Machine Learning with Python

About the course

- Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves
- Prerequisite : Basic Knowledge on Descriptive & Inferential Statistics
- Eligibility : MS/M.Tech/B.E/B.Tech/M.C.A/M.B.A/M.sc in Science/M.com
- Duration : Duration : 3 Months(60 Hours) : Only Saturday & Sunday

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Course Details

Python for Data Science |

Introduction to Machine Learning & Examples |

Introduction to Cluster Analysis |

About Agglomerative Hierarchical Clustering |

K-Means Procedure & Medoid Cluster Analysis |

Introduction to Dimensionality Reduction |

About Principal Component Analysis & Examples |

Introduction to Association Rules |

About Market Basket Analysis |

Apriori/Support/Confidence/Lift & Examples |

Introduction to Bayes Law |

About Naïve Bayes Algorithm & Examples |

About Variance-Bias Tradeoff |

About Gradient Decent/Ascent Procedures |

About Maximum Likelihood Method |

About Logistic Regression & Examples |

About K-NN Classifier (Nearest-Neighbor Methods) & Examples |

Introduction to Tree based Methods |

Introduction to Decision Trees & Examples |

About Ensemble Methods |

Introduction to Random Forest |

About Bagging & Bootstrap & Examples |

Introduction to Support Vector Machines |

About Maximum Marginal Classifier & Support Vector Classifier |

Kernel Trick & SVM with more than Two Classes & Examples |

Introduction to Neural Networks |

About Single & Multi-Layer Perceptron |

About Forward Feed & Backward Propagation & Examples |

About Ridge Regression |

About Lasso Regression & Examples |

Introduction to Simple Linear Regression |

Introduction to Multiple Linear Regression |

Estimation of Model Parameters |

Hypothesis Testing in Multiple Linear Regression |

Model Adequacy Checking |

Variable Selection Methods & Examples |

Polynomial Regression |

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