The prevailing trend in online education is the rise of Massive Open Online Courses (MOOCs). Free courses on machine learning, data science, data analysis, and Python are readily available, based on educational programs from top universities such as Stanford, Harvard, and John Hopkins. If you are seeking online data science courses, and comprehensive insights into free machine learning courses to enhance your knowledge, these offerings are a treasure trove of knowledge and expertise.
The majority of free machine learning courses are available in English and can be found on well-known online education platforms like Coursera, edX, and Edureka. Some are also accessible on YouTube.
It’s important to note that free Coursera courses are only available for listening to lectures. To complete assignments or obtain a certificate upon course completion, you need to subscribe or pay for the certificate, which can then be added to your resume.
You can access video materials and course lectures for free. To do so, simply click the “Listen to Course” button at the bottom of the course page on Coursera.
Supervised Machine Learning by Andrew Ng
Provided by: Deeplearning AI
The course Supervised Machine Learning is an introductory course in the Machine Learning Specialization. It can be audited for free. You’ll learn to create machine learning models using Python, utilizing popular machine learning libraries like NumPy and scikit-learn. The course covers topics such as supervised learning, prediction, binary classification, linear regression, and logistic regression. The course instructor is the legendary Andrew Ng, co-founder of Coursera, former head of Google Brain, and a major advocate for machine learning.
Duration: 15 hours
Mathematics for Data Science Free Course
Provided by: Duke University
The course Mathematics for Data Science is designed to teach the fundamentals of mathematics required for advanced data science courses. It’s tailored for students with a school-level math background. The course explains the mathematical principles underlying the field of data science, introducing mathematical concepts and terminology step by step.
Upon completion of this course, students will have a solid grasp of the terminology, notation, concepts, and algebraic rules essential for data science professionals before diving into more advanced material.
Duration: 13 hours
Foundations of Data Science: K-Means Clustering with Python
Provided by: University of London
The course Foundations of Data Science: K-Means Clustering with Python quickly familiarizes you with the foundational concepts of data science. It prepares you for intermediate and advanced courses on the topic. The course emphasizes fundamental mathematical, statistical, and programming skills required for typical data analysis tasks.
Using the example of data clustering, you will explore these foundational concepts and apply basic programming skills essential for mastering data science methods. The course includes mathematical and programming exercises, as well as a small data clustering project using a provided dataset.
Duration: 29 hours
Data Processing with Python
Provided by: Nanjing University
The course Data Processing with Python is designed for beginners in programming. It starts with the basics of Python syntax, then moves on to acquiring data locally and from the internet using Python. The course covers data representation, basic and advanced statistical analysis methods, data visualization, and creating simple graphical interfaces for data display and processing, progressively advancing from level to level.
Duration: 29 hours
Practical Time Series Analysis
Provided by: New York State University
In the course Practical Time Series Analysis, you will work with datasets that involve sequential information, such as stock prices, annual rainfall, solar activity, and agricultural product prices. You will study several mathematical models that can be used to describe processes generating such data. The course covers the basics of data visualization and making predictions based on data.
Duration: 24 hours
Scikit-Learn Course – Machine Learning in Python
Provided by: Freecodecamp
Scikit-learn stands as a no-cost machine learning library designed for the Python programming language. Dive into the realm of machine learning through a comprehensive course focused on utilizing scikit-learn.
Duration: 3 hours
High-Dimensional Data Analysis
Provided by: Harvard University
If you are looking for better understanding of data analysis and interpretation, then this data science course is tailor-made for your interests. On the course High-Dimensional Data Analysis you will deep dive into the mathematical essence of distance, which lays the foundation for embracing the singular value decomposition (SVD) as a means of reducing dimensionality and embracing multi-dimensional scaling. Furthermore, you will delve into the intricate world of the batch effect – a formidable challenge in genomics data analysis – exploring its complexities and unveiling techniques to detect and rectify it.
Notably, you’ll unveil the power of principal component analysis and factor analysis, showcasing a practical application in the realms of data visualization and analysis for high-throughput experimental data.
Duration: 14 hours
Ethical Foundations of Data Science
Provided by: University of Michigan
The course Ethical Foundations of Data Science addresses questions such as data ownership, confidentiality assessment, obtaining informed consent, and the meaning of ethical data use. The course covers ethical and confidential aspects of collecting and managing big data, examines the impact of data science on modern society, principles of fairness, responsibility, and transparency, and delves into advanced practices of responsible data management. It also covers the significance of voluntary disclosure when using metadata to inform basic algorithms and/or complex artificial intelligence systems, as well as advanced practices of responsible data management, including understanding the Fair Information Practice Principles and laws related to the “right to be forgotten.”
Duration: 14 hours
10-Hour Comprehensive Machine Learning Course
Provided by: Edureka
The Comprehensive Machine Learning Course offered by Edureka is a 10-hour journey through the intricacies of machine learning algorithms. This course caters to both beginners and professionals aiming to master complex machine learning algorithms. It encompasses a wide array of topics, including supervised and unsupervised learning, reinforcement learning, classification, clustering, regression, dataset preparation, quantitative and qualitative data analysis, prediction algorithms, Bayes’ theorem, random forest method, entropy, k-means method, SVM, and Markov decision process.
Duration: 10 hours
Machine Learning with Graphs
Provided by: Stanford University
The course Machine Learning with Graphs explores vital research in the field of structure and analysis of big data, as well as models and algorithms that abstract their core properties. This course provides insights into practically analyzing data from large-scale networks and reasoning about them through models of network structure and evolution.
Duration: 35 hours
Accelerated Machine Learning Course with TensorFlow API Interfaces
Provided by: Google
At Google’s Accelerated Machine Learning Course, you’ll gain insights into the distinctions between machine learning and traditional programming. Explore concepts such as loss functions, gradient descent, and building deep neural networks. Additionally, learn to gauge model efficiency. This course includes lectures, video tutorials, practical assignments, and visualizations.
Duration: 15 hours
From Excel to MySQL: Approaches to Business Data Analysis
Provided by: Duke University
The Specialization comprises five courses that demonstrate the utilization of Excel, Tableau, and MySQL for data analysis, prediction, model creation, and data visualization to address challenges and enhance business processes.
Duration: 8 months (5 hours per week)
Process Analysis: Data Processing and Analysis in Action
Provided by: Eindhoven University of Technology
The Process Analysis: Data Processing and Analysis in Action course elucidates fundamental methods of intelligent business process analysis. These methods can be employed to automatically create process models from raw event log data. The course also provides user-friendly software, real-life datasets, and practical skills for immediate application of theory.
The learning journey begins with an overview of approaches and technologies that use event data to support decision-making and (re)design business processes. The course emphasizes process mining as a bridge between data mining and business process modeling.
Duration: 22 hours