Statistical Models Courses and Certifications

Time Series Forecasting in Python - Tutorial for Beginners

Master time series forecasting fundamentals in Python with baseline models, ARIMA, cross-validation, exogenous features, prediction intervals, and evaluation metrics.

• freeCodeCamp
• 1 hour 33 minutes
• Self-Paced
• Free Video

Center for Language & Speech Processing(CLSP), JHU

Discriminative Training and Maximum Entropy Models for Statistical Machine Translation

Discover discriminative training techniques and maximum entropy models for statistical machine translation through expert insights from CLSP research.

• YouTube
• 1 hour 11 minutes
• Self-Paced
• Free Video

Time Series Analysis

Master time series data analysis from basics to advanced forecasting using statistical models, machine learning, and deep learning architectures like RNNs and LSTMs.

• openHPI
• 2 weeks
• Self-Paced
• Free Certificate

Regression

Exploring Data Visually Introduction to Probability

• Brilliant
• Self-Paced
• Paid Course

NPTEL;  IIT Kanpur

Linear Regression Analysis and Forecasting

1 rating

The accuracy of forecasting depends upon the goodness of obtained model. What are the steps and checks required to obtain a good model and in turn, how to do forecasting is being aimed to be taught in this course.

• YouTube
• 12 hours
• Self-Paced
• Free Video

The Julia Programming Language

Julia Programming Language Tutorial - MIT 2013

Discover Julia's high-performance programming capabilities through MIT's comprehensive tutorial covering data analysis, parallel computing, optimization, and metaprogramming techniques.

• YouTube
• 5 hours 52 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking 2022

Master Bayesian inference, multilevel modeling, and causal reasoning through practical statistical methods that avoid common pitfalls in data analysis and research design.

• YouTube
• 1 day 46 minutes
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking Fall 2017

Master Bayesian statistical methods and causal inference through comprehensive lectures accompanying the acclaimed "Statistical Rethinking" book by Richard McElreath.

• YouTube
• 19 hours 46 minutes
• Self-Paced
• Free Video

Stanford Seminar - Efficient and Resilient Systems in the Cognitive Era

Explore efficient and resilient systems for cognitive computing, covering swarm intelligence, sensor fusion, hardware optimization, and energy efficiency in the context of IBM's research.

• YouTube
• 1 hour 16 minutes
• Self-Paced
• Free Online Course

Smart Physical Systems from the Standpoint of an AI Company

Explore machine learning, deep learning, and their limitations in this seminar. Gain insights into AI applications in autonomous driving, voice control, and image processing.

• YouTube
• 56 minutes
• Self-Paced
• Free Online Course

Deep Generative Models - Lecture 2: Background

Explore fundamental concepts and background for deep generative models, covering essential topics to build a strong foundation in this advanced machine learning field.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video

Deep Generative Models - Energy Based Models - Lecture 14

Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights from Stanford's expert on advanced generative modeling techniques.

• YouTube
• 1 hour 25 minutes
• Self-Paced
• Free Video

Deep Generative Models - Energy Based Models - Lecture 11

Explore energy-based models in deep learning, their applications, and theoretical foundations. Gain insights from Stanford's expert on advanced generative modeling techniques.

• YouTube
• 1 hour 25 minutes
• Self-Paced
• Free Video

Quantitative Social Science Methods

1 rating

First course for incoming Harvard Government Department PhD students; also taken by students in other departments, and students elsewhere through the Harvard Extension School. Instructor: Gary King.

• YouTube
• 19 hours
• Self-Paced
• Free Video

Derek Banas

Linear Regressions with StatsModels - Data Science Tutorial

Learn to create linear regressions using StatsModels, exploring datasets and examples with NumPy, Pandas, Matplotlib, and Seaborn. Analyze professor ratings and tipping strategies through practical applications.

• YouTube
• 44 minutes
• Self-Paced
• Free Video