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 scientiAsts, 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.
Social Media and Contact#