Hands-on Machine Learning with Python
| Week | Topic | Details |
|---|---|---|
| Week - 01 | Overview of Python programming language | 1. Python Data Structures (list, tuple, string, dictionary) 2. Conditional, Loop 3. alias, map, function 4. Operations - zip, list comprehension 5. Python Class, Object, Inheritance 6. A brief introduction to other popular libraries |
| Week - 02 | Python for Data Science | 1. Numpy array 2. Pandas 3. Data Visualization (Matplotlib, Seaborn) |
| Week - 03 | Exploratory Data Analysis (EDA) | 1. Importing different types of data 2. Identify issues in data (missing values, outliers, unrelated features) 3. Missing data handling (imputation) 4. Feature Engineering 5. Outlier removal 6. Data analysis, finding patterns in data Open Ended Lab - 01 on EDA |
| Week - 04 | Build your first ML model | 1. Introduction to Scikit-learn 2. Data Preprocessing (imputation, normalization, removal) 3. Different ways of validation (cross-validation, stratification) 4. Model training 5. Analyzing predictions (underfitting, overfitting) 6. Example - Regression & Classification problem |
| Week - 05 | ML Pipeline | 1. Different performance measures 2. Feature selection techniques (Filter and Wrapper methods) 3. Hyperparameter tuning |
| Week - 06 | Imbalanced Data handling | 1. Data resampling techniques 2. Cost-sensitive Learning 3. Real-world Application: Cervical Cancer Diagnosis |
| Week - 07 | Explainable AI (XAI) | 1. End-to-end ML pipeline with two real-world datasets 2. Related issues - Data Leakage, AI Bias 3. XAI techniques - SHAP, Lime Open Ended Lab - 02 on ML model |
| Week - 08 | Introduction to Deep Learning | 1. Introduction to TensorFlow 2. Build your first DL/CNN model using TensorFlow 3. CIFAR dataset 4. Cat-vs-dog dataset |
| Week - 09 | Working on Real-world Datasets | 1. Data preparation in Kaggle and Colab 2. Hand-drawn Electric circuit Schematic Components prediction 4. Malaria Parasite Detection from blood smears |
| Week - 10 | Transfer Learning | Overview of Transfer Learning techniques |
For details: https://www.asifnewaz.com/teaching/hands-on-ml-with-python
The course offers practical, hands-on experience in Machine Learning and Deep Learning using Python and TensorFlow. It includes comprehensive guidance on the entire ML pipeline, with many real-world applications.
The provided notebooks come with detailed instructions, and some labs are supplemented with video lectures for additional support. Some Quiz and exercise questions are also provided to test your skills.
Some content is currently being updated. It will be uploaded soon. For any inquiry or assistance, email: eee.asifnewaz@iut-dhaka.edu