HomeFree CourseAdvanced Statistical Modeling for Deep Learning Practitioner – (Free Course)

Advanced Statistical Modeling for Deep Learning Practitioner – (Free Course)

What you’ll learn

  • You will learn the most common probability distributions such as normal distribution and binomial distribution.
  • You will learn how to transform skewed data to be normally distributed using different transformation methods such as log, square root, and power transformation
  • You will learn how to calculate confidence intervals for statistical estimates such as model accuracy.
  • You will learn the concepts of population data vs sample data.
  • You will learn what random sampling means and how it affects data analysis.
  • You will learn the evaluation metrics for classification models.
  • You will understand what we mean by underfitting and overfitting in machine leaning and statistical modeling.

 

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This course includes:

  • 5.5 hours on-demand video
  • Access on mobile and TV
  • Full lifetime access
  • Certificate of completion

Description

In the rapidly evolving field of artificial intelligence, the ability to harness the power of deep learning models relies heavily on a strong foundation in advanced statistical modeling. This course is designed to equip deep learning practitioners with the knowledge and skills needed to navigate complex statistical challenges, make informed modeling decisions, and optimize the performance of deep neural networks.

Course Objectives:

  1. Mastering Advanced Statistical Techniques: Gain a deep understanding of advanced statistical concepts and techniques, including multivariate analysis, Bayesian modeling, time series analysis, and non-parametric methods, tailored specifically for deep learning applications.
  2. Optimizing Model Performance: Learn how to use statistical tools to fine-tune hyperparameters, handle imbalanced datasets, and address overfitting and underfitting issues, ensuring that your deep learning models achieve peak performance.
  3. Interpreting Model Outputs: Develop the skills to interpret and critically evaluate the outputs of deep learning models, including confidence intervals, prediction intervals, and uncertainty quantification, enhancing the reliability of your AI systems.
  4. Incorporating Probabilistic Modeling: Explore the world of probabilistic modeling and Bayesian neural networks to incorporate uncertainty into your models, making them more robust and reliable in real-world scenarios.
  5. Time Series Forecasting: Master time series analysis techniques to make accurate predictions and forecasts, with a focus on applications like financial modeling, demand forecasting, and anomaly detection.
  6. Advanced Data Preprocessing: Learn advanced data preprocessing methods to handle complex data types, such as text, images, and graphs, and apply statistical techniques to extract valuable insights from unstructured data.
  7. Hands-On Projects: Apply your knowledge through hands-on projects and case studies, working with real-world datasets and deep learning frameworks to solve challenging problems across various domains.
  8. Ethical Considerations: Discuss ethical considerations and best practices in statistical modeling, ensuring responsible AI development and deployment.

Who Should Attend:

– Data scientists and machine learning engineers seeking to deepen their statistical modeling skills for deep learning.

– Researchers and practitioners in artificial intelligence aiming to improve the robustness and interpretability of their deep learning models.

– Professionals interested in staying at the forefront of AI and machine learning, with a focus on advanced statistical techniques.

Prerequisites:

– A strong foundation in machine learning and deep learning concepts.

– Proficiency in programming languages such as Python.

– Basic knowledge of statistics is recommended but not mandatory.

Join us in this advanced statistical modeling journey, where you’ll acquire the expertise needed to elevate your deep learning projects to new heights of accuracy and reliability. Uncover the power of statistics in the world of deep learning and become a confident and capable practitioner in this dynamic field.

 

Who this course is for:

  • This course is for students who want to learn statistics from data science perspective.

 

How to Get this course FREE?

Get a 100% Discount On Udemy Paid Courses by clicking on the Apply Here Button. This Course coupon code is automatically added to the Apply Here Button.

Apply this Coupon: 117863DFC729A3558224   is applied  (For 100% Discount)

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Note: The udemy Courses Will be free for a Maximum of 1000 Learners can use the promo code AND Get this course 100% Free. After that, you will get this course at a discounted price.

 

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FAQs – Advanced Statistical Modeling for Deep Learning

What specific topics will I learn in this course?

You’ll delve into various probability distributions, including normal and binomial distributions, along with methods to transform skewed data for better analysis.

How will this course enhance my skills in statistical modeling?

By mastering advanced statistical techniques tailored for deep learning, you’ll gain insights into multivariate analysis, Bayesian modeling, time series analysis, and non-parametric methods.

Who is the target audience for this course?

This course is designed for data scientists, machine learning engineers, researchers, and practitioners in artificial intelligence who want to deepen their statistical modeling skills for deep learning applications.

What prerequisites do I need to have before enrolling?

While a strong foundation in machine learning and deep learning concepts is necessary, proficiency in programming languages such as Python is also recommended.

Will I receive a certificate upon completion of the course?

Yes, upon completing the program, you’ll receive a certificate of completion to showcase your expertise in advanced statistical modeling for deep learning.

How long is the course, and what is the format?

The course comprises 5.5 hours of on-demand video content, accessible on mobile and TV platforms, ensuring flexibility in your learning schedule.

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My name is Pratik Nagda and I am a resident of the small town of Nallasopara in Maharashtra. I come from a very poor family and I live with my mom. My father was dead when I was in 4th grade. Due to that financial issue, I was forced to go to a hostel from the 5th to the 10th. I know the basics of blogging and all because in my final year of school, after that, when I came home, I had a great fight with my mom because she wanted to take me science and I wanted to take science. Finally, I convinced my mom. I have done engineering at St. John College of Engineering and Management, which is in Palghar. I still love my college very much. Even today, I keep sharing the experiences of my college with my readers. Today I am the owner of the CareerBoostZone English blogging site and through my blog, I have prepared a better employment platform for Readers I feel happy that I tried to empower readers by helping in employment and the development of the country. And my efforts are continuing towards success.
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