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