Machine Learning Course

Thane | Andheri | Online

Admission open : 09th July 2020

Weekdays & Weekend Batches

(Rated 4.6 based on 112 customer reviews)

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Machine Learning Training Course In Mumbai & 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

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

Machine Learning Course In Mumbai

Machine Learning Course In Mumbai

Lessons

  1. Introduction

    • What is Data Science
    • Who is a Data Scientist
    • Types of Data
    • Machine learning introduction
    • Deep learning and AI introduction
  2. Descriptive statistics

    • Introduction to statistics
    • Mean
    • Median and mode
    • Variance and Standard deviation
    • Outliers and Inter quartile range
    • Covariance and correlation coefficient
    • Normal distribution
    • Skewness
  3. Probablity

    • Introduction to probability
    • Theoretical and Frequentest approach
    • Dependent and Independent events
    • Conditional probability
    • Bayes Theorem
    • Binomial distribution
    • Standardization and calculate Z score
  4. Inferential statistics

    • Introduction to inferential statistics
    • Sampling techniques
    • Central limit theorem
    • Hypothesis testing
  5. Machine learning

    • Linear regression
    • OLS
    • Gradient descent
    • Implementation with Scikit learn
    • Evaluation metrics (MSE, R2)
  6. Regularization

    • Cost functions
    • Bias variance tradeoff
    • Ridge and Lasso regularization
    • Hyper-parameter tuning
  7. EDA and Preprocessing

    • Handling missing values
    • Handling outliers Handling skewness
    • Scaling
    • Label Encoding
    • Feature engineering
  8. Feature selection

    • Co-relation coefficient and heatmap
    • Chi square test
    • ANNOVA
    • Random Feature elimination
    • PCA
  9. Classification

    • Logistic regression
    • Odds ratio and Sigmoid function
    • Confusion matrix
    • Accuracy, Recall, Precision, F1 score
    • AUC-ROC
  10. Descision Tree

    • Introduction to decision tree
    • Terminologies
    • Entropy and Gini index
    • Visualizing decision tree
    • Handling over fitting (Pruning)
  11. Ensembling

    • Naive Aggregation
    • Bootstrap Aggregation and RandomForest
    • Stacking
  12. Gradient boosting

    • AdaBoosting
    • Gradient boosting algorithm
    • XGBoosting
  13. Clustering

    • Introduction to unsupervised learning
    • Introduction to clustering
    • K-Means
    • Hierarchical clustering
  14. Support Vector Machines

    • Linear SVM
    • Soft margin
    • SVM kernel¬†functions
    • Multi-class Classification
  15. Case Study

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