data science course

We can summarize data science job life and jargon's job profile

 Many questions are arising regarding, how to become a data scientist and what are the skills required. Here I’ll help you gain knowledge on how you acquire the skills of a data scientist. The challenge of becoming a Data scientist is that you need to obtain right skills and the profession demands a long list of skills to get hired.

 Jargon is a literary term that is defined as the use of specific phrases and words in a particular situation, profession, or trade. These specialized terms are used to convey hidden meanings accepted and understood in that field.

 Excellent communication skills means that you're able to convey information clearly, concisely, and convincingly in both written and verbal form. There's so much information-sharing that happens within companies to move projects along that employers can't afford to hire someone who isn't able to communicate their thoughts and ideas via email, in a presentation deck, or in a meeting.

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Object detection is a computer vision task that involves both localizing one or more objects within an image and classifying each object in the image. Sequence based object detection using mask RCNN is a challenging computer vision task that requires both successful object localization in order to locate and draw a bounding box around each object in an image. An extension of object detection involves marking the specific pixels in the image that belong to each detected object detection instead of using coarse bounding boxes during object localization. There are perhaps four main variations of the approach, resulting in the current pinnacle called Mask R-CNN.
R-CNN,Fast R-CNN,Faster R-CNN,Mask R-CNN. Mask R-CNN is a sophisticated model to implement, especially as compared to a simple or even state-of-the-art deep convolutional neural network model.

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We can summarize data science job life cycle with three major skills. Which covers three major skills on Data Science job profile.

Many questions are arising regarding, how to become a data scientist and what are the skills required. Here I’ll help you gain knowledge on how you acquire the skills of a data scientist. The challenge of becoming a Data scientist is that you need to obtain right skills and the profession demands a long list of skills to get hired.

Programming skills are required, no matter which role or company you’re interviewing for, you’re probably going to be presumed to know how to use the tools of the trade. This sounds like a database querying languages like SQL and a statistical programing language, like Python and R.


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Face Aging Using Conditional GANs (GANs) are extensions of the GANs model. You can read about Conditional GANs in my previous post here. In this post, I will try to explain how we can implement a GANs to perform automatic face aging. Face Aging GAN(Age-GANs) introduced by Grigory Antipov, Moez Baccouche, and Jean-Luc Dugelay, in their paper with titled Face Aging With Conditional Generative Adversarial Networks. The Face Aging-Gan has four networks.

 An Encoder : It learns the inverse mapping of input face images and the age condition with the latent vector Z.
Encoder network generates a latent vector of the input images. The Encoder network is a CNN which takes an image of a dimension of (64, 64, 3) and converts it into a 100-dimensional vector.

 There are four convolutional blocks and two dense layers.
Each convolutional block has a convolutional layer, followed by a batch normalization layer, and an activation function except the first convolutional layer.

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 Deep Learning was developed as a Machine Learning approach to deal with complex input-output mappings. While traditional ML methods successfully solve problems where final value is a simple function of input data. On the contrary, Deep Learning techniques are able to capture composite relations between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices.

Basic definition of Deep Learning is a set of ML techniques that use stacked layers of transformation trainable from the beginning to the end. Performance is the main key difference between both algorithms. Although, when the data is small, Deep Learning algorithms don’t perform well. This is the only reason Deep Learning algorithms need a large amount of data to understand it perfectly.

 Deep Learning is discovered and proves to have the best techniques with state-of-the-art performances. Thus, Deep Learning is surprising us and will continue to do so in the near future. Recently, researchers are continuous in exploring Machine Learning and Deep Learning. In the past, researchers were limited to academia. But, nowadays, research in ML and Deep Learning is making their place in both industries and academia.

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 Choosing the appropriate forecasting technique to employ is a challenging issue and requires a comprehensive analysis of empirical results. Recent research findings reveal that the performance evaluation of forecasting models depends on the accuracy measures adopted. Some methods indicate superior performance when error based metrics are used, while others perform better when precision values are adopted as accuracy measures. As scholars tend to use a smaller subset of accuracy metrics to assess the performance of forecasting models, there is a need for a concept of multiple accuracy dimensions to assure the robustness of evaluation. 

