Machine Learning Course In Mumbai

Machine Learning Course In Mumbai

Machine Learning Course In Mumbai

Machine Learning Training Thane - Itvedant

Machine learning helps software applications to become more accurate in predicting outcomes.

'Simple and powerful are the two words which define the most modern programming language - Python.'

Machine Learning with Python course fees: Affordable Fees

"Knowledge is always free we charge for trainers time. And if you are unsatisfied at the end of the course or if you feel we failed to deliver whatever is promise then, we refund you your complete fees."

To know more about ML: Visit  https://www.itvedant.com/python-training-course and to know more about Data Science - Visit https://www.itvedant.com/data-science-course

Topics covered in ML is given below:

Google Review of Itvedant - Itvedant is known as the best class for python training

Lessons

    • What is Artificial Intelligence
    • History of Artificial Intelligence
    • Foundations of Artificial Intelligence
      • Philosophy
      • Mathematics
      • Phycology
      • Neuroscience
      • Computer Engineering
    • Overview of Machine Learning
    • Types of Machine Learning
    • Applications of Machine Learning
    • Steps in developing a Machine Learning Application
    • AI Frameworks - Tensorflow
    • Linear Regression
    • Logistic Regression
    • Vectors
    • K-Mean
    • Probability
    • NumPy
    • SciPy
    • Maximum Margin Linear Separators
    • Quadratic Programming solution to finding maximum margin separators
    • Kernels for learning non-linear functions.
    • Using Decision Trees
    • Constructing Decision Trees,
    • Classification and Regression Trees (CART)
    • K-means clustering
    • Hierarchical clustering
      • Agglomerative clustering
      • Divisive clustering
    • Expectation Maximization Algorithm
    • Supervised learning after clustering
    • Radial Basis functions
    • Dimensionality Reduction Techniques
    • Principal Component Analysis
    • Independent Component Analysis
    • Introduction
    • Elements of Reinforcement Learning
    • Model based learning
    • Temporal Difference Learning
    • Generalization
    • Partially Observable States
    • Q-Learning

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