daddyawesome/freecodacamp

Things I learn From FreeCodeCamp

FreeCodeCamp

This should be my Repo for my studies using FreeCodeCamp

My RoadMap

1. JavaScript Algorithms and Data Structures Certification (300 hours)
2. Learn Python Programming from Scratch*

Python notes for beginners

• Introduction
• Syntax
• Statement, Indentation, and Comments
• Variables and Datatypes
• Operators
• Numbers
• Strings
• Data structure
• List
• Tuples

Python notes for intermediates

• Module
• Classes, objects and Packages
• Methods
• Iterators
• Decorators
• Generators
• Pandas and Numpy
• Web Scrapping
• API's
• SQL
• Cloud Data Storage (Google BigQuery)
• Data Visualization (Matplotlib, Seaborn, plotly, bokeh for flask)
• Data Preparation
• Statistics

  • Descriptive/Summary statistics How to summarise a sample of data Different types of distributions Skewness, kurtosis, central tendency (e.g. mean, median, mode) Measures of dependence, and relationships between variables such as correlation and covariance

  • Experiment design Hypothesis testing Sampling Significance tests Randomness Probability Confidence intervals and two-sample inference

  • Machine learning Inference about slope Linear and non linear regression Classification

  • Statsmodels

    Resources: I am a big fan of the statistics course on Khan academy for learning the basics. The SciPy lecture notes are another great resource to learn these concepts in Python. I also highly recommend reading the book Think Stats — available for free online.

• Calculus

  • Derivatives Geometric definition Calculating the derivative of a function Nonlinear functions

  • Chain rule Composite functions Composite function derivatives Multiple functions

  • Gradients Partial derivatives Directional derivatives Integrals

    Resources: One of the best resources I have come across to learn these principles is this machine learning cheatsheet which also covers linear algebra, regression, and the maths behind neural networks. I also really love this blog post that provides a gentle introduction to calculus with practical examples.

• Linear Algebra
  • Vectors and spaces Vectors Linear combinations Linear dependence and independence Vector dot and cross products
  • Matrix transformations Functions and linear transformations Matrix multiplication Inverse functions Transpose of a matrix Resources: This blog post by Ritchie Ng covers matrices and vectors really well. If you want a more in depth overview of the field this is a good free book that gives an extensive coverage of linear algebra.

Python notes for advanced learners

• Web framework
• TensorFlow
• Scikit Learn
• Keras
• Machine Learning
• Deep learning
• Artificial Intelligence
• Relational Database

Rule 1: never pick up a half done website. Rule 2: never take a job where they want to "do things themselves". And don't work with fixed prices if you calculate your price on a best case scenario. Rule 3: don't do content manangement Rule 4: don't promise a sales target on their website. Rule 5: start sending invoices every money and not just at the end of the project. Rule 5: Put every website on your own webserver and don't release it untill it's paid. Rule 6: Don't work for free. Rule 7: Don't work for free.