The objective of this course is to impart knowledge about basic data science and application in Industries.

Sr. No. | Topic | Sub Topic | Theory (Hours) | LAB (Hours) |
---|---|---|---|---|

Welcome to Machine Learning | Introduction To Machine Learning | 2 | 1 | |

Introduction of Data Science, ML and AI | ||||

Concept of Data Science - What/When/Why | ||||

Artificial Intelligence Evolution | ||||

Find out where Machine Learning is applied in Technology and Science. Applications in real scenarios | ||||

Basics of Python | Basic programming syntax of Python, File Handling | 3 | 2 | |

Fundamental of Machine Learning Categories | Supervised Learning | 1 | 1 | |

Unsupervised Learning | ||||

Reinforcement Learning | ||||

Working with Data | Reading Files, Scraping the Web, Cleaning, Munging and Manipulating | 2 | 2 | |

Visualizing Data | Bar Charts, Line Charts, Scatterplots | 1 | 1 | |

Statistics | Describing a Single Set of Data Correlation and Causation | 1 | ||

Mathematical Foundations | Dependence and Independence, Conditional Probability, Bayes’s Theorem, Random Variables, Continuous Distributions, The Normal Distribution | 3 | ||

Machine Learning Python Packages | Data Analysis Packages | 3 | 2 | |

NumPy | ||||

Matplotlib | ||||

Pandas | ||||

Sklearn | ||||

Supervised Machine Learning | Regression | 1 | 1 | |

Classification | ||||

Generalization, Overfitting and Underfitting | ||||

UnSupervised Machine Learning | Challenges in unsupervised learning | 1 | 1 | |

Supervised Machine Learning Algorithms | Linear models | 2 | 2 | |

k-Nearest Neighbor | ||||

Naive Bayes Classifiers | ||||

Decision trees | ||||

Supervise ML - Classification | Classification - LogisticRegression, Sigmoid Function | 1 | 1 | |

Bagging and Boosting | Random Forest | 1 | 1 | |

Supervised ML - Regression | Understand how continuous supervised learning is different from discrete learning | 2 | 1 | |

Code a Linear Regression in Python with scikit-learn | ||||

UnSupervise ML - Clustering | k-Means clustering | 2 | 1 | |

Introduction Neural Networks | Genearl introduction of Neural Networks-Learning And Generalization, Overview of Deep Learning | 1 | ||

Prepare Your Data For Machine Learning | Need For Data Pre-processing | 2 | 2 | |

Data Transforms | ||||

Rescale Data | ||||

Standardize Data | ||||

Normalize Data | ||||

Binarize Data | ||||

Evaluate the Performance of Machine Learning Algorithms with Resampling | Evaluate Machine Learning Algorithms | 2 | 2 | |

Split into Train and Test Sets | ||||

K-fold Cross Validation | ||||

Leave One Out Cross Validation | ||||

Repeated Random Test-Train Splits | ||||

Understand different error metrics such as MSE and MAE in the context of Machine Learning. | ||||

Which Techniques to Use When | ||||

Examples of Real-time application of ML | Weather forecasting, Stock market prediction, Object recognition, Real Time Sentiment Analysis, etc. | 2 | 4 | |

Industrial use case walk through | Industrial use case walk through for supervised learning | 2 | 4 | |

Industrial use case walk through for unsupervised learning |

(Associate Professor, Dept. of Computer Science and Engineering)

karkare@cse.iitk.ac.in

https://www.cse.iitk.ac.in/users/karkare/