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 scientists with industry leadership, research excellence & ML teaching at a top universities
Multiple payment options
Installment plans, discounts available for groups
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.
Industry
Focused
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
Extensive EDA
& Visualization
20
Statistical
Models
Data Science Foundations with Python
This course introduces absolute beginners to key data science concepts and skills using Python, focusing on hands-on coding and practical exercises. It covers essential data processing techniques, comprehensive exploratory data analysis (EDA) and visualization, and 20 foundational models that are routinely used in both industry and academia for group comparison, correlation and prediction. No prior coding or statistics knowledge is required.
By the end, students will be well-prepared for more advanced machine learning courses and will be strong candidates for entry-level data science jobs.
Data Engineering
- Python Basics, Data Structures, Data Wrangling and Preprocessing, Data Aggregation and Group Operations, Advanced Data Processing
Data Exploration
- Exploratory Data Analysis (20 steps)
- Visualization (19 types)
Probability and Statistics
- Probability Theory, Statistical Distributions, Hypothesis Testing
Foundational Models for Group Comparison (A/B testing)
- 12 models for A/B testing and group comparison, suitable for one, two or more groups, normally vs non-normally distributed data, continuous vs categorical data types, paired vs unpaired design
Correlation Analysis
- 4 models for correlation analysis, suitable for linear vs non-linear relationships including complex patterns
Foundational Machine Learning Algorithms
- 4 foundational machine learning algorithms for both supervised (regression and classification tasks) and unsupervised learning (clustering)
24
Models &
Algorithms
Cutting
Edge
Models
Applied Machine Learning with Python
This fast-paced course covers 24 commonly used models and algorithms in supervised and unsupervised learning with structured data. It includes recent and powerful models like LightGBM, which are not covered in most of the latest ML textbooks, ensuring students are industry-ready with cutting-edge skills.
Upon completion, students will be strong candidates for mid-level data science jobs/roles. This course is strongly recommended before taking the advanced course “Artificial Intelligence: Theory and Applications.”
Supervised Learning
- Linear Algorithms: 8 models for regression and classification tasks, utilizing simple to complex approaches with regularization techniques
- Nonlinear Algorithms: 4 algorithms suitable for modeling complex patterns in data through sophisticated prediction techniques for both numerical and categorical data types
- Ensemble Algorithms: 5 powerful algorithms, including very recently developed algorithms, that combine multiple models to enhance accuracy and robustness in predictions
Unsupervised Learning
- Clustering Algorithms: 4 methods for grouping data based on similarities, suitable for discovering patterns and structures without predefined labels
- Dimensionality Reduction and Feature Learning: 3 techniques for reducing the number of variables under consideration or learn significant features from data, enhancing model performance and interpretability
An adapted version of this course was taught at University of Maryland College Park in Summer 2024 as a graduate and upper undergraduate level course.
Deep
Learning
Computer
Vision
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.
Testimonials
A thorough and insightful course led by Dr. Kumar Mainali, which delves into machine learning fundamentals using Python. It includes exploratory data analysis, visualization, predictive modeling, and advanced machine learning algorithms. Dr. Mainali's deep expertise and clear instruction, with comprehensive materials and practical projects, significantly enhance the learning experience. Highly beneficial for integrating machine learning skills into professional work.
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The course is the best refresher for a working professional like myself. After learning several ML algorithms in this class, I have started incorporating this into my daily data analysis tasks. Dr. Mainali is confident and knows his material very well. His style of teaching is in a manner that makes difficult concepts easy to understand. I highly recommend this class to both beginners and working professionals.
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This course, led by Dr. Kumar Mainali, covers an extensive range of topics from algorithm fundamentals to advanced data handling, blending theory with practical applications effectively. Dr. Mainali’s clear instruction and hands-on projects enhance understanding and engagement, making complex concepts accessible. Real-world projects align with industry trends.
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Flexible virtual format with extensive resources and a collaborative learning environment enhances understanding and application of AI principles, suitable for beginners and those looking to deepen their knowledge. The course was well-structured and supportive, making it easier to overcome challenges and deepen my understanding of machine learning, which at the time was basically nothing.
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An accessible and well-tailored course for beginners, especially effective for young learners. Instructor Kumar Mainali excels in adapting to student needs, exemplified by extending the capstone project deadline to ensure thorough understanding. His commitment to student support through regular office hours made challenges manageable, resulting in a highly positive learning experience.
View original sourceInstructor Profile
Instructor Profile
PhD, 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.
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.
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 section 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.
PhD (Physics), MS (Statistics)
The University of Texas at Austin
Expert in Data Science and Machine Learning
Accomplished Senior Data Scientist with over a decade of experience in Fortune 500 tech companies and a dedicated educator, sharing expertise in Data Science, Business Analytics, and AI, including Generative AI and LLM.
Academic Credentials
Holds a PhD in Physics and an MS in Statistics and Data Sciences from The University of Texas at Austin.
Industry Impact
Senior Data Scientist offering solutions for optimization, product recommendation, and sales forecasting at Apple. Over the past decade, contributed to groundbreaking projects across multiple industry-leading companies, including a major telecommunications provider and a global technology firm specializing in networking hardware.
Machine Learning Expertise
Proficient in supervised and unsupervised ML techniques, deep learning, and generative AI, with a particular focus on Large Language Models (LLM) for cutting-edge applications. Designed and developed multiple GPT-based algorithms to solve industry problems.
Research Innovation and Patents
Co-Author of 50 publications in the field of physics and statistics in top journals including Nature. Co-inventor on a US patent for an Orchestration System for Distributed Machine Learning Engines, demonstrating innovative contributions to the field of AI and machine learning.
Passionate Educator of Machine Learning
Co-taught courses co-organized by The University of Texas at Austin covering Data Science, Business Analytics, and AI.
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