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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!