The most important thing to learn to become a Data Scientist is the pipeline, i.e, the process of getting and processing data, understanding the data, building the model, evaluating the results (both of the model and the data processing phase) and deployment. Learn Logistic Regression first to become familiar with the pipeline and not being overwhelmed with fancy algorithms. So here are 5 reasons why we should start with Logistic Regression first to become a Data Scientist.
Artificial intelligence (AI) is giving customer experience a shot in the arm. There are some clear front runners - organisations embracing the power of AI to solve consumer pain points such as in banking and financial services. These front runners differ in many ways; they discuss a set of key practices they should follow to differentiate themselves while building a customer experience strategy for an AI-driven environment. They aim for a holistic approach to deploying AI in customer experience.
The Internet of Things (IoT) is projected to grow significantly over the coming years. This growth is being driven by the promise of increased insight, enhanced customer satisfaction, and greater efficiency. These benefits are made possible as sensor data from devices and the power of Internet-based cloud services converge. One of the key concerns related to the successful adoption of the IoT is having sufficiently strong security mechanisms in place throughout the ecosystem—to mitigate the increased security risks of connecting devices to the Internet.
K-means clustering is a very popular unsupervised learning algorithm. It takes your data and learns how it can be grouped. Through a series of iterations, the algorithm creates groups of data points — referred to as clusters — that have similar variance and that minimize a specific cost function. By using the within-cluster sum of squares as cost function, data points in the same cluster will be similar to each other, whereas data points in different clusters will have a lower level of similarity.
You need to understand when do you need to have a well-defined project folder structure. Also, do not focus on hyper parameter tuning in the early stage as it may spend too many resource and time. If a solution is confirmed then it should be a good time to finding a better hyper parameter before launching the prediction service. Modularized your processing, training, metrics evaluation functions are important steps to manage to tune. Focus on building a model but not make sure everything works well in an unexpected scenario.
Explainable AI is still an evolving field and scientists are trying to find different ways to make neural networks interpretable without compromising their performance. We have yet to see standards emerge, there are however several interesting initiatives that are aimed at creating explainable AI and keeping track of where the defining attributes for an automated decision come from. The use of non-explainable AI presents challenges in domains that explicitly require all decisions to be retraceable.
Well, the biggest advantage of deep learning is really its shortcoming. The very fact that humans don’t have to identify distinguishing features means that the machine defines what it deems important. Interpretability of deep learning algorithms and visual explanation of results is a rapidly evolving field, and research is fast catching up. And yes, it needs tons of data to even get started. So yes, there are some hiccups in this area, but the stellar and stable results clearly outweigh the cons, for now.
With robots becoming more and more competent and intelligent, there are more applications for them than ever in a variety of industries. That’s got a lot of people worried that robots are getting ready to take over their jobs. It’s understandable, of course, to fear that you might become obsolete in the workplace—your ability to earn an income depends on being able to find a job. In the debate over robots in the workplace, there’s a lot to think about. Will robots eventually take over most jobs? Is your job really in jeopardy? Let’s take a look.
Cross-Validation is a very powerful tool. It helps us better use our data, and it gives us much more information about our algorithm performance. In complex machine learning models, it’s sometimes easy not pay enough attention and use the same data in different steps of the pipeline. This may lead to good but not real performance in most cases, or, introduce strange side effects in others. We have to pay attention that we’re confident in our models.
You probably knew that there are two types of machine learning. Supervised and unsupervised. Well, there is a third one, called Reinforcement Learning. RL is arguably the most difficult area of ML to understand because there are so many things going on at the same time. It is a really astonishing area and you should definitely know about it. It involves complex thinking and 100% focus to grasp it, and some math.
Big data analytics raises a number of ethical issues, especially as companies begin monetizing their data externally for purposes different from those for which the data was initially collected. The scale and ease with which analytics can be conducted today completely changes the ethical framework. We can now do things that were impossible a few years ago, and existing ethical and legal frameworks cannot prescribe what we should do. While there is still no black or white, experts agree on a few principles.
Word embeddings discussion is the topic being talked about by every natural language processing scientist for many-many years. The idea behind all of the word embeddings is to capture with them as much of the semantical/morphological/context/hierarchical/etc. information as possible, but in practice one methods are definitely better than the other for a particular task. The problem of choosing the best embeddings for a particular project is always the problem of try-and-fail approach, so realizing why in particular case one model works better than the other sufficiently helps in real work.
