With advancement of technology, one field that will be highly demanded in upcoming years is turning up to be Artificial Intelligence. It is bringing changes that is transforming the world. AI comes with its sub streams such as data mining, machine learning, neural networks etc. This field has already become the area of interest for many programmers and developers. However, still there are not many developers in this stream. 

Schools, Colleges and Organizations have started providing courses on AI. It is one of the best career option. But Artificial Intelligence is just a main stream. One should have a clear mind about his career opportunities. Following are few options you can opt if interested in Artificial Intelligence and want to have a career towards it:

1. A.I. Research Scholar
2. A.I. based Software Developer
3. Data Scientist
4. Machine Learning Engineer
5. Automation Engineer

To know more about them visit: https://www.technotification.com/2018/04/top-5-career-opportunities-in-artificial-intelligence.html

The buzzwords in technology are no new to someone. Whether it be Artificial Intelligence, Machine Learning, Data Sciences or Analytics, each of these are invading in our lives promising us better future. However, it is believed that expertise interested in data sciences are not widely spread. Data Sciences is a field that can improve business, can help in other technological fields, can help in decision making and more. 

It is rightly said that prevention is better than cure. A wrong step in data sciences can affect the decisions and the results. One should avoid the following mistakes while dealing with data:

1. Assuming your data is ready to use and all you need
2. Not exploring your data set before starting work
3. Not using control group to test your new data model in action
4. Starting with targets rather than hypotheses
5. Automating without monitoring the final outcome

To study mistakes like these read https://www.cio.com/article/3271127/data-science/12-data-science-mistakes-to-avoid.html?nsdr=true

One of the most interesting thing about the field of technology is that it never stops growing. There are changes that helps in evolution. The rate at which technology is growing is unmatchable and the only way to match that pace is by polishing our skills and keep them up to date.  The following are the top 3 tech skills that are need for tomorrow: 

1. Blockchain Technology – Blockchain is the structure of data that records transactions. It is digitally signed and thus ensure its authenticity. It is a good way to manage cryptocurrency.
2. Artificial Intelligence – Artificial Intelligence is an ongoing technology which is helping humans by making machines intelligent and capable of working the way humans do. Though they are many applications based on AI which we are already using but still there are many unexplored technologies.
3. Augmented and Virtual Reality- Augmented Reality and Virtual Reality have already shown remarkable progress in the field of gaming and there are many more applications which can bring tremendous changes.

To know more visit: https://www.technotification.com/2018/04/top-3-tech-skills.html

Machine Learning is an ongoing trend in the field of technology. However, there are only few machine learning programmers available right now. For beginners who are eager to learn and work on machine learning must work on algorithms. With machine learning algorithms, there is no need of human intervention.  There are different algorithms which will work for you. 

There are basically three types of algorithms:

1. Supervised Algorithms: which uses labelled datasets for training algorithms
2. Unsupervised Algorithms: which uses unstructured datasets for results
3. Reinforcement Learning: it uses feedbacks in order to reinforce a behavior

There are top 10 algorithms of machine learning that are must known for machine learning programmers:

1. Linear regression
2. Logistic regression
3. Classification and regression tree
4. Naïve bayes
5. KNN
6. Apriori
7. K-means
8. Principle Component Analysis
9. Random Forest
10. AdaBoost

Know more about them at https://www.technotification.com/2018/05/top-10-ml-algorithms.html 

Artificial Intelligence is making our lives better each day. It has also spread its wing in the field of Academics and made it more convenient. With computers and other smart devices, technology is making education more accessible to students. Artificial Intelligence is not only helping students but also automating and speeding up administrative tasks helping organizations by saving time. It is believed that soon AI in education industry will grow by 50%. Below are the four ways in which AI is helping education industry to grow:

1. The automation of administrative work
2. The addition of smart content
3. Smart tutors and personalization
4. Virtual lecturers and learning environment

Read more about them at https://towardsdatascience.com/4-ways-ai-is-changing-the-education-industry-b473c5d2c706

A software project always consumes company resources. Whether it be the employees or days, working on a software project is a tough task and meeting its requirements becomes prime motive for a company. However, even after applying so much efforts, many software projects come to their end before they are released or leaves the costumer dissatisfied. This failure often leaves company and clients in disguise and employees begin to look for explanation why it went wrong.

There could be many reasons behind this. Following are the top 7 reasons:

1. Too few team members
2. Fundamental feature changes
3. Picking Wrong Technology for the job
4. Poor Prioritization
5. Bad Architectural Decisions
6. Unrealistic Deadlines
7. False Belief in The Power of Software

There can be many more reasons behind this. A company must cross check them to ensure success of a software.

To know more about the reasons visit https://www.cio.com/article/3282464/application-development/14-reasons-why-software-projects-fail.html#tk.cioendnote

What could be better than knowing what future lies ahead us? Predictive Analysis is one such branch of data analytics which can be used to make predictions of future unknown events and is growing with a rapid pace. On the other hand, innovation is an ongoing process which finds its application in almost every field. Without innovation, we would not have reached the platform at which we are now. A number of technological achievements have improved our lives.

