36-50 hours long courses

Knowledge has no shortcuts

Invest 6-10 hours per week

Classes meet entirely online

Comprehensive coverage

of statistical theory & ML algorithms

Extensive hands-on coding

in the cloud

Master data visualization

techniques for deep insights

Expert instruction

by PhD scientist with research excellence, industry leadership & ML teaching at a top university

Multiple payment options

Installment plans, discounts available

Bridging Gaps of University and Online Programs in ML Education

Our courses are designed to equip you with the skills needed to solve real-world problems, filling gaps left by traditional university and online ML courses. University programs often miss incorporating comprehensive sets of models, adequate practical applications, cloud computing, and end-to-end pipelines. Meanwhile, many online courses focus too narrowly on steps and scripts, bypassing the critical statistical theories that underpin algorithms, their strengths, and their weaknesses.

We address these shortcomings by reducing the focus on extensive statistical theory typical in academia, concentrating instead on the essential mathematics, statistics, and practical applications necessary to understand and reliably use ML algorithms. This ensures you can establish complete pipelines and communicate results clearly, preparing you for effective professional practice.



No Shortcuts to Mastery: The Path to ML and AI Expertise

In the rapidly evolving fields of Machine Learning and Artificial Intelligence, there are no shortcuts to proficiency. Our curriculum, spanning two comprehensive courses, is meticulously designed to provide the theoretical foundations, essential algorithms, and best practices required to become a competent ML and AI professional.

We currently offer two courses to cover traditional machine learning and recent deep learning models/algorithms. A similar course is taught by the same instructor, a senior data scientist and PhD-level scientist, at the University of Maryland College Park, focusing on practical application of ML and problem-solving.

Course Offerings


Models &


Applied Machine Learning with Python

This fast-paced course covers about 23 commonly used models and algorithms in supervised and unsupervised learning with structured data. It includes recent and powerful models like Light GBM, which are not covered in most of the latest ML textbooks, ensuring students are industry-ready with cutting-edge skills.

Supervised Learning

  • Linear Algorithms: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Logistic Regression, Lasso Regression, Elastic Net Regression, Ridge Regression
  • Nonlinear Algorithms: Decision Trees, Naïve Bayes, K-Nearest Neighors, Support Vector Machines
  • Ensemble Algorithms: Bagging, Random Forest, Gradient Boosting Machines (GBM), XGBoost, Light GBM.

Unsupervised Learning

  • Clustering Algorithms: K-Means Clustering, Hierarchical Clustering, DBSCAN, Agglomerative Clustering, Gaussian Mixture Models
  • Dimensionality Reduction and Feature Learning: PCA (Principal Component Analysis), NMDS (Non-metric Multidimensional Scaling).

An adapted version of this course will be taught at University of Maryland College Park in Summer 2024.



Artificial Intelligence: Theory and Applications

This course delves into the heart of AI, leveraging the transformative power of deep learning and its advanced variants. Each topic is examined with a strong emphasis on practical applications and real-world problem-solving. We analyze both structured and unstructured data, including tabular, image, time-series, and text data.

Core Neural Network Architecture

  • Artificial Neural Network (ANN), Deep Learning

Specialized Neural Network Applications

  • Visual Data Processing: Convolutional Neural Networks (CNNs)
  • Sequential Data Analysis: Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMNs)
  • Advanced Natural Language Processing: Transformer Models.

Innovative Techniques for Specific Task

  • Realistic Data Generation: Generative Adversarial Networks (GANs)
  • Data Compression and Feature Extraction: Autoencoders.
  • Strategic Decision-Making: Reinforcement Learning Models

Upon completing this course, students will be equipped with these capabilities:

Data Preprocessing

Master techniques for effective data preprocessing, handle categorical and imbalanced data efficiently, and employ feature selection strategies to enhance model accuracy and computational efficiency.

Comprehensive Exploratory Data Analysis & Visualization

Gain proficiency in conducting thorough exploratory data analysis, utilizing a myriad of visualization techniques to uncover hidden patterns and insights within data.

Model Selection and Implementation

Acquire the skills to confidently choose, develop, and apply many machine learning algorithms, comprehending each model's theoretical aspects, strengths, and limitations, and fine-tuning models through hyperparameter optimization within an end-to-end analysis pipeline.

Practical Application

Develop the ability to execute comprehensive machine learning workflows, from initial data analysis to model deployment, ready for practical application in diverse real-world scenarios.

Teaching Philosophy and Practice

Emphasis on Practical Application

Our teaching philosophy emphasizes practical application supplemented with adequate theoretical foundations. By minimizing the focus on extensive statistical theory typical of traditional courses, we make room for practical hands-on application. This approach makes the course accessible to students from a variety of backgrounds, requiring minimal prior knowledge in mathematics and statistics.

Interactive Sessions: From Theory to Practice

Each class session combines engaging lectures with practical coding exercises in the Cloud. This dual approach ensures a rich learning environment where theoretical concepts are immediately applied to real-world datasets. Through varied datasets and a range of visualization techniques, students gain the confidence and expertise to adeptly navigate and analyze data. The course culminates in the ability to construct an end-to-end data processing and analysis pipeline, empowering the ability for practical application of the learned ML algorithms.


Instructor Profile

Instructor Profile

Dr. Kumar Mainali
PhD (Ecology, Evolution, and Behavior), MS (Statistics)
The University of Texas at Austin

Expert in Machine Learning

Senior Data Scientist & PhD-Level Scientist with extensive academic and industry experience; teaches a graduate-level ML course at the University of Maryland College Park.


Academic Credentials

Holds separate graduate degrees in Natural Science and Statistics from the University of Texas at Austin, obtained on the same day.


Scientific Journal Publications

Authored 40 research papers, including first-authored papers in esteemed journals such as Science Advances, PLoS Computational Biology, and Global Change Biology.


Research Impact

Visiting Research Scientist at the University of Maryland College Park and Senior Data Scientist at Chesapeake Conservancy; harnessed ML and AI in over a dozen significant projects.


Developer of Libraries/Packages

Developed multiple installable libraries for advanced computation in biodiversity analysis. e.g., see here and here


Recognized Contributions and Funding Success

Attracted over $1.1 million in research grants as Principal Investigator from the National Science Foundation, Electric Power Research Institute, and other organizations; conservation work influenced major legislative outcomes, including citations in Senate hearings.


Editorial and Media Presence

Served as guest editor for the Journal of Biogeography’s special issue on emergent technologies, including ML and AI. Featured in over 50 media outlets across the US and Australia, covering various aspects of research, profile, and interviews.

For more information or to enroll, please contact us at