
Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Evaluation, Regression Analysis, Scikit Learn (Machine Learning Library), Applied Machine Learning, Predictive Modeling, Machine Learning, Dimensionality Reduction, Decision Tree Learning, Python Programming, Logistic Regression, Classification Algorithms, Feature Engineering
Intermediate · Course · 1 - 3 Months

University of Colorado Boulder
Skills you'll gain: Recurrent Neural Networks (RNNs), Vision Transformer (ViT), PyTorch (Machine Learning Library), Keras (Neural Network Library), Scikit Learn (Machine Learning Library), Large Language Modeling, Natural Language Processing, Embeddings, Network Model, Network Architecture, Algorithms
Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Data Import/Export, Programming Principles, Web Scraping, File I/O, Python Programming, Jupyter, Data Structures, Pandas (Python Package), Data Manipulation, JSON, Computer Programming, Restful API, NumPy, Object Oriented Programming (OOP), Application Programming Interface (API), Automation, Data Analysis
Beginner · Course · 1 - 3 Months

Multiple educators
Skills you'll gain: Unsupervised Learning, Supervised Learning, Transfer Learning, Machine Learning, Jupyter, Applied Machine Learning, Data Ethics, Decision Tree Learning, Model Evaluation, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Data Preprocessing
Beginner · Specialization · 1 - 3 Months

Skills you'll gain: Exploratory Data Analysis, Data Analysis, Data Import/Export, Data Manipulation, Data Transformation, Predictive Modeling, Data Cleansing, Data Preprocessing, Model Evaluation, Predictive Analytics, Pandas (Python Package), Regression Analysis, Feature Engineering, Statistical Analysis, Matplotlib, Scikit Learn (Machine Learning Library), Data Visualization, NumPy, Python Programming
Intermediate · Course · 1 - 3 Months
University of Michigan
Skills you'll gain: Feature Engineering, Model Evaluation, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning, Decision Tree Learning, Unsupervised Learning, Python Programming, Random Forest Algorithm, Regression Analysis, Classification Algorithms, Artificial Neural Networks
Intermediate · Course · 1 - 4 Weeks

DeepLearning.AI
Skills you'll gain: Supervised Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, NumPy, Predictive Modeling, Classification Algorithms, Feature Engineering, Artificial Intelligence, Model Evaluation, Data Preprocessing, Python Programming, Logistic Regression, Regression Analysis, Unsupervised Learning
Beginner · Course · 1 - 4 Weeks

DeepLearning.AI
Skills you'll gain: Large Language Modeling, Prompt Engineering, Artificial Intelligence, Jupyter, Python Programming, Data Analysis, AI Enablement, Application Development, Scripting, Programming Principles, Automation, Application Programming Interface (API), Debugging, Data Structures
Beginner · Course · 1 - 4 Weeks

Imperial College London
Skills you'll gain: Linear Algebra, Dimensionality Reduction, NumPy, Regression Analysis, Calculus, Applied Mathematics, Data Preprocessing, Unsupervised Learning, Feature Engineering, Machine Learning Algorithms, Jupyter, Advanced Mathematics, Statistics, Artificial Neural Networks, Algorithms, Mathematical Modeling, Python Programming, Derivatives
Beginner · Specialization · 3 - 6 Months

EDHEC Business School
Skills you'll gain: Investment Management, Portfolio Management, Text Mining, Portfolio Risk, Asset Management, Network Analysis, Data Visualization Software, Investments, Machine Learning Methods, Return On Investment, Unstructured Data, Predictive Modeling, Web Scraping, Machine Learning, Social Network Analysis, Financial Statements, Applied Machine Learning, Financial Market, Financial Modeling, Risk Management
Beginner · Specialization · 3 - 6 Months

University of Pennsylvania
Skills you'll gain: Statistical Machine Learning, Data Preprocessing, Model Evaluation, PyTorch (Machine Learning Library), Statistical Methods, Probability, Probability & Statistics, Sampling (Statistics), Logistic Regression, Deep Learning, Probability Distribution, Supervised Learning, Statistics, Machine Learning Methods, Machine Learning, Agentic systems, Artificial Intelligence, Artificial Neural Networks, Algorithms, Python Programming
Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Object Oriented Programming (OOP), Data Structures, Data Cleansing, Python Programming, Data Analysis, NumPy, Pandas (Python Package), Data Manipulation, Analytical Skills, Scripting, Algorithms, Debugging
Beginner · Course · 1 - 4 Weeks
Python machine learning is a subset of artificial intelligence that focuses on the development of algorithms that allow computers to learn from and make predictions based on data. It is important because it enables businesses and individuals to automate processes, gain insights from large datasets, and improve decision-making. As industries increasingly rely on data-driven strategies, proficiency in Python machine learning becomes a valuable asset for professionals looking to enhance their skills and career prospects.‎
With skills in Python machine learning, you can pursue a variety of roles in the tech industry and beyond. Common job titles include data scientist, machine learning engineer, AI researcher, and business analyst. These positions often involve analyzing data, developing predictive models, and implementing machine learning solutions to solve real-world problems. The demand for professionals with expertise in Python machine learning continues to grow, making it a promising career path.‎
To succeed in Python machine learning, you should develop a strong foundation in programming, particularly in Python. Key skills include understanding machine learning algorithms, data preprocessing, model evaluation, and data visualization. Familiarity with libraries such as NumPy, pandas, and scikit-learn is also essential. Additionally, a grasp of statistics and linear algebra will enhance your ability to understand and implement machine learning techniques effectively.‎
There are several excellent online courses available for learning Python machine learning. Some notable options include the AI and Machine Learning Essentials with Python Specialization and the Machine Learning: Theory and Hands-on Practice with Python Specialization. These courses provide a comprehensive introduction to the concepts and practical applications of machine learning using Python.‎
Yes. You can start learning Python machine learning on Coursera for free in two ways:
If you want to keep learning, earn a certificate in Python machine learning, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎
To learn Python machine learning, start by familiarizing yourself with the basics of Python programming. Once comfortable, explore online courses that cover machine learning concepts and techniques. Engage in hands-on projects to apply what you've learned, and utilize resources like tutorials and forums for additional support. Consistent practice and real-world application will help reinforce your understanding and build your confidence in this field.‎
Typical topics covered in Python machine learning courses include supervised and unsupervised learning, regression analysis, classification techniques, clustering, and neural networks. Courses often emphasize practical applications, such as data preprocessing, feature selection, and model evaluation. By exploring these topics, you will gain a well-rounded understanding of how to implement machine learning solutions effectively.‎
For training and upskilling employees or the workforce in Python machine learning, consider courses like the Applied Machine Learning with Python and the Machine Learning, Data Science and Generative AI with Python Specialization. These programs are designed to equip learners with practical skills and knowledge that can be directly applied in the workplace.‎