Severe shortcomings in both ‘data’ and ‘training’ take the A & I out of mAgIc, making it meaningless. This is the biggest bottleneck for AI’s progress today. But wait.. if machines could take up any human task, why not this one too? Can we make machines learn-to-teach themselves? Yes, this is doable. Enter GANs... Or its complex sounding expansion, Generative Adversarial Networks. If Deep learning is the next big thing that’s taking the cake, GAN is the cream on that cake. The possibilities have never looked so exciting!
Machine learning uses patterns in data to label things. Sounds magical? The core concepts are actually embarrassingly simple. How does it actually work? If you were expecting magic, well, the sooner you’re disappointed, the better. Machine learning may be prosaic, but what you can do with it is incredible! It lets helps you write the code you couldn’t come up with yourself, allowing you to automate the ineffable. Don’t hate it for being simple. Levers are simple too, but they can move the world.
The current consumer IoT device landscape is still immature. For consumer IoT devices to thrive, device management capabilities need to evolve in a few ways. Effective device management is critical to establishing and maintaining the health, connectivity, and security of IoT devices. Effective device management is critical to establishing and maintaining the health, connectivity, and security of IoT devices. What consumer IoT needs is a truly open IoT device management ecosystem.
Disruptive AI technologies are significantly boosting creative productivity, and it’s not just happening in the movie business. All companies from big to boutique shops need to create content to connect with their clients. Business owners, content marketers, investors, anyone with a story to tell but little time to tell it, will soon have AI-powered tools to create high-quality content at a much faster rate. AI systems are still a long way from encoding the visceral and emotional knowledge of humans.
Big data and IoT are entwined. How? Every device around us is connected to the cloud, sharing every minute bit of our data. Smart farming, e-health, smart retail, smart home, smart cities, smart environment are few applications of IoT in today’s world. A lot of data is generated from these applications that industries gather with the aim to improve their business workflows, enhance customer experience, and stay relevant in the ever-increasing competition. This means that IoT directly impacts data, making it swell in size, and companies should leverage new-age technologies to draw accurate insights from the data to make informed business decisions.
Demystifying Data Science, a free conference for aspiring data scientists and data-curious business leaders, was designed to provide insight on the training, tools, and career paths of data scientists, the conference was fully interactive, featuring real-time chat, worldwide Q&A, and polling. 14 speakers presented live before taking questions submitted via the real-time conference chat feature. The talks cover a wide range of topics: from showcasing your work to connecting with data leaders, from telling a persuasive data story to debugging myths in data science.
Ecommerce businesses often struggle to convert online browsers into actual shoppers due to their inability to replicate the traditional “physical shopping” experience that most buyers are accustomed to. Artificial intelligence is, however, rewriting this script by helping ecommerce businesses to not only attract but also retain customers. Businesses cannot, therefore, ignore the powerhouse that AI is growing to become. That is why, e-commerce websites must considerer seriously implementing AI into their businesses and enjoy its benefits. Here are five ways through which AI is achieving this.
The BYOD (bring your own device) issue gives IT managers and internet security contractors plenty to fret over. Ninety-five percent of the data breaches we see involve BYOD. And most of them are totally well-meaning employees who don’t know they’re doing anything wrong. What is the core of the BYOD dilemma? People are so plugged in that they go almost catatonic if you take their smartphones and other devices away. In a competitive labor market, you’d better have some pretty sweet perks to compensate for the deprivation.
A lot has happened in the sales tech space. The major change of this landscape iteration is the restructuring of the Sales Intelligence layer. The new layer features list providers regardless of their data collection method. Differentiate between vendors offering data augmentation services. Eventually, add call and web intelligence solutions, growingly used in sales organizations. Besides understanding how the category shapes up, follow steps for your vendor selection. Keep in mind this explosion of players brought a lot of variability in data quality.
While IoT can open up a plethora of opportunities, implementing the concept on the existing objects for smart interaction is not an easy task. As IoT is in its early phase of adoption and the technology has not yet grown to accommodate a superior level of connectedness, as popularized by tech enthusiasts or the mainstream media, it is imperative to keep realistic goals for implementing successful IoT solutions. While there are many challenges in developing a feasible IoT solution, the rapid advancements in technology can propel the IoT development.
While it’s true that data tends to decay over time, you always need to be sure that you are analyzing the right data. As marketers, while we become more and more inclined to a data-driven approach to enable customer experience, the key question to know is “Are we even looking at the right data?” (Or) “Do we have access to the right data?” Put yourself in the shoes of the customer and you’ll realize that most times, great CX need not always be about wowing the customer at every opportunity.