100 Days of Machine Learning Challenge

Welcome to the 100 Days of Machine Learning Challenge, a comprehensive journey into the world of machine learning, tailored for a diverse audience including aspiring data scientists, professionals in related fields, and enthusiasts.

Overview

This program is designed for individuals with high college-level algebra and basic Python knowledge. It offers a well-rounded educational experience through video lectures, comprehension questions, and hands-on tutorials.

Course Structure

Module 1: Introduction to Python and Basic Mathematics (Weeks 1-2)

Focus: Basic Python programming and foundational mathematics.
Topics: Python syntax, linear algebra, calculus, statistics.

Module 2: Data Preprocessing and Exploratory Data Analysis (Weeks 3-4)

Focus: Data preprocessing methods and exploratory data analysis.
Topics: Data preprocessing, visualization, descriptive statistics.

Module 3: Supervised Learning - Regression and Classification (Weeks 5-6)

Focus: Regression and classification algorithms.
Topics: Regression, classification, decision trees, SVM.

Module 4: Unsupervised Learning and Dimensionality Reduction (Weeks 7-9)

Focus: Unsupervised learning techniques and reducing data complexity.
Topics: Clustering, Gaussian Mixture Models, PCA, t-SNE.

Module 5: Deep Learning Foundations (Weeks 10-12)

Focus: Core concepts and architectures in deep learning.
Topics: Neural networks, CNNs, RNNs, image and sequence processing.

Module 6: Advanced Machine Learning and Current Trends (Weeks 13-14)

Focus: Advanced topics and emerging trends in machine learning.
Topics: Reinforcement learning, transfer learning, GANs, attention mechanisms.

Module 7: Practical Aspects of Machine Learning (Weeks 15-17)

Focus: Operationalizing machine learning models and understanding transformers.
Topics: MLOps, ETL processes, transformer models.

Module 8: Applied AI and Ethical Considerations (Weeks 18-19)

Focus: Application of AI in various industries and ethical considerations.
Topics: AI in healthcare, finance, retail, manufacturing, AI ethics.

Module 9: Capstone Project (Weeks 20-21)

Focus: Application of learned concepts in a comprehensive project.
Topics: Data analysis, model building, and evaluation.

Join Our Community

Connect with learners and experts in our community. Share your insights, participate in discussions, and collaborate on projects.

Start Date: January 1st, 2024.

Social Media and Contact

We are excited to embark on this journey of exploration and discovery in machine learning with you. Let's learn and grow together!

ajay-vishnu / 100daysofml.github.io Goto Github PK

100daysofml.github.io's Introduction

100 Days of Machine Learning Challenge

Overview

Course Structure

Module 1: Introduction to Python and Basic Mathematics (Weeks 1-2)

Module 2: Data Preprocessing and Exploratory Data Analysis (Weeks 3-4)

Module 3: Supervised Learning - Regression and Classification (Weeks 5-6)

Module 4: Unsupervised Learning and Dimensionality Reduction (Weeks 7-9)

Module 5: Deep Learning Foundations (Weeks 10-12)

Module 6: Advanced Machine Learning and Current Trends (Weeks 13-14)

Module 7: Practical Aspects of Machine Learning (Weeks 15-17)

Module 8: Applied AI and Ethical Considerations (Weeks 18-19)

Module 9: Capstone Project (Weeks 20-21)

Join Our Community

Social Media and Contact

100daysofml.github.io's People

Contributors

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

Recommend Org

Facebook

Microsoft

Google

Alibaba

D3

Tencent