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Advanced Machine Learning with Python | Courses Certification Training


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Courses Overview

Machine Learning @YCSPL  Green Park Delhi

  1. Crash Course on Python - Conditions, Loops, Data structures (list, tuples, array)


  1. Python Tools for Data Analysis and Machine Learning: Numpy, Pandas, Matplotlib                                                                                                            
  2. Basics of Data Science                                                                                      
  • Understanding different types of data
  • Data pre-processing
  • Data scaling & normalization
  • Dealing with missing and skewed data
  • Feature scaling
  • Distance metrics
  1. Machine Learning:
  1. Supervised Learning: Introduction
  1. Regression:                                                                                                                                      
    1. Introduction
    2. Real-world problems
    3. Regression algorithms (Simple Linear Regression, Multiple Linear Regression, Lasso Regression, Ridge Regression, K-Nearest Neighbor) – Theory and Implementation
    4. Evaluation Metrics – MAE, MSE, RMSE
    5. Mini-project (open-source dataset/Kaggle)
  2. Common concepts and best practices in Machine Learning                                          
    1. Overfitting
    2. Regularization
    3. Cross-Validation
  3. Classification:                                                                                                                               
    1. Introduction
    2. Real-world problems
    3. Classification algorithms (Logistic Regression, K-Nearest Neighbor , Naïve Bayes, Decision Tree, Random Forest, Ensemble Learning – Bagging, Boosting) – Theory and Implementation
    4. Evaluation Metrics- Precision, Recall, F-Measure, ROC curve
    5. Mini-project (open-source dataset/Kaggle)
  1. Unsupervised Learning                                                                                                                        
    1. Clustering :
      1. Introduction
      2. Real-world problems
      3. Clustering Algorithms (Hierarchichal, Agglomerative)
      4. Evaluation Metrics
      5. Mini-project (open-source dataset/Kaggle)

  • Duration: 35
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