementation of deep learning might replace someone who works on repetitive, manual tasks but it just can’t replace the engineer or the scientist developing and maintaining a deep learning application.

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1- An overview of data mining applications

If you’ve made up your mind to learn data mining, here are some applications of it, knowing which would help you choose your career path:

• Market Basket Analysis: This modeling technique is based upon a theory that buying a specific set of articles makes you more inclined to buy another group of articles. A retailer can use this technique to recognize a buyer’s purchase behavior, which can then be used to tweak the offerings or change the store’s layout to encourage buying. The use of differential analysis comparison of results between customers in different demographic groups or even between stores at different locations can help retailers make the necessary changes to boost sales.
• Fraud Detection: Unlike time-consuming and complex traditional methods of fraud detection, data mining can help find meaningful patterns in data. This information can then be used to collect sample records, classify them as fraudulent or non-fraudulent, and build a model using this data where the algorithm is designed to spot whether the record is fraudulent or not.
• Future Healthcare: Using data mining approaches such as machine learning, multi-dimensional databases, statistics, data visualization, and soft computing, the number of patients in each category can be predicted. This can help develop processes that ensure each patient gets appropriate care at the right time and the right place. Apart from helping to improve healthcare and reduce costs, data mining can also assist healthcare insurers in identifying fraud and abuse.
• Education: EDM or Educational Data Mining helps predict the future learning behavior of students, and even lets you study the effects of educational support as well as how advanced scientific knowledge can help in learning. Thus, data mining can help educational and training institutes focus on what they should teach and the ways to effectively teach apart from predicting the results of the students.

Apart from the above, many other industries like banking, transportation, manufacturing, etc. can also gain from data mining and data science.

2- How to learn data mining?

If you have your eyes set on the field of data science and want to master data mining, you can either get enrolled in a full-time course or find some bootcamps to join where you’ll learn all that you need to, albeit much faster than a traditional course. In case you plan to use bootcamps, remember that you’ll need a good statistical and machine learning foundation to understand what’s being taught and apply this knowledge to get useful information by cutting the noise of Big Data.

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1- The three main data science skill sets you need

Even if you don’t have a computer science background, you will need the three main data science skill sets namely programming, statistics, and business knowledge if you aim to have a successful data scientist career.

If you plan to become a data scientist, you’ll need to use programming skills to handle data at scale that can fill terabytes of space. You’ll also need a solid grasp on statistics and mathematics to evaluate patterns in data and manipulate it using different methods. Understanding business fundamentals is an equally important skill to ensure you’re capable of communicating your findings to the concerned teams or management people and encourage them to make informed decisions based on such data-driven insights.

Though you’ll need a diverse skill set to excel in the field of data science, you don’t need to worry as most data scientists won’t have picked up all of these skills in an academic environment. This indicates there’s often a lot of self-learning involved in the process, which would be advantageous for you, especially if you don’t have a computer science or statistics/math background.

You should remember that if you can prove through project work that you’ve got serious data science skills, it won’t matter whether you acquired them on your own, through a formal degree program, or via a data science bootcamp in Silicon Valley.

2- The fast-track to becoming a data scientist

Online and offline bootcamps typically offer a mentor-guided curriculum tailored to get you working with data from day one. With industry experts and experienced data scientists as mentors, who use real-world data to teach you, you’ll get your hands on real-world data from the beginning of the program. You’ll even get hands-on, project-focused classes that prepare you for data science employment by the time you end the training.

Unlike lengthy traditional degree courses, these bootcamps offer extremely targeted learning that demands you stay committed to your studies and invest 15-20 hours (or even more, at times!) regularly right from the beginning. With a hands-on learning approach, these bootcamps make you work with real data sets to analyze interesting problems and even give you additional opportunities for guided real-life projects.

When you join a leading data science bootcamp in Silicon Valley, you’ll not only get real-world experts as your mentors but even get the chance to learn and grow via peer interaction. When working on group projects with other aspiring data scientists, you’ll be able to ask questions, brainstorm to find solutions, and even learn from your peers.

If you plan to become a data scientist but don’t have a computer science background, find a data science bootcamp in Silicon Valley to make your dream come true.

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