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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 Methods, Machine Learning, Model Training, Model Optimization, Machine Learning Algorithms, Unsupervised Learning, Python Programming, Classification Algorithms, Artificial Neural Networks

    4.6
    Rating, 4.6 out of 5 stars
    ·
    8.8K 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, 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
    Rating, 3.4 out of 5 stars
    ·
    16 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Supervised Machine Learning: Regression and Classification

    Skills you'll gain: Supervised Learning, Applied Machine Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, Model Training, NumPy, Machine Learning Algorithms, Predictive Modeling, Classification Algorithms, Feature Engineering, Artificial Intelligence, Model Evaluation, Data Preprocessing, Python Programming, Logistic Regression, Model Optimization, Regression Analysis, Algorithms

    4.9
    Rating, 4.9 out of 5 stars
    ·
    32K reviews

    Beginner · Course · 1 - 4 Weeks

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

    Edureka

    Applied Machine Learning Without Coding

    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

  • Status: Free Trial
    Free Trial
    D
    S

    Multiple educators

    Machine Learning

    Skills you'll gain: Unsupervised Learning, Supervised Learning, Machine Learning Methods, 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

    4.9
    Rating, 4.9 out of 5 stars
    ·
    39K reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    C

    Coursera

    Machine Learning with Scikit-learn, PyTorch & Hugging Face

    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, Machine Learning Software

    4.4
    Rating, 4.4 out of 5 stars
    ·
    27 reviews

    Intermediate · Professional Certificate · 3 - 6 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    G

    Google Cloud

    Advanced Machine Learning on Google Cloud

    Skills you'll gain: Model Deployment, Model Optimization, Convolutional Neural Networks, Google Cloud Platform, Natural Language Processing, Tensorflow, MLOps (Machine Learning Operations), Large Language Modeling, Reinforcement Learning, Model Training, Transfer Learning, Computer Vision, Keras (Neural Network Library), Systems Design, Applied Machine Learning, Image Analysis, AI Personalization, Cloud Deployment, Recurrent Neural Networks (RNNs), Machine Learning

    4.5
    Rating, 4.5 out of 5 stars
    ·
    1.5K reviews

    Advanced · Specialization · 3 - 6 Months

  • 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)

    4.8
    Rating, 4.8 out of 5 stars
    ·
    13 reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    N

    New York University

    Machine Learning and Reinforcement Learning in Finance

    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
    Rating, 3.7 out of 5 stars
    ·
    820 reviews

    Intermediate · Specialization · 3 - 6 Months

  • 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 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

    4.7
    Rating, 4.7 out of 5 stars
    ·
    747 reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    P

    Packt

    Applied Machine Learning and Model Optimization

    Skills you'll gain: Supervised Learning, Model Optimization, Feature Engineering, Applied Machine Learning, Unsupervised Learning, Model Evaluation, Machine Learning Methods, Statistical Machine Learning, Machine Learning Algorithms, Predictive Modeling, Model Training, Data Preprocessing, Classification Algorithms, Artificial Intelligence and Machine Learning (AI/ML), Dimensionality Reduction, Data Transformation, Fine-tuning

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Washington

    Machine Learning

    Skills you'll gain: Model Evaluation, Classification Algorithms, Regression Analysis, Applied Machine Learning, Machine Learning Methods, Feature Engineering, Machine Learning, Image Analysis, Machine Learning Algorithms, AI Personalization, Unsupervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Predictive Modeling, Supervised Learning, Bayesian Statistics, Statistical Machine Learning, Model Training, Logistic Regression, Statistical Modeling, Data Mining

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

    Intermediate · Specialization · 3 - 6 Months

1234…709

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
  • Supervised Machine Learning: Regression and Classification : DeepLearning.AI
  • Applied Machine Learning Without Coding: Edureka
  • Machine Learning: DeepLearning.AI
  • Machine Learning with Scikit-learn, PyTorch & Hugging Face: Coursera
  • Advanced Machine Learning on Google Cloud: Google Cloud
  • AI Driven Machine Learning with Python: EDUCBA
  • Machine Learning and Reinforcement Learning in Finance: New York University
  • Introduction to Applied Machine Learning: Alberta Machine Intelligence Institute

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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