Applied Machine Learning courses can help you learn data preprocessing, model selection, feature engineering, and evaluation metrics. You can build skills in implementing algorithms, optimizing performance, and interpreting results in practical contexts. Many courses introduce tools like Python, TensorFlow, and scikit-learn, that support developing machine learning models and applying AI techniques to solve real-world problems.
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 Methods, Machine Learning, Model Training, Model Optimization, Machine Learning Algorithms, Unsupervised Learning, Python Programming, Classification Algorithms, Artificial Neural Networks
★ 4.6 (8.8K) · Intermediate · Course · 1 - 4 Weeks

Johns Hopkins University
Skills you'll gain: Computer Vision, Model Evaluation, PyTorch (Machine Learning Library), Supervised Learning, Unsupervised Learning, Image Analysis, Applied Machine Learning, Data Preprocessing, Dimensionality Reduction, Machine Learning Methods, Reinforcement Learning, Feature Engineering, Machine Learning Algorithms, Convolutional Neural Networks, Regression Analysis, Data Processing, Model Training, Machine Learning, Deep Learning, Model Optimization
★ 3.5 (17) · Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Matplotlib, Feature Engineering, Data Preprocessing, NumPy, Plot (Graphics), Tensorflow, Pandas (Python Package), Data Visualization, Model Training, Statistical Visualization, Data Wrangling, Jupyter, Applied Machine Learning, Seaborn, Statistical Machine Learning, Data Engineering, Machine Learning, Development Environment, Network Model, Network Engineering
Beginner · Specialization · 1 - 3 Months

Multiple educators
Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Training, Applied Machine Learning, Machine Learning Algorithms, Transfer Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Model Evaluation, Responsible AI, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning
★ 4.9 (39K) · Beginner · Specialization · 1 - 3 Months

Google Cloud
Skills you'll gain: Model Deployment, Google Cloud Platform, Natural Language Processing, Tensorflow, MLOps (Machine Learning Operations), Model Evaluation, Computer Vision, Large Language Modeling, Reinforcement Learning, Convolutional Neural Networks, Image Analysis, Transfer Learning, Model Optimization, Model Training, Keras (Neural Network Library), Cloud Deployment, Applied Machine Learning, AI Personalization, Recurrent Neural Networks (RNNs), Machine Learning
★ 4.5 (1.5K) · Advanced · Specialization · 3 - 6 Months

Johns Hopkins University
Skills you'll gain: Computer Vision, Model Evaluation, Supervised Learning, Image Analysis, Data Preprocessing, Applied Machine Learning, Machine Learning Methods, Feature Engineering, Machine Learning Algorithms, Data Processing, Model Training, Model Optimization, Machine Learning, Data Cleansing, Scikit Learn (Machine Learning Library), Machine Learning Software, Data Integration, Data Transformation, Classification Algorithms
★ 3.7 (12) · Intermediate · Course · 1 - 4 Weeks

Skills you'll gain: Model Evaluation, Supervised Learning, Machine Learning Methods, AI Personalization, Classification Algorithms, Machine Learning, Model Training, Logistic Regression, Model Optimization, Data Analysis, Driving engagement, Persona (User Experience)
Intermediate · Course · 1 - 4 Weeks

New York University
Skills you'll gain: Supervised Learning, Machine Learning Methods, Model Evaluation, Reinforcement Learning, Applied Machine Learning, Statistical Machine Learning, Statistical Methods, Dimensionality Reduction, Unsupervised Learning, Machine Learning Algorithms, Artificial Neural Networks, Statistical Modeling, Decision Tree Learning, Predictive Modeling, Financial Trading, Financial Market, Model Training, Machine Learning, Derivatives, Tensorflow
★ 3.7 (825) · Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Model Evaluation, Classification Algorithms, Regression Analysis, Data Science, Statistical Modeling, Predictive Modeling, Machine Learning Methods, Exploratory Data Analysis, Machine Learning, Data Analysis, Applied Machine Learning, Machine Learning Software, Feature Engineering, Random Forest Algorithm, Supervised Learning, Logistic Regression, Data Processing, Model Optimization, Data Manipulation, Data Visualization
Intermediate · Course · 1 - 4 Weeks

