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'Gutfeld' guests discuss reports a Google software engineer was suspended for saying artificial intelligence is sentient. Google is a divisive company. Maybe you love the ease of Gmail and how easy it is to find anything you want online. Or maybe you get a shiver up your spine thinking of all the data the search giant has on you. You might see a few personal details when you search for yourself online.

Artificial Intelligence (AI) is the process of programming a computer to make decisions for itself. This type of independent and self-learning system has a wide range of applications in modern business, including B2B marketing. Although it's still in the early stages of development and application, AI can already help B2B marketers in a number of ways. After analyzing dozens of use cases, we picked the ones that proved to be most efficient. In this post, we will explain the basics of B2B marketing and show you seven tips and tricks on how to use AI in this field of business.

Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease. All the important libraries you would need to work on Machine learning lifecycle. Full-fledged course on Statistics so that you don't have to take another course for statistics, we cover it all. Data cleaning and exploratory Data analysis with all the real life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course. All the mathematics behind the complex Machine learning algorithms provided in a simple language to make it easy to understand and work on in future. Hands-on practice on more than 20 different Datasets to give you a quick start and learning advantage of working on different datasets and problems. More that 20 assignments and assessments allow you to evaluate and improve yourself on the go. Total 10 beginner to Advance level projects so that you can test your skills.

Data Science and Analytics is a highly rewarding career that allows you to solve some of the world's most interesting problems and Statistics the base for all the analysis and Machine Learning models. This makes statistics a necessary part of the learning curve. Analytics without Statistics is baseless and can anytime go in the wrong direction. For a majority of Analytics professionals and Beginners, Statistics comes as the most intimidating, doubtful topic, which is the reason why we have created this course for those looking forward to learn Statistics and apply various statistical methods for analysis with the most elaborate explanations and examples! This course is made to give you all the required knowledge at the beginning of your journey, so that you don't have to go back and look at the topics again at any other place.

TinyML is a generic approach for shrinking AI models and applications to run on smaller devices, including microcontrollers, cheap CPUs and low-cost AI chipsets. While most AI development tools focus on building bigger and more capable models, deploying TinyML models requires developers to think about doing more with less. TinyML applications are often designed to run on battery-constrained devices with milliwatts of power, a few hundred kilobytes of RAM and slower clock cycles. Teams need to do more upfront planning to meet these stringent requirements. TinyML app developers need to consider hardware, software and data management and how these pieces will fit together during prototyping and scaling up. ABI Research predicts the number of TinyML devices will grow from 15.2 million shipments in 2020 to a total of 2.5 billion by 2030.

Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease. All the important libraries you would need to work on Machine learning lifecycle. Full-fledged course on Statistics so that you don't have to take another course for statistics, we cover it all. Data cleaning and exploratory Data analysis with all the real life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course. All the mathematics behind the complex Machine learning algorithms provided in a simple language to make it easy to understand and work on in future. Hands-on practice on more than 20 different Datasets to give you a quick start and learning advantage of working on different datasets and problems. More that 20 assignments and assessments allow you to evaluate and improve yourself on the go. Total 10 beginner to Advance level projects so that you can test your skills.

Let's start off with a slightly curious question – have you considered building machine learning models using just pen and paper? It's actually a phenomenal method of learning how an algorithm works underneath. But here's the catch – we are living in the midst of a data boom. This means we can't calculate or design anything manually. The data we work with in machine learning projects is huge (and I mean HUGE).

One in 5 Americans surveyed say they own an Amazon Echo smart speaker (from $39), supported by new data published by Trading Platforms, a leading education and comparison platform for online traders. This is consistent with Edison Research and NPR's Smart Audio Report findings. Google Nest is its next closest competitor with roughly 8 percent of the U.S. market share, according to Trading Platforms. Considering how many of us are confined to our homes because of COVID-19, we may be using these devices more, too. "Before the pandemic, most people used their smart speakers for getting the weather and listening to music. But over the past year, they've become our meditation guides and yoga teachers, our homework helpers and bedtime story readers, the way we discover new recipes and local takeout spots – and so much more," says Katherine Prescott, Founder and Editor of VoiceBrew, which distributes an email newsletter devoted to smart speakers, Monday through Thursday.

Imagine if you could get all the tips and tricks you need to tackle a binary classification problem on Kaggle or anywhere else. Hopefully, this article gave you some background into binary classification tips and tricks, as well as, some tools and frameworks that you can use to start competing. If you want to go deeper, simply follow the links and see how the best binary classification models are built. This article was originally written by Derrick Mwiti and posted on the Neptune blog. You can find more in-depth articles for machine learning practitioners there.