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
★ 3.4 (16) · Intermediate · Specialization · 3 - 6 Months

Board Infinity
Skills you'll gain: Anomaly Detection, Feature Engineering, Fraud detection, Unsupervised Learning, Continuous Monitoring, Autoencoders, MLOps (Machine Learning Operations), Machine Learning Methods, Statistical Machine Learning, Model Training, Time Series Analysis and Forecasting, System Monitoring, Applied Machine Learning, Model Deployment, Statistical Analysis, Taxonomy
Beginner · Course · 1 - 4 Weeks

Skills you'll gain: Computer Vision, Deep Learning, Image Analysis, Convolutional Neural Networks, Exploratory Data Analysis, Feature Engineering, Tensorflow, Model Training, Predictive Modeling, Transfer Learning, Applied Machine Learning, Machine Learning Methods, Application Development, Predictive Analytics, Model Evaluation, Machine Learning, Network Model, Analytics, Network Architecture, Design
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

Skills you'll gain: Natural Language Processing, Large Language Modeling, Fine-tuning, Model Evaluation, Recurrent Neural Networks (RNNs), Data Ethics, Responsible AI, Text Mining, Transfer Learning, PyTorch (Machine Learning Library), Artificial Neural Networks, Data Preprocessing, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Classification Algorithms, Applied Machine Learning, Data Processing, Machine Learning, Data Analysis, Data Cleansing
★ 3.1 (8) · Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Model Evaluation, Sampling (Statistics), NumPy, Pandas (Python Package), Data Literacy, Feature Engineering, Statistical Visualization, Data Analysis, Predictive Modeling, Analytics, Regression Analysis, Predictive Analytics, Statistics, Analysis, Computational Thinking, Data Science, Business Intelligence, Applied Machine Learning, Data Quality, Machine Learning
Beginner · Specialization · 3 - 6 Months

Skills you'll gain: Generative AI, Model Evaluation, Supervised Learning, Generative Model Architectures, Recurrent Neural Networks (RNNs), Unsupervised Learning, Data Preprocessing, Large Language Modeling, Time Series Analysis and Forecasting, Exploratory Data Analysis, LLM Application, Applied Machine Learning, Data Collection, Model Optimization, Convolutional Neural Networks, Model Deployment, Transfer Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Model Training
★ 4.2 (35) · Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Data Preprocessing, Supervised Learning, Model Optimization, Feature Engineering, Pandas (Python Package), Data Wrangling, Exploratory Data Analysis, Data Quality, Model Training, Applied Machine Learning, Data Processing, Data Manipulation, Statistical Machine Learning, Data Transformation, Classification And Regression Tree (CART), Data Cleansing, Data Pipelines, Machine Learning, Data Modeling, Data Architecture
Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Supervised Learning, Computer Vision, Recurrent Neural Networks (RNNs), Machine Learning Methods, Convolutional Neural Networks, Plot (Graphics), Matplotlib, Data Visualization, Probability & Statistics, Deep Learning, AI Personalization, Classification Algorithms, Artificial Intelligence, Plotly, Statistical Analysis, Statistical Methods, Machine Learning, Applied Machine Learning, Digital Signal Processing, Statistical Inference
★ 3.8 (8) · 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

DeepLearning.AI
Skills you'll gain: Model Training, Machine Learning Algorithms, Transfer Learning, Machine Learning, Applied Machine Learning, Data Ethics, Decision Tree Learning, Model Evaluation, Tensorflow, Responsible AI, Supervised Learning, Deep Learning, Classification Algorithms, Random Forest Algorithm, Model Optimization, Artificial Neural Networks, Logistic Regression, Regression Analysis
★ 4.9 (8.8K) · Beginner · Course · 1 - 4 Weeks
Top-rated Applied Machine Learning courses offered by Edureka on Coursera.
Earn a certificate in Applied Machine Learning from top universities and companies.
Top-rated beginner-friendly Applied Machine Learning courses with no prerequisites.
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.‎