42 steps of machine learning
Plan your projects and define important tasks and actions
42 steps of machine learning
von saurabh kamble
1. 7 Steps to Understanding Deep Learning
1.1. Step: Introducing Deep Learning
1.2. Step : Getting Technical
1.3. Step : Backpropagation and Gradient Descent
1.4. Step : Getting Practical
1.5. Step : Convolutional Neural Nets and Computer Vision
1.6. Step : Recurrent Nets and Language Processing
1.7. Step : Further Topics
2. 14 Steps to Mastering Machine Learning With Python
2.1. Step : Basic Python Skills
2.2. Step : Foundational Machine Learning Skills
2.3. Step : Scientific Python Packages Overview
2.4. Step : Getting Started with Machine Learning in Python
2.5. Step : Machine Learning Topics with Python
2.6. Step : Advanced Machine Learning Topics with Python
2.7. Step : Deep Learning in Python
2.8. Step : Machine Learning Basics Review & A Fresh Perspective
2.9. Step : More Classification
2.10. Step : More Clustering
2.11. 11 Step : More Ensemble Methods
2.12. 12 Step : Gradient Boosting
2.13. 13 Step : More Dimensionality Reduction
2.14. 14 Step : More Deep Learning
3. 7 Steps to Mastering SQL for Data Science
3.1. Step : Relational Database Basics
3.2. Step : SQL Overview
3.3. Step : Selecting, Inserting, Updating
3.4. Step : Creating, Dropping, Deleting
3.5. Step : Views and Joins
3.6. Step : SQL for Data Science
3.7. Step : SQL Integration with Python, R
4. 7 Steps to Mastering Data Preparation with Python
4.1. Step : Preparing for the Preparation
4.1.1. Steps for Data preparation :
4.1.2. 1) Data wrangling: Convert raw data to meaning full data with following tools. 2) Python library for pandas.
4.1.3. 3) converting the data into "Data sink"
4.2. Step : Exploratory Data Analysis
4.2.1. Its a approach to analysis Data sets