Advanced Statistical Modeling for Deep Learning Practitioner

Advanced Statistical Modeling for Deep Learning Practitioner, available at $19.99, with 27 lectures, and has 1515 subscribers.

You will learn about 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. This course is ideal for individuals who are This course is for students who want to learn statistics from data science perspective. It is particularly useful for This course is for students who want to learn statistics from data science perspective.

Enroll now: Advanced Statistical Modeling for Deep Learning Practitioner

Summary

Title: Advanced Statistical Modeling for Deep Learning Practitioner

Price: $19.99

Number of Lectures: 27

Number of Published Lectures: 27

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Foundations of Data Analysis: Exploring Data Types, Central Tendencies,Measures

Lecture 1: Understanding Fundamental Data Types: Integer, String, and Boolean

Lecture 2: Demystifying Data Types: Qualitative and Quantitative Data Explained

Chapter 2: Data Types and Central Tendencies: Unveiling the Mean, Median, and Mode

Lecture 1: Understanding Data Types and Central Tendencies: Exploring Mean, Median, and Mod

Chapter 3: Navigating Variability: Measures of Dispersion in Business Statistics

Lecture 1: In-Depth Analysis of Measures of Dispersion in Business Statistics

Lecture 2: Python Essentials: Exploring Data Types and Calculating Central Tendencies in GC

Chapter 4: Analyzing Data with Python: Sampling, Uniform Distribution, Z-Score, P-Value etc

Lecture 1: Exploring Uniform Distribution and Z-Scores in Python using Google Colab

Lecture 2: Hypothesis Testing with Python: P-Values and T-Tests in Google Colab

Lecture 3: Analyzing Business Data: Confidence Intervals and Analysis of Variance in Python

Chapter 5: Analyzing Coefficients,Correlation,Causation etc in Business Statistics python

Lecture 1: Analyzing Coefficients, Correlation, and Causation in Business Statistics python

Lecture 2: Assessing Data Assumptions with QQ Plots and Python Implementation of Hypothesis

Chapter 6: Exploring Data Quality and Patterns: Insights from Histograms,CDF and others etc

Lecture 1: Exploring Data Quality and Patterns: Insights from Histograms,Box Plots,Outliers

Lecture 2: Exploring Data Cleaning Insights, Cumulative Distribution Functions (CDF)

Chapter 7: Data Cleaning and Exploring Variable Relationships in Python for Business Statis

Lecture 1: Data Cleaning and Exploring Relationships Between Variables in Python for Busine

Lecture 2: Enhancing Business Insights: Data Cleaning and Correlation Analysis with HeatMap

Lecture 3: Correlation Analysis with Pearson and Spear-man Rank Correlation

Chapter 8: Deciphering Time Series Characteristics: Unveiling the Essence of Time Series

Lecture 1: Unlocking the Secrets of Time Series Data: Understanding Time Series Characteris

Lecture 2: Decomposing Time Series: Unraveling the Components for Clear Analysis

Chapter 9: Mastering Time Series Analysis: Unveiling Moving Averages,Harnessing ACF&PACF

Lecture 1: Exploring Time Series Analysis: Unveiling Moving Averages and ACF/PACF Patterns

Lecture 2: Mastering Time Series Analysis with ARIMA Models: A Comprehensive Guide

Lecture 3: Mastering Time Series Analysis: Demystifying ARIMA Models for Clear and Comprehe

Chapter 10: Demystifying Gaussian Distributions: A Comprehensive Analysis of Statics with py

Lecture 1: Understanding Gaussian Distributions: A Comprehensive Analysis of Statistical Pa

Lecture 2: Understanding the Central Limit Theorem (CLT) and Its Implications for Skewed

Lecture 3: Unraveling Skewed Distributions: Central Tendency Analysis Through Sampling

Chapter 11: Unlocking Insights: Exploring Analysis of Variance in Statics Analysis

Lecture 1: Unlocking Insights: Exploring Analysis of Variance in Statics Analysis

Lecture 2: Unlocking the Power of Data: Statics Analysis, Predictive Modeling, Reverse Tran

Chapter 12: Unraveling UK Road Accidents: A Time Series Deep Learning Approach for Clear Ins

Lecture 1: Unraveling UK Road Accidents: Time Series Deep Learning Analysis with Python

Lecture 2: Predicting Future UK Road Accident Trends: A Clear Exploration of Time Series

Instructors

Akhil Vydyula
Data Scientist | Data & Analytics Specialist | Entrepreneur

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