42 steps of machine learning

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. 7 Steps to Mastering SQL for Data Science

2.1. Step : Relational Database Basics

2.2. Step : SQL Overview

2.3. Step : Selecting, Inserting, Updating

2.4. Step : Creating, Dropping, Deleting

2.5. Step : Views and Joins

2.6. Step : SQL for Data Science

2.7. Step : SQL Integration with Python, R

3. 7 Steps to Understanding NoSQL Databases

3.1. Step : Why NoSQL?

3.2. Step : NoSQL Basics

3.3. Step : Understanding Key-value Stores

3.4. Step : Understanding Document Stores

3.5. Step : Understanding Column-oriented Databases

3.6. Step : Understanding Graph Databases

3.7. Step : Bringing it All Together

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

4.3. Step : Dealing with Missing Values

4.4. Step : Dealing with Outliers

4.5. Step : Dealing with Imbalanced Data

4.6. Step : Data Transformations

4.7. Step : Finishing Touches & Moving Ahead

5. 14 Steps to Mastering Machine Learning With Python

5.1. Step : Basic Python Skills

5.2. Step : Foundational Machine Learning Skills

5.3. Step : Scientific Python Packages Overview

5.4. Step : Getting Started with Machine Learning in Python

5.5. Step : Machine Learning Topics with Python

5.6. Step : Advanced Machine Learning Topics with Python

5.7. Step : Deep Learning in Python

5.8. Step : Machine Learning Basics Review & A Fresh Perspective

5.9. Step : More Classification

5.10. Step : More Clustering

5.11. 11 Step : More Ensemble Methods

5.12. 12 Step : Gradient Boosting

5.13. 13 Step : More Dimensionality Reduction

5.14. 14 Step : More Deep Learning