Moto X Play Detailed Review

Moto X Play

Moto X play launch was eagerly anticipated in India when Motorola and Flipkart announced its release on 14th September 2015. It was anticipated that Moto X play would be approximately priced around ₹ 25000, given its specifications and pricing of 2nd generation Moto X. However Motorola pleasantly surprised everyone by killer pricing: ₹ 18,499 (16 GB) and 19,999 (32 GB). Moreover, the 21 MP primary camera puts it ahead of its competitors comfortably. This is arguably Motorola’s best offering this year so far.

Since I was planning to replace my old cellphone, I decided to go for Moto X Play (32 GB) and ordered it on Flipkart as soon as it became available, which also ensured that I got some cool offers with it from Flipkart! 🙂

Here are my initial impressions about the phone after exploring it thoroughly in the last 2-3 days.

Design & Build Quality

The phone is pretty well designed and the build is solid as compared to flimsy plastic builds we are experiencing from some other manufacturers. The phone feels like a premier phone straight out of the box. Despite its large size (5.5 inches display), phone fits well in the palm and rubberized back cover ensures good grip as well. The back cover is removable & swappable, but the battery is not removable. The front is covered by large 5.5 inch display covered with Corning Gorilla Glass, 5 MP front camera and openings for speaker & microphone. There is no notification LED available here. The top of the phone provides dual nano-SIM and Micro SD slot along with 3.5mm headset socket. Unlike few other manufacturers, Motorola provides headset in the box. The bottom of the phone has micro USB port for charging and data transfer. The power switch as well as volume control are located on the right edge of the phone, which is a more sensible placement as compared to left since many flip-covers could unintentionally press volume control on the left.

Unlike its cheaper sibling Moto G3, Moto X Play is not water resistant but it is water repellant with nano-coating technology.

So, are you passionate about everything that you do?

So, are you passionate about everything that you do?
You could be a Multipotentialite!

I got hooked up by the question itself. It is quite intriguing for someone like me who gets pulled by diverse interests all the time.

In this interesting TEDx talk, Emilie Wapnick discusses ‘Why Some of us Don’t Have One True Calling’ and how it could actually be a good thing.

I had written a blog post about Career Concepts & Career Paths based on the work of Michael Driver and Ken Brousseau of the University of Southern California. Spiral career concept is an interesting case study and this TEDx talk reminded me of that as I could see quite a few parallels about getting bored and moving on to new learning and new jobs/careers. There have been other terminologies for this, including this one that I love: Renaissance Souls. Anyway!

Usually, specialists (experts) are valued more by most businesses and they tend to get more attention/adulation. But it is crucial for businesses as well as for those Renaissance Souls themselves to realize that they bring unique perspective with their diverse interests and their intersections, which is invaluable. Watch Emilie Wapnick talk about all this in her TEDx talk here –

Essential Machine Learning Algorithms in a nutshell

I am sharing some brief but insightful videos that explain the essential Machine Learning (ML) algorithms quite well. All these videos are part of Data Science and Machine Learning Essentials course by Microsoft on edX platform.

If you’re interested in learning Machine Learning thoroughly, I would highly recommend longer Machine Learning course by Stanford University professor Andrew Ng on Coursera platform. It is one of the best CS courses I have ever taken!

Watch these wonderful videos –

• Classification – In classification we try to predict if the given test entity belongs to a specific class or not based on the training set we use to train the algorithm. Thus, classification is predicting a true/false value for an entity with a given set of features. For example, we use classification to determine if the given email is a SPAM or not. The mail is checked for various features such as presence of certain words in its contents, the sender etc. to determine f it can be classified as a SPAM or not. It can also be used to detect credit card frauds, detecting if tumour is malignant or not and many such classification problems.

• Regression – Regression is used to predict a real numeric value outcomes. It can be used to predict sales figures, number of customers for the business based on the training set we use to train the algorithm. The training set examples contain features that denote factors that are most likely to have effect on the outcome. For example, to predict selling price of the house, its total built-up area would be one of the most important features.

• Clustering – Unlike Classification and Regression, clustering is an unsupervised ML algorithm. In clustering, we try to group entities with similar features. For example,clustering can be used to determine the locations of telephone towers so that all users receive optimum signals. We may also use clustering to group products or customers where we may not have established categorization.

• Recommendation – Recommendation is used to recommend an item to a user based on his previous usage/purchases or preferences of similar users. For example, it can be used on online shopping sites such as Amazon to recommend new books or items to a user. Netflix uses it to recommend movies to their customers.