Machine-Learning Training

At the Completion on said course the students will have full professional knowlege of coding skills, developing logics & websites/applications, working on live projects.We are the best providers of Python Training in navi mumbai, kharghar with excellent placements. By giving the perfect Python Training in navi mumbai, we differ very much from others.By the expert guidance in learning Python Training in navi mumbai we can proudly say we are the top providers.Enroll yourself for Python course or Python classes in Kharghar Near by following areas are Panvel, Belapur, Kamothe, Nerul, Vashi in Navi Mumbai.

Target Audience

10th & 12th class students Undergraduates, Graduates Post-Graduates & Job aspirants


C & OOPS Concepts would be an advantage


45 days Duration, Classes taken 5 Days a week


5 day in a week, Weekend batch, Only Sunday batch

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  1. Introduction: What is Machine Learning? What is Artificial Intelligence?
  2. Landscape of problems
    1. Supervised versus unsupervised learning
    2. Classification versus forecasting
    3. Classifying data sets: Tall, wide, and dense data
    4. Predictive modeling/ policy intervention
  3. Python basics
    1. Anaconda
    2. Spyder
    3. Numpy, Scipy
    4. Matplotlib
    5. Scikit Learn
  4. Model complexity
    1. Overfitting
    2. Training/validation/testing
  5. Simplest models
    1. Getting started with scikit-Learn
    2. K nearest neighbors
    3. Application: Iris data
    4. Linear regression
  6. Controlling model complexity
    1. Regularization
    2. Ridge regression
    3. Lasso regression
  7. Classification
    1. Logistic regression
    2. Support vectors
    3. Naive Bayes
    4. Linear discriminant
    5. Multi(k>2) class problems
  8. Decision trees
    1. Controlling complexity again
    2. Feature importance and reading trees
    3. Bagging predictors
    4. Multiple trees (The Random Forest)
    5. Boosting
  9. Recommendation systems
    1. Content based recommendation
    2. Collaborative filtering
  10. Clustering
    1. K-means and Hierarchical clustering
    2. Principal component analysis
  11. Model evaluation
    1. Cross validation
    2. Evaluation metrics
    3. Tuning Models