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    Machine Learning with Python Free Course

    Machine Learning with Python Free Course

    Learn Machine Learning Python and become a certified database professional for free!

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    Prerequisites

    Machine Learning with Python Course Prerequisites
    • Familiarity with Python Programming Language
    • Basic Mathematics and Statistics Knowledge
    • Understanding of Probability and Statistics
    • Basic Data Analysis and Data Manipulation Skills
    Course Prerequisites
    • 450,000 +
      Professionals trained
    • 250+
      Workshops every month
    • 100+
      Countries and counting

    Propel ML Skills with Python

    Propel ML Skills with Python

    24+ hours of Self-Paced Learning Content

    Practice with Guided Hands-On Exercises

    Learn-by-Doing with Immersive Learning

    Test Your Learning with Recall Quizzes

    Unlock Knowledge with Interactive eBooks

    Accelerate Progress with Auto-Graded Assessments

    Build on your foundational knowledge of Machine Learning and develop solutions for complex business problems using the capability of machine learning. Learn advanced concepts and understand them by practicing your learning in hands-on exercises. Create machine learning models and explore the right methods to evaluate such models.

    Design, develop and deploy machine learning models that provide business value to the customer and to the organization as well. Learn how and when to use supervised learning and unsupervised learning. You will also understand various machine learning concepts like feature engineering, model optimization, and more to level up your skills as a machine learning specialist.

    This machine learning with python free course videos is based on actionable insights from machine learning experts with active experience in various industries.

    The course will help you advance your machine learning skills for free. On completing the course, you will also get a certificate of completion issued by KnowledgeHut. Not only with this course help you learn Machine Learning with Python will also put you on the path to become an in-demand machine learning professional.

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    Who Can Attend the Course

    Who Should Attend
    • Software Engineers
    • Data Engineers
    • Data Analysts
    • Data Scientists
    • Frontend Developers
    • Backend Developers
    • Full Stack Developers
    • Economists
    • Programmers
    • Statisticians
    • Researchers
    Can I learn
    What You Will Learn
    1
    Linear Algebra

    Learn how to use linear algebra in creating effective machine learning algorithms.

    2
    Calculus

    Familiarize yourself with the Calculus techniques used in machine learning.

    3
    Machine Learning Fundamentals

    Master the foundational concepts in machine learning and how to apply them wisely.

    4
    Linear Regression

    Learn to use linear regression to make a machine learning model for predictive analysis.

    5
    Lasso Regression

    Leverage Lasso regression to build more accurate prediction models with Python.

    6
    Ridge Regression

    Learn how and when to use ridge regression and how it produces better results.

    WHY KNOWLEDGEHUT?

    The KnowledgeHut Edge

    Superior Outcomes

    Focus on skilled-based outcomes with advanced insights from our state-of-the art learning platform.

    Immersive Learning

    Go beyond just videos and learn hands-on with guided exercises, projects, assignments and more.

    Continual Support

    Learn better with support along the way. Get 24/7 help, stay unblocked and ramp up your skills.

    World-Class Instructors

    Course instructors and designers from top businesses including Google, Amazon, Twitter, and IBM.

    Real-World Learning

    Get an intimate, insider look at leading companies in the field through real-world case studies.

    Industry-Vetted Curriculum

    Six months of post-training mentor guidance to overcome challenges in your web development career.
    Skills You Will Gain
    • Understand Machine Learning Concepts
    • Data Preprocessing
    • Data Exploration
    • Feature Engineering
    • Building Machine Learning Models
    • Model Evaluation
    • Supervised Learning Algorithms
    • Unsupervised Learning Algorithms
    • Model Optimization
    • Hyperparameter Tuning
    Whoshouldlearn image

    Machine Learning with Python Free Course Curriculum

    Curriculum

    1. Linear Algebra

    Learning Objective:

    Learn how to use linear algebra to represent and manipulate data and in creating mathematical models.

    2. Calculus

    Learning Objective:

    Learn how you can use calculus to understand and optimize machine learning algorithms.

    3. Machine Learning Fundamentals

    Learning Objective:

    Understand the difference between statistical learning and machine learning and how ML approaches use statistical functions under the hood to find good models. Learn important machine learning concepts such as overfitting and cross-validation for overfitting detection and model evaluation. Additionally, understand bias, variance and the bias-variance tradeoffs in the context of machine learning models.

    Topics

    • The Training and Testing Paradigm / Introduction to scikit-learn
    • Model Evaluation Methods (Subtopics: Deviance, AIC, Pseudo R-squared)
    • Model Performance Measures (Subtopics: Confusion Matrix and Cross Entropy)
    • Imbalanced Data Subtopics: What is Imbalanced Data and How to Deal with It - Oversampling, Undersampling and SMOTE)
    • Overfitting and Cross Validation (K-fold Cross Validation, LOOCV)
    • Bias and Variance, Trade-offs
    • Hyper Parameters and Grid Search
    • Case Study on Linear Regression with scikit-learn

    4. Linear Regression

    Learning Objective:

    Cover the simplest form of statistical learning: linear regression, including simple linear regression and multiple linear regression. Understand topics of covariance, correlations and method of least squares. Significance of parameters, methods to assess fit and assumptions about the data are covered to understand the results and validity of applying linear regression.

    Topics

    • Covariance and Correlation
    • Simple Linear Regression
    • Slope and Intercept, Interpretation
    • OLS Method and Assumptions
    • Introduction to Statsmodels
    • Multiple Linear Regression
    • Beta Coefficient, Significance and Their Interpretation
    • Errors and Metrics Related to Errors, R^2 and Adj R^2
    • Linear Regression Diagnostic Plots

    Machine Learning with Python Free Certification

    Machine Learning with Python Free Certification

    1. What is Machine Learning with Python?

    Python is one of the most popular languages being used today. Machine Learning is one of the fastest growing technologies in the world right now and is being used across all industries by leading organizations. Naturally, Python has become the language of choice for ML professionals.

    There is a great demand for machine learning experts to build accurate models that help organizations benefit from this modern technology. Machine learning is used to build models that can make predictions or take decisions based on business requirements and can learn without human intervention to get better and more accurate results.

    For individuals interested in delving into the field of machine learning with Python, there are excellent opportunities to enhance their knowledge and skills through free machine learning Python courses. These courses offer valuable resources and instruction, enabling learners to grasp the fundamentals of machine learning, understand the practical implementation of algorithms, and gain proficiency in using Python libraries and frameworks for data analysis and model building.

    2. Why learn Python for Machine Learning?

    Machine Learning is an emerging technology that is rapidly developing and being deployed around the world in every industry. Python is widely used in Machine Learning and hence there is a huge community of ML professionals building and collaborating on ML projects using Python.

    While there are many languages that are used in ML, Python is the most popular and hence, an ML professional would benefit a lot from learning to work with Python. Using Python for machine learning would also make it easier for you to work and collaborate on ML projects. With the Python machine learning course for free you can give quite a boost to your ML career. Enroll in a free Python machine learning course to give a significant boost to your ML career.

    3. What are the prerequisites required to learn the Python for Machine Learning course?

    This free machine learning with Python course is not suitable for beginners. It is designed for learners who are not new to machine learning and Python hence we do not cover the basics since it would not be valuable to the learner. You should meet the following criteria for you to benefit from this course:

    Have a familiarity with Python programming language and how to work with it.

    Possess basic mathematics and statistics knowledge that is needed to understand some of the concepts covered in the course.

    Understanding of probability and statistics is also necessary to grasp some of the key concepts.

    At least basic data analysis and data manipulation skills are also required for learners to start applying what they learn.