As the phrase goes, “everything that can be digitized, will be digitized”, is fast replaced by “If something can be run by algorithms, it will be”. Algorithms are supposed to be performing the following tasks: • Reading resumes: With natural language processing, resumes can be read faster and with more careful eyes. • Using spreadsheets: Soon the analysis made by experts using spreadsheets would be taken over by AI. • Hiring consultants: Since the analysis will all be done by algorithms, hiring consultants is really not needed as before. Hence, for coping up with the changes, one needs to get acquainted with the programs, rent a machine learning expert to design algorithms or make it on your own and invest for the future by learning new software. Read more at:https://www.experfy.com/blog/algorithms-are-replacing-leadership-strategies

Machine Learning and Deep Learning both uses the algorithms fed into them. While in the first, the algorithm needs to be told how to make accurate prediction, in the latter, the algorithms are fed via neural networks, making the operation similar to a human brain and involving lower chances of mistakes as compared to Machine Learning. While Machine Learning gives result for a numerical and text field, Deep Learning also enables face, voice and handwriting recognition. Also, with new data fed into the system, the accuracy rates by Deep Learning are much more than by Machine Learning. Although Deep Learning is anyday better than Machine Learning, Machine Learning plays a vital role in the existing economy. Read more at https://www.analyticsindiamag.com/understanding-difference-deep-learning-machine-learning/

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 

We may be decades away from robots attending us at the hospitals, but the influence of AI technology in the medical field have arrived. It’s a known fact that in AI, Machine Learning (ML) is considered to be the best approach but most of the AI solutions concerning medical sectors are not an example of ML. They are generally using the algorithms that are created by humans. Then what exactly is happening with AI in the Medical Field? 

https://www.wired.com/story/this-computer-uses-lightnot-electricityto-train-ai-algorithms/

Microsoft is all set to infuse AI onto Raspberry Pi, a tiny device. They are working on systems that can run machine learning algorithms on microcontrollers as small as a speck of red pepper flake. If not totally tiny, there are devices such as sensors in the current scenario that can collect data and send it to machine learning models running in the cloud. However, the disadvantage of this is that the processing requires a lot of power in data crunching along with occupying a lot of storage space. This is where the team at Microsoft is playing a big role. The only hitch is to get neural network in as small as a breadcrumb sized micro controller. The entire research process is in line with Microsoft’s growing indulgence in the area of AI and machine learning. Read more at:  http://analyticsindiamag.com/making-tiny-bits-smart-infusing-ai-onto-raspberry-pi/

Subsets of AI are diversifying and algorithms are growing advanced. AI had an alarming impact in many instances. Certain applications of AI are called black box because it is difficult to understand how the result have been generated. Decoding the black box technique involves optimizing a given function in isolation, and sharing it as necessary. This makes the work a lot easier and scales the data. Firms need to make people aware of AI's applications in order to make it more transparent. AI cannot be completely trusted with certain applications. In future, we have to embrace AI and develop trust on it because it has many advantages and black box is a positive step in this direction. Read more at: http://analyticsindiamag.com/making-sense-black-box-artificial-intelligence-trust-ai-completely/

Machine Learning is the backbone of today’s insights on customer, products, costs and revenues which learns from the data provided to its algorithms. And hence algorithms are the next most important thing in data science after data.
Hence , the question which algorithm to use ? Some of the most used algorithms and their use cases are as follow :

1) Decision Trees - It’s output is easy to understand and can be used for Investment decision ,Customer churn ,Banks loan defaulters,etc.

2) Logistic Regression - It’s a powerful way of modeling a binomial outcome with one or more explanatory variables and can be used for Predicting the Customer Churn, Credit Scoring & Fraud Detection, Measuring the effectiveness of marketing campaigns, etc. ,

3) Support Vector Machines - It’s a supervised machine learning technique that is widely used in pattern recognition and classification problems and can be used for detecting persons with common diseases such as diabetes, hand-written character recognition, text categorization, etc. ,

4)Random Forest: It’s an ensemble of decision trees and can solve both regression and classification problems with large data sets and used in applications such as Predict patients for high risks, Predict parts failures in manufacturing, Predict loan defaulters, etc.

Hence based on your need and size of your dataset , you can use the algorithm that is best for your application or problem.
You can read the full article by Sandeep Raut at http://www.datasciencecentral.com/profiles/blogs/want-to-know-how-to-choose-machine-learning-algorithm

Contextual Integration refers in identifying meaningful relationships between different information types. This gives a multi-dimensional view of the data rather than a single access point. The best approach is to analyze these volumes of data from different perspectives. The traditional way is to follow a fragmented approach. The web teams, marketing and sales team will look at the different statistics offered by data. This lengthens the time to take decisions and also introduces inaccuracy. The need is to look at data from many angles to create a multi- dimensional profile of the customer. Then predictive analytics can assess and lead to intelligent messaging. Machine Learning is also helping to improve these predictive analytics algorithms by checking it on the real time data. Read more about it in the article written by Dominik Dahlem (Senior Data Scientist at Boxever) at: http://data-informed.com/contextual-integration-secret-weapon-predictive-analytics/

Machine Learning is getting easier and accessible because of the computing power becoming affordable. Moreover, big enterprises are making their algorithm open source. This is because data is the food. More data an algorithm gets, the better it becomes. But from step 1, making algorithms, feeding data in humans play a significant role. Sometimes there are outliers which the algorithms cannot interpret. Here human intervention is necessary. They manually check such pieces. But when these are fed into algorithms, they make them robust by identifying outliers. Thus, human intervention is both necessary for accuracy and training. Read more at: http://insidebigdata.com/2016/01/11/human-in-the-loop-is-the-future-of-machine-learning/

Firms often spend a huge amount of money on display marketing. But as a consumer, do you remember at all the last display ad you saw. The answer is NO. Display Marketing can be very effective if targeted. It is important to know which item should be placed on which site and what part of the site. Most vendors don't go into details of the strategy, thus shelling out more money out of those interested in display marketing. Algorithmic attributions can be used to determine how the impressions will impact the ROI. Learn more about it in the article written by Sandy Martin (Sr. Business Consultant) at : http://blogs.adobe.com/digitalmarketing/analytics/how-to-get-more-from-display-marketing-with-analytics/

Machine learning has showed tremendous potential to transform companies from inside out. Everyday new algorithms are coming up that are being used to encounter data and tackle new problems. On the other hand, a closer look at machine learning reveals it to be nothing more than a branch of statistics for a world of big data. Business executives with a thorough understanding of machine learning have the ability to reach efficient business outcomes. In this age of data, firms have to work with large scale data. Both advanced software and hardware is needed to manage, analyze and store it. Herein lies the applicability of machine learning. To know more, please follow: http://www.dataversity.net/what-business-execs-need-to-know-about-machine-learning/

The Sports Industry is evolving. With the requirement to be accurate and the presence of data far beyond what humans can perceive and make collective sense of, there has risen a need to be able to observe, process and evaluate the actions of both teams. With the availability of large amounts of data to train the system, we can now accurately predict and develop strategies for the team. Machine learning is already being used to understand the conservative strategies of away teams at the English Premier League. It can also be applied to predict the behavior of individual players such as cricket bowlers in the IPL. Researches are also working on ML Algorithms to identify talented sportsmen based on their psychological characteristics and practice history. Read at: http://www.science20.com/the_conversation/machine_learning_and_big_data_is_changing_sports-155628