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  • Applied Machine Learning

Applied Machine Learning Courses

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.


Popular Applied Machine Learning Courses and Certifications


  • Status: Free Trial
    Free Trial
    U

    University of Michigan

    Applied Machine Learning in Python

    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

    4.6
    Rating, 4.6 out of 5 stars
    ·
    8.7K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Applied Machine Learning

    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, Reinforcement Learning, Feature Engineering, Machine Learning Algorithms, Convolutional Neural Networks, Regression Analysis, Data Processing, Machine Learning, Data Mining, Data Cleansing, Deep Learning, Artificial Neural Networks

    3.5
    Rating, 3.5 out of 5 stars
    ·
    14 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    D
    S

    Multiple educators

    Machine Learning

    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

    4.9
    Rating, 4.9 out of 5 stars
    ·
    38K reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Supervised Machine Learning: Regression and Classification

    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

    4.9
    Rating, 4.9 out of 5 stars
    ·
    31K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    U

    University of Washington

    Machine Learning

    Skills you'll gain: Model Evaluation, Classification Algorithms, Regression Analysis, Applied Machine Learning, Feature Engineering, Machine Learning, Image Analysis, Unsupervised Learning, Predictive Modeling, Supervised Learning, Bayesian Statistics, Logistic Regression, Statistical Modeling, Artificial Intelligence, Data Preprocessing, Deep Learning, Data Mining, Decision Tree Learning, Computer Vision, Statistical Machine Learning

    4.6
    Rating, 4.6 out of 5 stars
    ·
    16K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    E

    EDUCBA

    AI Driven Machine Learning with Python

    Skills you'll gain: Model Evaluation, Classification Algorithms, Regression Analysis, Matplotlib, Feature Engineering, Time Series Analysis and Forecasting, Data Preprocessing, Jupyter, Image Analysis, Cloud Deployment, Scikit Learn (Machine Learning Library), Applied Machine Learning, Tensorflow, Amazon Web Services, Python Programming, Data Transformation, Logistic Regression, Machine Learning Methods, Machine Learning, Artificial Intelligence and Machine Learning (AI/ML)

    Beginner · Specialization · 1 - 3 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    A

    Alberta Machine Intelligence Institute

    Introduction to Applied Machine Learning

    Skills you'll gain: Data Preprocessing, Data Ethics, Applied Machine Learning, Machine Learning, Machine Learning Algorithms, Product Lifecycle Management, Case Studies, Data Collection, Supervised Learning, Business Requirements, Data Quality, Business Analysis, Unsupervised Learning, Artificial Intelligence

    4.7
    Rating, 4.7 out of 5 stars
    ·
    746 reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    N

    New York University

    Machine Learning and Reinforcement Learning in Finance

    Skills you'll gain: Supervised Learning, Model Evaluation, Reinforcement Learning, Applied Machine Learning, Machine Learning, Statistical Methods, Dimensionality Reduction, Unsupervised Learning, Artificial Neural Networks, Decision Tree Learning, Predictive Modeling, Financial Trading, Financial Market, Derivatives, Scikit Learn (Machine Learning Library), Markov Model, Regression Analysis, Deep Learning, Market Liquidity, Tensorflow

    3.7
    Rating, 3.7 out of 5 stars
    ·
    819 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Preview
    Preview
    D

    Duke University

    Introduction to Machine Learning

    Skills you'll gain: PyTorch (Machine Learning Library), Logistic Regression, Transfer Learning, Reinforcement Learning, Convolutional Neural Networks, Deep Learning, Image Analysis, Applied Machine Learning, Natural Language Processing, Machine Learning, Recurrent Neural Networks (RNNs), Artificial Neural Networks, Supervised Learning, Unsupervised Learning, Python Programming, Computer Vision, Medical Imaging

    4.7
    Rating, 4.7 out of 5 stars
    ·
    3.8K reviews

    Intermediate · Course · 1 - 3 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    C

    Coursera

    Advanced Machine Learning Techniques

    Skills you'll gain: Transfer Learning, Data Preprocessing, Hugging Face, Model Evaluation

    Intermediate · Course · 1 - 3 Months

  • Status: Preview
    Preview
    U

    University of London

    Machine Learning for All

    Skills you'll gain: Data Preprocessing, Machine Learning, Artificial Intelligence, Model Evaluation, Data Analysis, Image Analysis, Data Collection, Classification Algorithms

    Build toward a degree

    4.7
    Rating, 4.7 out of 5 stars
    ·
    3.5K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    S

    SkillUp

    Machine Learning for Medical Data

    Skills you'll gain: Healthcare Ethics, Health Informatics, Machine Learning, Deep Learning, Machine Learning Software, Clinical Informatics, Statistical Machine Learning, Convolutional Neural Networks, Data Preprocessing, Predictive Modeling, Electronic Medical Record System, Machine Learning Algorithms, Recurrent Neural Networks (RNNs), Model Evaluation, Data Analysis

    Intermediate · Course · 1 - 4 Weeks

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In summary, here are 10 of our most popular applied machine learning courses

  • Applied Machine Learning in Python: University of Michigan
  • Applied Machine Learning: Johns Hopkins University
  • Machine Learning: DeepLearning.AI
  • Supervised Machine Learning: Regression and Classification : DeepLearning.AI
  • Machine Learning: University of Washington
  • AI Driven Machine Learning with Python: EDUCBA
  • Introduction to Applied Machine Learning: Alberta Machine Intelligence Institute
  • Machine Learning and Reinforcement Learning in Finance: New York University
  • Introduction to Machine Learning: Duke University
  • Advanced Machine Learning Techniques: Coursera

Skills you can learn in Machine Learning

Python Programming (33)
Tensorflow (32)
Deep Learning (30)
Artificial Neural Network (24)
Big Data (18)
Statistical Classification (17)
Reinforcement Learning (13)
Algebra (10)
Bayesian (10)
Linear Algebra (10)
Linear Regression (9)
Numpy (9)

Frequently Asked Questions about Applied Machine Learning

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:

  1. Preview the first module of many applied machine learning courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

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.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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