Rustam-Z 🚀 | Date: 19.08.2020

https://coursera.org/share/cd414086f69f439c2e9f19d2b23403cc

• Week ##1
  • What is Machine Learning?
  • Supervised/Unsupervised learning
  • Linear Regression with one variable
  • Cost Function
  • Gradient Descent
  • Gradient Descent For Linear Regression
  • Linear Algebra (Matrix & Vector)
• Week #2
  • Linear Regression with Multiple Variables
  • Multiple Features
  • Gradient Descent For Multiple Variables
  • Polynomial Regression
  • Octave Turorial
• Week #3
  • Logistic Regression (Classification problem)
  • Hypothesis Representation
  • Cost Function
  • Advanced Optimization
  • Multiclass Classification: One-vs-all
  • Regularization (The Problem of Overfitting)
    • Cost Function
    • Regularized Linear Regression
    • Regularized Logistic Regression
• Week #4
  • Neural Networks: Representation
  • Model Representation for Neural Networks
  • Multiclass Classification
• Week #5
  • Neural Networks Learning
  • Cost Function
  • Backpropagation Algorithm
  • Gradient Checking
  • Random Initialization
• Week #6
  • Advice for Applying Machine Learning
    • Evaluating a Hypothesis
    • Model Selection and Train/Validation/Test Sets
    • Bias vs. Variance
    • Regularization and Bias/Variance
  • Machine Learning System Design
    • Prioritizing What to Work On
    • Error Analysis
    • Error Metrics for Skewed Classes
    • Data For Machine Learning
• Week #7
  • Support Vector Machines (SVM), is a machine learning algorithm for classification.
  • Large margin intuition
  • Kernels I & II
  • Using An SVM
• Week #8
  • Unsurepvised Learning: Clustering
  • K-Means Algorithm (groupings of unlabeled data points)
  • Dimensionality Reduction - Principal Component Analysis
• Week #9
  • Anomaly Detection
  • Gaussian distribution
  • Recommender Systems
    • Collaborative Filtering
    • Low Rank Matrix Factorization
    • Mean Normalization
• Week #10
  • Large Scale Machine Learning
  • Stochastic Gradient Descent
  • Mini-Batch Gradient Descent
  • Online Learning
  • Map Reduce and Data Parallelism
• Week #11
  • Application Examples: Photo OCR
  • Problem Description and Pipeline
  • Getting Lots of Data and Artificial Data
  • What Part of the Pipeline to Work on Next

• How to start learning ML
• Top Machine Learning Courses
• Book: Machine Learning For Absolute Beginners (2018) | Author: Oliver Theobald
• Book: The Elements Of Statistical Learning: Data Mining, Inference and Prediction