Inspired by Machine Learning for Software Engineers & How to learn Deep Learning in 6 months

This is my multi-month study plan to become machine learning engineer.

I have a Software Engineering degree, not a Computer Science degree. I have an itty-bitty amount of basic knowledge about: Calculus, Linear Algebra, Discrete Mathematics, Probability & Statistics from university.

"We learn driving a car — by driving. Not by learning how the clutch and the internal combustion engine work." When learning machine learning(AI), I'm following the same top-down approach.

I take one subject from the list below, read and google it, run code and take notes. I will forget most of the learnt material, so I make flashcards. I use Anki to review on my phone, wherever I am.

※　Some subjects in Japanese

• Motivation
• Python
  • Numpy
• Machine Learning Overview
• Machine Learning
  • Machine Learning Mastery With Python - Jason Brownlee
  • Regression
  • Classification
  • Visualization
  • Preprocessing
  • Improve Results
  • Example ML
• Deep Learning
  • Deep Learning with Python - Jason Brownlee
  • Long Short Term Memory - Jason Brownlee
  • Recurrent Neural Network
  • Convolutional_Neural_Network
  • Others
  • Practical Deep Learning for Coders
  • TensorFlow
  • End-to-End Machine Learning with TensorFlow on GCP

• If I just simply don’t quit, I can’t not fail

• データマイニング・機械学習分野の概要 - 神嶌 敏弘

• Simple Linear Regression

• Logistic Regression
• k-Nearest Neighbors

• A Conceptual Framework for Data Visualization
• Matplotlib basic
• Density and Contour Plots
• PythonDataAnalyticsAndVisualization_Chapter4.Data Visualization
• Matplotlib Scatter plot

• About Feature Scaling and Normalization
• Data Preprocessing
• SCALING, STANDARDIZING, NORMALIZING
• Standardize or Normalize _Examples in Python
• Feature selection for supervised models using SelectKBest

• Improve Your Model Performance Using Cross Validation

• Guide to training and deploying machine learning models using Python
• Linear Regression on Boston Housing Dataset

• RNN Intiuition
• Build RNN Stock Market
• RNN：時系列データを扱うRecurrent Neural Networksとは

• 定番のConvolutional Neural Networkをゼロから理解する

• Understanding LSTM Networks

• Understanding Learning Rates and How It Improves Performance in Deep Learning
• Gradient Descent: All You Need to Know
• Gradient Descent in a Nutshell

I learned the foundations of Deep Learning:Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. I worked on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. I practice all these ideas in Python and in TensorFlow.

I will learn Machine Learning Concepts, Knime, Apache Spark