Results for "introduction to applied machine learning"
Alberta Machine Intelligence Institute
Skills you'll gain: Data Preprocessing, Data Ethics, Applied Machine Learning, Machine Learning Methods, Machine Learning, Machine Learning Algorithms, Product Lifecycle Management, Case Studies, Data Collection, Data Capture, Supervised Learning, Business Requirements, Data Quality, Business Analysis, Unsupervised Learning, Artificial Intelligence
Skills you'll gain: Unsupervised Learning, Exploratory Data Analysis, Feature Engineering, Dimensionality Reduction, Supervised Learning, Classification Algorithms, Regression Analysis, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Statistical Methods, Data Preprocessing, Applied Machine Learning, Model Evaluation, Statistical Inference, Predictive Modeling, Machine Learning Methods, Statistical Hypothesis Testing, Model Training, Data Processing, Machine Learning
Skills you'll gain: Model Evaluation, Data Preprocessing, Model Training, Reinforcement Learning, Model Optimization, Deep Learning, Large Language Modeling, PyTorch (Machine Learning Library), Python Programming, Applied Machine Learning, Image Analysis, Machine Learning Methods, Transfer Learning, Natural Language Processing, Tensorflow, Computer Vision, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Machine Learning Algorithms, Convolutional Neural Networks
Skills you'll gain: Generative AI, Generative AI Agents, Google Gemini, Google Cloud Platform, Generative Model Architectures, MLOps (Machine Learning Operations), Prompt Engineering, Tensorflow, AI Workflows, Cloud Infrastructure, Applied Machine Learning, Artificial Intelligence, Data Infrastructure, Multimodal Prompts, Model Deployment, Model Training, Machine Learning, Model Evaluation, Supervised Learning
Imperial College London
Skills you'll gain: Dimensionality Reduction, Linear Algebra, Regression Analysis, NumPy, Calculus, Unsupervised Learning, Applied Mathematics, Statistical Methods, Descriptive Statistics, Model Optimization, Mathematical Software, Jupyter, Statistics, Numerical Analysis, Applied Machine Learning, Geometry, Artificial Neural Networks, Data Science, Data Manipulation, Data Transformation
Multiple educators
Skills you'll gain: Unsupervised Learning, Supervised Learning, Machine Learning Methods, Model Training, Applied Machine Learning, Machine Learning Algorithms, Transfer Learning, Machine Learning, Jupyter, Decision Tree Learning, Model Evaluation, Responsible AI, Tensorflow, Data Ethics, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms
What brings you to Coursera today?
DeepLearning.AI
Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Statistical Methods, Probability Distribution, Linear Algebra, Statistical Inference, Model Optimization, Machine Learning Methods, Statistics, Applied Mathematics, Probability, Calculus, Dimensionality Reduction, Applied Machine Learning, Mathematical Software, Data Transformation, Machine Learning
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, Statistical Modeling, Python Programming, Supervised Learning, Machine Learning, Data Processing, Agentic systems, Artificial Intelligence, Algorithms, AI literacy
Skills you'll gain: Model Evaluation, Model Deployment, Model Training, Model Optimization, Data Preprocessing, MLOps (Machine Learning Operations), Data Cleansing, Microservices, Data Pipelines, Feature Engineering, Data Quality, Containerization, Application Deployment, Service Level, Extract, Transform, Load, Data Transformation, System Monitoring, Machine Learning Methods, Performance Tuning, Machine Learning Algorithms
Johns Hopkins University
Skills you'll gain: Model Evaluation, Predictive Modeling, Machine Learning Algorithms, Model Training, Machine Learning Methods, Feature Engineering, Supervised Learning, Machine Learning Software, Classification And Regression Tree (CART), Predictive Analytics, Applied Machine Learning, Data Preprocessing, R Programming, Classification Algorithms, Machine Learning, Random Forest Algorithm, Regression Analysis
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
Skills you'll gain: Feature Engineering, Decision Tree Learning, Applied Machine Learning, Supervised Learning, Advanced Analytics, Statistical Machine Learning, Machine Learning, Machine Learning Algorithms, Unsupervised Learning, Analytics, Model Training, Random Forest Algorithm, Model Optimization, Predictive Modeling, Model Evaluation, Python Programming, Performance Tuning, Classification Algorithms
In summary, here are 10 of our most popular introduction to applied machine learning courses
- Introduction to Applied Machine Learning: Alberta Machine Intelligence Institute
- IBM Introduction to Machine Learning: IBM
- Python Machine Learning By Example: Packt
- Introduction to AI and Machine Learning on Google Cloud: Google Cloud
- Mathematics for Machine Learning: Imperial College London
- Machine Learning: DeepLearning.AI
- Mathematics for Machine Learning and Data Science: DeepLearning.AI
- AI and Machine Learning Essentials with Python: University of Pennsylvania
- Machine Learning Made Easy for Software Engineers: Coursera
- Practical Machine Learning: Johns Hopkins University