These days, Innovation has found a guide in Predictive Analytics that helps to walk towards success.  Many innovations are made but majority of them never succeeds. Predictive Analytics is going to play an important role aiming towards new products ensuring greater economic stability and progress in coming years. 

To know more about how predictive analysis can help in innovation read https://www.smartdatacollective.com/predictive-analytics-methods-make-innovation-successful/

Artificial Intelligence is the trend and need of this hour. It has already found its applications in many fields. This technology is changing and improving the world at a tremendous speed and for our betterment. There is no doubt that AI is future. However not many of us knows its basic application in Business. Business needs time to time changes to meet the requirements. AI can help and change business in many ways.

Top five way in how Artificial Intelligence can help and upgrade your business are:

1. Cheaper Analytics
2. Hiring
3. Customization
4. Anticipation 
5. Security

Know more about it at:  https://www.techrepublic.com/article/top-5-ways-ai-will-change-business/

In this world of competition, companies need to take advantage of available data and take a look about what might happen in future. Predictive Analysis is one such branch of Data Analytics that aims to make predictions about future outcomes using various algorithms and other data analytics tools. Methods like data mining, big data, machine learning are back bone of Predictive Analysis and organizations are able to decode patterns and relations which helps them to detect risk and opportunity. Financial Services, Law Enforcements, Automotive, Healthcare are few fields which have already adapted this technology. 

To know more visit: https://www-cio-com.cdn.ampproject.org/c/s/www.cio.com/article/3273114/predictive-analytics/what-is-predictive-analytics-transforming-data-into-future-insights.amp.html

Whether you are a data analyst in a firm or a developer training its machine learning model, you deal with data. Rather you need data! Data is one of the essential things which is needed to create a foundation. The decisions and results are relied on the output you get from the data. Thus, data is important and like every other thing, it also works on the principle of Garbage In, Garbage Out.

Many people make mistake while feeding data to their data set with a hope to get better results.

However, they end up having an ugly dataset with a greater risk of damaging their product.

The 6 most common mistakes are: Not Enough Data, Low Quality Classes, Low Quality Data, Unbalanced Classes, Unbalanced Data, No Validation or Testing.

These mistakes can be fixed which could further help in fetching good results.

One just need to remember that their dataset is equally important to the model they are working on. Without a balanced dataset, getting a fine finish product is next to impossible.

To know how to fix those mistakes visit: https://hackernoon.com/stop-feeding-garbage-to-your-model-the-6-biggest-mistakes-with-datasets-and-how-to-avoid-them-3cb7532ad3b7

Classification of data is very important in many organizations. They can be used to make decisions. But the task of classification can be very tedious. Now imagine a machine doing this job. Classification using machine learning is with the help of supervised learning approach and algorithms. Machine learns from the data input given to it and with the help of this learning, it classifies new observation.

For example, we want to check number of male and female members in an organization. Here we can train our machine to do this classification. 

Classification using machine learning is one of the trending technologies being used in various fields. It has many applications in many domains other than IT.

Various algorithms can be used to implement classification. There are two types of learners in classification – 
Lazy Learners - which simply store the training data and wait until a testing data appears. They classify the data based on most related data.
Eager Learners – that construct a classification model based on given training data.

Different classification algorithms are – Decision Tree, Naive Bayes, Artificial Neural Networks, K-nearest neighbor.

Read more about them and various evolution methods at https://towardsdatascience.com/machine-learning-classifiers-a5cc4e1b0623

Artificial Intelligence, Machine Learning and Deep Learning are relatively newer technologies invading the fields of information technology, business etc. Though developers are walking towards this era, currently the number of experts is relatively less. The company often makes mistakes by starting up with the technologies instead of focusing on business needs. They often make mistakes by assigning out of domain work to some. For e.g. Hiring data scientists and asking them to build something interested from given database. Rather than a team must be formed of product managers, data engineers, data scientist and DevOps engineers.A team of four will be a kick start to improve our process and giving better results. Now everybody has an opportunity to improve the models, optimise the deployment and scale the business. 

Talking about ML, many projects fail due to complex structures. This could occur because of working on wrong problem, to having wrong data, failing to build a model or failing to deploy it correctly. Read more at: https://medium.com/@guyernest/the-flywheel-of-machine-learning-systems-50aa6d992382

Internet is a vast place where one could get and post information globally. Many search engines help you to find what you want using different search algorithms. Ever since the first search algorithm was discovered, many new searching algorithms are being invented and used to make searching process easier. However, there are times when text-based searching becomes really exhausting. Take an example of flower. You are very fascinated by a flower you saw in wilds and is very curious to know about it. You start searching about it using it properties like colour of petals, number of petals, description of leaves etc. This would be very tedious and still there is no surety whether you will get results or not. 