Board Infinity
Skills you'll gain: Feature Engineering, Model Deployment, Data Preprocessing, Machine Learning Methods, Scikit Learn (Machine Learning Library), Supervised Learning, Machine Learning Algorithms, Applied Machine Learning, Machine Learning, Test Script Development, Model Evaluation, Unsupervised Learning, Containerization, Data Processing, Data Wrangling, Unit Testing, Development Testing, Software Development, Python Programming, Application Programming Interface (API)
Intermediate · Course · 1 - 4 Weeks

Skills you'll gain: Supervised Learning, Model Optimization, Feature Engineering, Applied Machine Learning, Unsupervised Learning, Model Evaluation, Machine Learning Algorithms, Predictive Modeling, Model Training, Data Preprocessing, Classification Algorithms, Dimensionality Reduction, Data Transformation, Fine-tuning
Advanced · Course · 1 - 3 Months

Skills you'll gain: AWS SageMaker, Unsupervised Learning, Feature Engineering, Time Series Analysis and Forecasting, Model Deployment, Model Optimization, Cloud Deployment, Amazon Web Services, Applied Machine Learning, Model Training, Machine Learning Methods, Amazon S3, Artificial Intelligence and Machine Learning (AI/ML), Cloud Computing, Model Evaluation, Forecasting, Data Preprocessing, Dimensionality Reduction
★ 3.7 (6) · Beginner · Course · 1 - 3 Months
Top-rated Applied Machine Learning courses offered by Johns Hopkins University on Coursera.
Earn a certificate in Applied Machine Learning from top universities and companies.
Applied machine learning is a branch of artificial intelligence that focuses on using algorithms and statistical models to analyze and interpret complex data. It is important because it enables organizations to make data-driven decisions, automate processes, and enhance user experiences. By leveraging applied machine learning, businesses can uncover insights from vast amounts of data, leading to improved efficiency and innovation across various sectors.‎
Careers in applied machine learning are diverse and growing rapidly. Some potential job titles include Machine Learning Engineer, Data Scientist, AI Research Scientist, and Business Intelligence Analyst. These roles often require a blend of programming skills, statistical knowledge, and domain expertise, allowing professionals to work on projects that range from developing predictive models to creating intelligent systems.‎
To succeed in applied machine learning, you should develop a strong foundation in programming languages such as Python or R, as well as proficiency in data manipulation and analysis. Key skills include understanding algorithms, statistical modeling, data visualization, and machine learning frameworks like TensorFlow or Scikit-learn. Additionally, familiarity with cloud platforms and data engineering concepts can be beneficial.‎
There are many excellent online courses available for learning applied machine learning. Some recommended options include the Applied Machine Learning Specialization and Applied Machine Learning: Techniques and Applications. These courses provide a structured learning path and practical experience to help you build your skills.‎
Yes. You can start learning applied machine learning on Coursera for free in two ways:
If you want to keep learning, earn a certificate in applied machine learning, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎
To learn applied machine learning, start by identifying your current skill level and the specific areas you want to focus on. Enroll in introductory courses to build foundational knowledge, then progress to more advanced topics. Engage in hands-on projects to apply what you learn, and consider joining online communities or forums to connect with others in the field for support and collaboration.‎
Typical topics covered in applied machine learning courses include supervised and unsupervised learning, regression analysis, classification techniques, clustering, natural language processing, and model evaluation. Courses often emphasize practical applications and real-world case studies to help learners understand how to implement machine learning solutions effectively.‎
For training and upskilling employees in applied machine learning, consider courses like the IBM Machine Learning Professional Certificate or the Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate. These programs are designed to equip professionals with the necessary skills to apply machine learning techniques in their work.‎