• Regression problems are supervised learning problems in which the response is continuous
  • Linear regression is a technique that is useful for regression problems.

Classification problems are supervised learning problems in which the response is categorical.
 
  Deep Learning techniques over regular data sets, tampered data sets and noisy data sets. First, Deep Learning techniques have been investigated over regular data sets, the experiments showed good results in terms of accuracy and error rate

Machine learning continues to be an increasingly integral component of our lives, whether we’re applying the techniques to research or business problems. Machine learning models ought to be able to give accurate predictions in order to create real value for a given organization.

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 Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.

 Data science has over the past few years come a really long way. That is why they are integral part of understanding the working of many industries, however complex and intricate.Data science is taking on a big and prime role in functioning and growth process of brands. Being a data scientist is therefore a prime position for any person as they have the big task of managing data and providing solutions for their problems, both within and outside the organisation.

 Data Scientist is someone who makes value out of data. Data scientist duties typically include creating various machine learning-based tools or processes within the company, such as recommendation engines or automated lead scoring systems. People within this role should also be able to perform statistical analysis.

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  Markov chains are a fairly common, and relatively simple, way to statistically model random processes. They have been used in many different domains, ranging from text generation to financial modeling. A popular example is r/SubredditSimulator, which uses Markov chains to automate the creation of content for an entire subreddit. Overall, Markov Chains are conceptually quite intuitive, and are very accessible in that they can be implemented without the use of any advanced statistical or mathematical concepts. They are a great way to start learning about probabilistic modeling and data science techniques.

  The Markov Chain is a model used to describe a sequence of consecutive events where the probability or chance of an event depends only on the event before it.If a sequence of events exhibits the Markov Property of the reliance on the previous state, then the sequence is called ‘Markovian’ in nature.

  For some problems in Reinforcement Learning, the actions performed in a particular state is directly related to the previous state, the actions performed in that state and the rewards that the agent receives upon performing said actions.

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     Machine Learning is one of the hottest career choices today. AI will create 2.3 million Machine Learning jobs by 2020, Emerging Jobs Report shows there are 9.8 times more Machine Learning Engineers working today than five years ago with 1,829 open positions listed on their site as of early 2018. Both Data Science and Machine Learning are generating more jobs than candidates right now, making these two areas the fastest growing tech employment areas today.

   There can be many factors contributing to it. One of the reasons is the increasing popularity in the machine learning industry. Most companies are integrating ML and AI into their top data initiatives, which holds true for Indian companies as well. As more companies are investing in machine learning, they are looking to hire more ML experts for cutting-edge research.

  While the demand for ML is growing, the underlying problem-description, skill sets required, or the people applying for these jobs are likely to look largely similar to what it is today. That being said, if we ask whether the appetite for complex models is growing, I think that in the short term, when a lot of organisations are still trying to splice data-driven decision making into their managerial DNA, the demand for basic skills and simpler models will dominate. Once most organisations have reached a certain level of analytical maturity, their appetite and ability to digest complex models will grow faster,

       At present, the world is going through another, possibly even stronger revolution: the use of artificial intelligence to perform complex cognitive tasks to solve business problems in ways that were previously either highly complicated or extremely resource-intensive. 

      Today, organizations must be innovative and leverage the latest technologies simply to stay in business. Enterprises that implement online retail, banking, and other services aren’t considering these channels as just another route to increase their revenue.

       It is important to understand that to undergo digital transformation, companies may need to completely re-engineer their current processes to make use of technologies like the Internet of Things (IoT), Big Data analytics, artificial intelligence, and others instead of doing patchwork on existing processes to adapt to digital technologies. Furthermore, it is also important for senior IT executives to consider digital initiatives in tandem with their cloud strategy instead of treating them in isolation.

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We have nealry 15+ Machine learning algorithms, but entire machine learning can be summarised into 3 major machine learning equation which rule entire machine learning. Most of the people are learning machine learning courses and planning a career in machine learning. This podcast is pretty important to rule in space of machine learning.

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