Advanced Statistical Modeling for Deep Learning and AI
What you'll learn
- Understand and apply key probability distributions, including Normal, Binomial, and Poisson distributions.
- Transform skewed datasets into normal distributions using techniques like log, square root, and power transformations.
- Calculate and interpret confidence intervals for critical statistical estimates, such as model accuracy.
- Distinguish between population data and sample data, and understand their roles in analysis.
- Perform random sampling correctly and understand its impact on the validity of data analysis.
- Evaluate classification models using metrics like accuracy, precision, recall, and F1 score.
- Identify and manage underfitting and overfitting issues in machine learning and statistical modeling.
- Apply statistical modeling concepts to real-world deep learning workflows.
Requirements
- No prior knowledge of statistics is required — all concepts will be explained from scratch.
- A basic understanding of Python programming is helpful (but not mandatory).
- A willingness to learn and apply statistical thinking in machine learning and deep learning contexts.
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 modelling 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:
- Deep Learning practitioners who want to strengthen their statistical modeling skills.
- Data Scientists and Analysts looking to apply statistics effectively in real-world AI projects.
- Machine Learning engineers aiming to improve their model evaluation and data preprocessing techniques.
- Students and professionals preparing for roles in data science, machine learning, or AI.
- Anyone interested in building a strong foundation in statistical thinking for AI-driven solutions.
Instructor
- 4.0 Instructor Rating
- 1,138 Reviews
- 70,495 Students
- 46 Courses
Hello, I'm Akhil Vydyula — Lead Data Engineer at Publicis Sapient, and Former Senior Data Scientist at PwC.
With over 5 years of rich industry experience and a strong focus on the BFSI sector, I’ve led and delivered end-to-end data and analytics solutions that power strategic decisions and transform business outcomes.
At Publicis Sapient, I currently lead complex data engineering initiatives, leveraging my deep expertise in cloud-native platforms like AWS to architect robust, scalable data pipelines. My work spans across developing and optimizing ETL workflows using PySpark and Spark SQL, orchestrating data flows via EMR, Step Functions, and EventBridge, and driving real-time and batch data processing into PostgreSQL (RDS/Redshift) environments. I've also implemented AWS Glue and DMS to seamlessly replicate and transform large-scale on-premise data into cloud-native formats.
Previously, at PwC, I specialized in advanced analytics and machine learning within the Advisory Consulting practice. I’ve built and deployed predictive models using statistical analysis, regression, classification, clustering, and text mining—particularly for risk identification and decision modeling. My passion lies in transforming raw data into actionable insights through effective data storytelling and visualization.
In parallel to my corporate career, I bring over 5 years of teaching experience, mentoring hundreds of aspiring data professionals. I’m deeply committed to helping students break into the data industry by translating real-world challenges into practical learning experiences.
Whether it's building data pipelines, uncovering business insights, or shaping the next generation of data talent, I thrive at the intersection of technology, strategy, and impact.
Let’s connect if you're passionate about data, eager to learn, or looking to collaborate on meaningful, data-driven initiatives.