Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models. In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks.
Climate change is here, and it's set to get much worse, experts say – and as a result, many industries have pledged to reduce their carbon footprints in the coming decades. Now, the recent jump in energy prices due mainly to the war in Ukraine, also emphasizes the need for development of cheap, renewable forms of energy from freely available sources, like the sun and wind – as opposed to reliance on fossil fuels controlled by nation-states. But going green is easier for some industries than for others,- and one area where it is likely to be a significant challenge is in data centers, which require huge amounts of electricity to cool off, in some cases, the millions of computers deployed. Growing consumer demand to reduce carbon output, along with rules that regulators are likely to impose in the near future, require companies that run data centers to take immediate steps to go green. And artificial intelligence, machine learning, neural networks, and other related technologies can help enterprises of all kinds achieve that goal, without having to spend huge sums to accomplish it.
Since 2002, Quantium have combined the best of human and artificial intelligence to power possibilities for individuals, organisations and society. Whether it be building forecasting engines that are driving down food wastage or creating mapping tools to support targeted measures in combatting human trafficking, Quantium believes in better goods, services, experiences, and championing the benefits of data for a brighter future. Q-Telco is the new joint venture between Quantium and Telstra to unlock the full potential of data and AI for Telstra and its customers. We'll do this by combining our market leading data science and AI capabilities with Telstra's customer, product and network data assets. This new partnership will not only provide personalised and data-enabled products and offers for Telstra's customers, but it will also embed proactive and predictive AI and machine learning across Telstra's core business.
Incorporating ethics and legal compliance into data-driven algorithmic systems has been attracting significant attention from the computing research community, most notably under the umbrella of fair8 and interpretable16 machine learning. While important, much of this work has been limited in scope to the "last mile" of data analysis and has disregarded both the system's design, development, and use life cycle (What are we automating and why? Is the system working as intended? Are there any unforeseen consequences post-deployment?) and the data life cycle (Where did the data come from? How long is it valid and appropriate?). In this article, we argue two points. First, the decisions we make during data collection and preparation profoundly impact the robustness, fairness, and interpretability of the systems we build. Second, our responsibility for the operation of these systems does not stop when they are deployed. To make our discussion concrete, consider the use of predictive analytics in hiring. Automated hiring systems are seeing ever broader use and are as varied as the hiring practices themselves, ranging from resume screeners that claim to identify promising applicantsa to video and voice analysis tools that facilitate the interview processb and game-based assessments that promise to surface personality traits indicative of future success.c Bogen and Rieke5 describe the hiring process from the employer's point of view as a series of decisions that forms a funnel, with stages corresponding to sourcing, screening, interviewing, and selection. The hiring funnel is an example of an automated decision system--a data-driven, algorithm-assisted process that culminates in job offers to some candidates and rejections to others. The popularity of automated hiring systems is due in no small part to our collective quest for efficiency.
Today, We'll look after something very big that you might have never seen or rarely seen on the web. We have researched for more than 35 days to find out all the cheatsheets on machine learning, deep learning, data mining, neural networks, big data, artificial intelligence, python, tensorflow, scikit-learn, etc from all over the web. To make it easy for all learners, We have zipped over 100 machine learning cheat sheet, data science cheat sheet, artificial intelligence cheat sheets and more in one article. You can also download the pdf version of this cheat sheets (links are already provided below every image). Note: The list is long.
Technology is not showing signs of slowing down any time soon. As we move into cloud computing, big data, natural language processing and artificial intelligence, the employment sector is gearing up for a big boost in the number of opportunities. Organisations such as Google, Microsoft, Facebook and Apple are aggressively hiring people with expertise in these domains, which makes them highly lucrative. Artificial intelligence is particularly on the cusp of a breakthrough. Technologies such as machine learning, neural networks, genetic algorithms and deep learning are receiving a lot of spotlight.
Projects have always been thought of as measurable improvements resulting from a result produced, which serve as the icing on the cake for achieving personal or corporate goals. Talking about individual projects, have you found it challenging to learn at home? Many of us are in the same boat -- there are far too many things to handle during these trying times, and learning has taken a back seat, contrary to our expectations. So, what are our options for getting back on track? How can we apply what we have learned about data science in the real world? Picking an open-source data science project and sticking with it is extremely beneficial.