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Data Science and Machine Learning Mathematics and Statistics-P2P

Learn the Mathematics, Statistics and Probability behind Data Science, Machine Learning, Artificial Intelligence!

Do you want to become a Data Scientist? Are you willing to learn Machine Learning? Well you’re at the right place!!

The average salary for a Machine Learning Engineer is $138,920 per year in the United States by Indeed.

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed ~ by Wikipedia.

Machine learning can easily consume unlimited amounts of data with timely analysis and assessment. This method helps review and adjusts your message based on recent customer interactions and behaviors. Once a model is forged from multiple data sources, it has the ability to pinpoint relevant variables. This prevents complicated integrations, while focusing only on precise and concise data feeds.

Machine learning algorithms tend to operate at expedited levels. In fact, the speed at which machine learning consumes data allows it to tap into burgeoning trends and produce real-time data and predictions

1. Churn analysis - it is imperative to detect which customers will soon abandon your brand or business. Not only should you know them in depth – but you must have the answers for questions like “Who are they? How do they behave? Why are They Leaving and What Can I do to keep them with us?”

2. Customer leads and conversion - you must understand the potential loss or gain of any and all customers. In fact, redirect your priorities and distribute business efforts and resources to prevent losses and refortify gains. A great way to do this is by reiterating the value of customers in direct correspondence or via web and mail-based campaigns.

3. Customer defections - make sure to have personalized retention plans in place to reduce or avoid customer migration. This helps increase reaction times, along with anticipating any non-related defections or leaves.

Many hospitals use this data analysis technique to predict admissions rates. Physicians are also able to predict how long patients with fatal diseases can live.

Insurance agencies across the world are also able to do the following:

  • Predict the types of insurance and coverage plans new customers will purchase.
  • Predict existing policy updates, coverage changes and the forms of insurance (such as health, life, property, flooding) that will most likely be dominant.
  • Predict fraudulent insurance claim volumes while establishing new solutions based on actual and artificial intelligence.

Machine learning is proactive and specifically designed for “action and reaction” industries. In fact, systems are able to quickly act upon the outputs of machine learning – making your marketing message more effective across the board.

Data Science and Machine Learning Mathematics and Statistics-P2P
New | h264, yuv420p, 1280×720 | ENGLISH, aac, 44100 Hz, stereo | 16h 19 mn | 3428.0 Mb
Instructor: The DataScience Stealth
Download: FILEFOXNTi

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