Machine Learning by Stanford University🚀

My path to learning ML | Rustam_Z🚀 | DAY-1: 19.08.2020

Coursera Certificate

Hello, I am writing this message after a long time. I am studying Deep Learning right now. Yes, of course, learning machine learning isn't easy. A great piece of advice I can give you is to focus on one thing, be patient, and learn with enthusiasm. Focus on just one thing! Work hard, work smart!

Syllabus

• 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

Resources

• https://www.coursera.org/learn/machine-learning/resources/zVvo7

• How to start learning ML

• Top Machine Learning Courses

• Machine Learning For Absolute Beginners (2018) | Author: Oliver Theobald

• The Elements Of Statistical Learning : Data Mining, Inference and Prediction

• Recommended ML books by ProgLib

• https://telegra.ph/What-REALLY-is-Data-Science-09-21