100-Days-Of-ML-Code

Introduction to 100-Days-Of-ML-Code

The 100-Days-Of-ML-Code project is a comprehensive journey designed for anyone who wants to delve into machine learning. It is inspired by the work of Avik Jain and provides a structured layout that aims to take participants from basic to advanced concepts in machine learning over the course of 100 days. The project offers a mix of theoretical insights, practical implementations, online courses, and much more, helping participants build a solid foundation in machine learning.

Supervised Learning

The journey begins with supervised learning, which forms the backbone of many machine learning algorithms. Throughout this section, participants work with labeled data to predict outcomes, refine predictions, and understand various algorithms used in classification and regression tasks.

1. Data Preprocessing (Day 1): The initial step in machine learning involves transforming raw data into an understandable format, addressing missing values, encoding categorical variables, and more.

2. Simple Linear Regression (Day 2): Engages with predicting a response using a straight line that best fits the data points.

3. Multiple Linear Regression (Day 3): Extends the linear regression model to accommodate multiple variables, enhancing prediction reliability.

4. Logistic Regression (Day 4-6): Participants dive deep into logistic regression, learning about its mathematical foundation, implementation, and application in binary classification problems.

5. K-Nearest Neighbors (k-NN) (Day 7): This algorithm classifies data points based on their proximity to already classified points.

6. Support Vector Machine (SVM) (Day 9-14): A powerful algorithm used for classification that focuses on finding the best boundary that segregates different classes.

7. Decision Trees and Random Forests (Day 23-34): Decision trees model decisions based on conditions associated with data, and random forests enhance prediction reliability by using multiple decision trees.

Unsupervised Learning

Moving to unsupervised learning, this project helps participants discover hidden patterns in data without predefined labels.

1. K-Means Clustering (Day 43-44): A popular clustering technique where data is grouped into clusters based on feature similarity.

2. Hierarchical Clustering (Day 54): Unveils methods to build nested clusters for the data, represented in a dendrogram format.

Practical Skills and Knowledge Expansion

The project not only covers core machine learning algorithms but also extends into various supporting skills necessary for a holistic understanding of data science and machine learning:

1. Deep Learning and Neural Networks (Day 35-42): Explore the fundamentals of neural networks, including backpropagation and training of complex architectures with TensorFlow and Keras.

2. Mathematics for Machine Learning (Day 26-32): Revisits essential mathematical concepts like linear algebra and calculus, using engaging tutorials and visualizations.

3. Data Preparation and Analysis with Pandas and NumPy (Day 45-48): Instruction on using these powerful libraries for efficient data manipulation, aggregation, and visualization.

4. Data Collection Techniques (Day 21): Introduction to web scraping techniques using Beautiful Soup to gather data required for various machine learning applications.

Conclusion

The 100-Days-Of-ML-Code is meticulously orchestrated to equip learners with practical skills and robust understanding across the spectrum of machine learning. With a meticulously documented learning path, including code implementations on GitHub, educational videos, and linked resources, the project not only fosters learning but also nurtures a community of enthusiastic learners paving their path in the thrilling domain of machine learning.