Now imagine for searching with the help of picture. You just click a picture and rest will be done for you. This is known as Visual Searching and to achieve that Machine Learning is used. This type of searching can be extensively used in various domains. Initially a large amount of dataset will be required to train your machine. However, by using the concept of neural networks, this could be achieved and used. Read more at: 

Read more about it at https://medium.com/gsi-technology/ml-in-visual-search-part-i-d54cf4f2b509

Digitalization has become a global movement and initiatives are being set to accelerate the rate of adoption. 2030 has been declared as the target and many initiatives are already on their road. One such automation process that can that can change everything significantly is the business automation process. From improving customer experience to cutting down cost, business automation has the power to help the firm grow. 

However, automation efforts are only beneficial if the CIOs make right investments and decisions. It is often seen that with a wish to automate, many leaders often make wrong decisions which could cost them potentially higher than excepted.

The three business goals that investments in automation will help are:

1. Digitalize and optimize operation
2. Create and act upon advanced insights
3. Drive business technology innovation.

Read about them at https://www.techrepublic.com/article/how-to-automate-your-business-3-critical-steps/ 

Data Science is a new field of interest and used in every sector. Whether it is a business, production line or a tech company, each of them wants someone to analyse their data. This would further help them to make decisions. Even though there is so much need of data scientist, still the number of data scientist is low. There are many characteristics that could define a good data scientist. 

Few of them starting with C are: Curious, Careful, Clever, Confident, Creative, Capable, Communicative, Considerate, Candid and Collaborative. 

To know further about these words visit: https://medium.com/@tableaucoach/characteristics-of-a-data-scientist-ten-cs-4e3b185cc7cd

Nowadays, Artificial Intelligence is not new to this world. AI is used in almost every field, especially in the field of business. To make business related decisions, you need data. This data is analysed and then plans and actions are decided. From identifying the problem to discover actionable business insights, right analysis of data can transform business operations and take it to higher level. 

However, with the power of AI you can automate this task of data analysing, transforming raw data into actionable business intelligence. One thing for which Artificial intelligence is hungry for is data! The more data you feed it, the better results it gives to you. But with the vast amount of data that AI requires, it also follows the concept of “Garbage in, Garbage out.” Feeding the right thing to AI should be the up-most priority to get results. For this reason, many companies are making changes in their data management space.

Read more at : https://www.technotification.com/2018/05/ai-changing-enterprise-data.html

Machine Learning is one of those technologies which have invaded in our lives to make it better. Without any doubt one can say that even though machine learning is in its initial phase, it has already become a part in our 24/7 running lives. Set of algorithms to use data, learn from it and then forecast future trends for that topic is expanding day by day.

Machine Learning and Data Sciences are often used together in order to predict future from varied data results available with us. One of the famous algorithm used in this field is SVM or Support Vector Machine which can be used for both regression and classification task. It uses the concept of hyperplanes and other mathematical functions in order to produce significant accuracy with less computation power. SVM has already proved itself in text categorization, image recognition, and in bioinformatics and now working in other.

To know more about how SVM works visit : https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47

Artificial Intelligence, Machine Learning and Deep Learning are now the most exploring topics for any techie. In spite of enough differentiation between these terms, they are often used interchangeably. To put an end to this confusion one could say that ML and DL are nothing but cousins of AI. 

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Many applications of AI are being seen and used today. From voice-powered personal assistants like Siri and Alexa to self-driving cars and many more are applications of AI.

On the other hand, Machine learning is an artificial intelligence (AI) that is discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience.

Whereas, deep learning is a subset of machine learning which is a collection of algorithms used in ML to build and train neutral networks and act as decision making nodes.

So, though AL, ML and DL are interrelated but in this vast field of technology they all stand on their own and using them interchangeably would not be justice.

Read more at: https://towardsdatascience.com/cousins-of-artificial-intelligence-dda4edc27b55

It is said that with the future of technology another thing that is coming nearer to us is the risk of human extinction. Raymond Kurzweil, Google’s Director of Engineering and Futurist, published a book, The Singularity is Near, in which he welcomes a future in which AI will overcome human intelligence. Though we are still know that as super intelligence, but for now we do encounter AI in almost every aspect of our lives. Though in his book, the author welcomes AI as positive, most people fear job loss and the worst as human extinction. History indicates that any advancement in technology not always turns out to be bad. But, one could not ignore that with current technologies almost 45% of the jobs for which we are paid are now automated. This has become a topic of debate and no one knows what stands for us in advent of future. To know more visit: https://medium.com/@tjajal/automation-and-the-rise-of-meaningful-work-d1c7d596fee6

Now a day’s evolution and invention can be seen in every field. One thing common in these fields is the need of management. Any project is successful only if a proper management plan is adapted. Here, one evolution turned out to be a mirror of legacy. Agile approach is significantly opposite of the traditional waterfall approach. 

Waterfall Approach, also known as Liner Sequential Life Cycle, is highly structured and sequential in nature whereas Agile methodology is a practice that helps continuous iteration of development and testing in the software development process. 

Both of these approaches have their own strengths and flaws. Thus, it becomes important for a business to identify and select most appropriate approach.

To know more visit: https://www.itbusinessedge.com/articles/choosing-between-the-two-approaches-to-project-management.html