Discover the Data Science Masterclass repository! Learn Python, stats, ML, DL, NLP, MLOps, cloud, and more. Join our batch for hands-on projects and expert guidance. Unlock the power of data today!
In Week 1 and 2, you will have a solid understanding of Python programming and be ready to dive into pandas, the library for data manipulation in data science. Make sure to complete the exercises and assignments to gain practical experience and enhance your skills.
We encourage you to actively participate, ask questions, and share your progress. Let's embark on this exciting journey together!
Week 2 delves into Python fundamentals, covering topics like introductory Python, packages, and Pandas basics. The included exercises and assignments will solidify your understanding and prepare you for more advanced data science concepts. Feel free to ask questions and engage with the community.
Week 3 introduces key statistical and probability concepts essential for data science. Dive into ANOVA, ChiSquare, and probability distributions. Practice and apply your knowledge using the provided resources. Don't hesitate to reach out for clarification or discussions.
Week 4 focuses on data preprocessing techniques, including data generation and manipulation. Work through the provided notebooks and datasets to reinforce your understanding. If you have questions or need assistance, feel free to ask.
Week 5 delves into data preprocessing with exploratory data analysis (EDA). Explore the provided notebooks and datasets related to HR Analytics, Loan Default, and Used Cars. Apply your knowledge and share your findings with the community.
Week 6 focuses on linear regression, covering topics like the application of derivatives, assumptions, gradient descent, and practical implementation. The provided datasets and notebooks, such as the Advertising dataset and Practical Implementation, will help you gain hands-on experience.
In Week 7, explore polynomial regression through the provided notebook and datasets. Gain insights into how polynomial features can enhance your regression models. Feel free to experiment and share your observations with the community.
Week 8 explores regularization techniques in polynomial regression. Dive into the provided materials and datasets to understand how regularization can improve the performance of your models. Experiment with different parameters and share your findings.
Stay tuned for the continuation of the Data Science Masterclass! If you have any questions or need assistance, don't hesitate to reach out to the community. Happy learning!
Week 9 introduces the concept of variance, bias, and pipeline in machine learning. Explore the provided notebooks and markdown files to understand the trade-offs and best practices. The pickle files include pre-trained models and encoders for your reference.
Week 10 covers the integration of machine learning models into user interfaces using Streamlit. Check out the provided notebook and files to learn how to create interactive and user-friendly applications. The preprocess_model.pkl and preprocessing.py are essential for the application's functionality.
Week 11 explores logistic regression, a fundamental technique for binary classification problems. Dive into the provided notes, notebook, and dataset to understand the principles and practical applications of logistic regression.
Week 12 covers two powerful classification algorithms - K-Nearest Neighbors (KNN) and Naive Bayes. Dive into the provided notebooks and materials to understand the principles behind these algorithms and their applications.
Week 13 introduces decision tree algorithms, covering both theory and practical implementation. Explore the provided materials and datasets to understand how decision trees work and how to implement them effectively.
Stay tuned for the continuation of the Data Science Masterclass! If you have any questions or need assistance, don't hesitate to reach out to the community. Happy learning!
Week 14 is dedicated to practical exercises and a hackathon project. Explore the provided notebook for the Cross Selling Hackathon, where various models have been implemented and results are provided. Additionally, there's a notebook demonstrating the use of SQL in conjunction with pandas for data analysis.
Week 15 focuses on the Random Forest algorithm. Dive into the provided lecture notes, PowerPoint presentation, and practical implementation notebook to understand the concepts and applications of Random Forest in machine learning.
Week 16 delves into ensemble learning techniques such as Boosting, Voting, and Stacking. Explore the provided notes, practical implementation notebook, and dataset to understand how these techniques can enhance model performance.
Week 17 introduces Dimensionality Reduction techniques, specifically Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). Dive into the provided notebook, lecture notes, and dataset to understand how these techniques can be applied to simplify data while retaining essential information.
Stay tuned for the continuation of the Data Science Masterclass! If you have any questions or need assistance, feel free to ask. Happy learning!
Week 18 explores Support Vector Machines (SVM), a powerful machine learning algorithm for classification and regression tasks. Dive into the provided notebook and lecture notes to understand the principles and practical implementation of SVM.
-
TimeSeries - Linear Regression with Seasonality_Additive.ipynb
-
TimeSeries - Linear Regression with Seasonality_Multiplicative.ipynb
Week 19 focuses on Time Series analysis. Dive into the provided notebooks and lecture notes to explore advanced time series models like ARIMA, smoothing methods, and linear regression with seasonality. The dataset and forecast results are also available for practical understanding.
Week 20 delves into AutoML (Automated Machine Learning), a process that automates the end-to-end process of applying machine learning to real-world problems. Explore the provided notebook and datasets to understand how AutoML can simplify the machine learning workflow.
Week 21 introduces the theory behind Neural Networks. Dive into the provided lecture notes and notebook to understand the basics of neural networks, with a focus on regression tasks.
Week 22 focuses on the practical application of Neural Networks. Explore the provided notebooks to understand classification tasks and hyperparameter tuning in neural networks. Additionally, there's an image file, noor.jpeg.
Stay tuned for the continuation of the Data Science Masterclass! If you have any questions or need assistance, feel free to ask. Happy learning!
Week 23 delves into Convolutional Neural Networks (CNN) theory and practical applications. Explore the provided notebooks and PDFs to understand the fundamentals of CNN and its implementation using TensorFlow. Additionally, there's a section dedicated to an Advanced CNN applied in the AnalyticsVidhya MNIST competition, along with associated datasets and results.
Week 24 explores the use of pre-trained CNN models. Dive into the provided notebook to understand how to work with pre-trained models, along with associated images for testing.
Week 25 introduces Transfer Learning, a machine learning technique where a model trained on one task is adapted for a second related task. Explore the provided notebooks and image datasets to understand transfer learning concepts, including data augmentation.
Week 26 covers Object Detection, focusing on the YOLO (You Only Look Once) algorithm. Explore the provided presentation, notebooks, and Python scripts to understand the basics of object detection using YOLO and the COCO dataset.
Week 27 explores MLOps, a set of practices that aims to unify machine learning system development and operations. Dive into the provided notebooks and datasets to understand how to use MLflow for managing the machine learning lifecycle.
Stay tuned for the continuation of the Data Science Masterclass! If you have any questions or need assistance, feel free to ask. Happy learning!
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent
