My-Ai-Journey

In this respository, i'll be documenting my journey to learning Ai. Starting from Data Analysis to Deep Learning, i'll be posting all the projects i've worked on and the topics i've learned throughout the upcoming weeks.

So whish me Luck :))

I'll be studying from Andre Ng Machine Learning Specialization for theoritcal learning, as well as Alexey Gregorev's ML zoomcamp for a more practical approach. As for a book reference, my choice is Introduction to Machine Learning with Python by Andreas C. Müller, Sarah Guido

I'll be adding more topics to the tables and updating this description as i go along

Pre-Machine Learning

Topic to Learn ( Machine Learning )	Progress
Python Programming	✔️
SQL	✔️
Python Database Connectivity	---
Web Scarping	✔️
Mathematics for Machine Learning	✔️
Descriptive Statistics	✔️
Probablity	---
Inferential Statistics	---
Numpy	✔️
Pandas	✔️
Matplotlip	---

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Topics i'd learned before creating this repository:

• Python Programming & OOP
• Software Engineering Solid Principles and Design Patterns
• Object Oriented Design
• Web Scraping
  • Top 250 Rated Movies Project uses BeautifulSoup4 and Requests libraries to scrape the IMDB website, to store the names, ranks, ratings, and release dates of all the Top 250 rated movies
  • Real Estate Project uses BeautifulSoup4 and Requests libraries to scrape Pararius website. I used the CSV library to store the names, locations, prices, and surface areas of each apartment in a csv file
• Database Design
• Sql ( Hackerrank Badge )

- Week 1:

1. Descriptive Statistics
2. Numpy

1. Descriptive Statistics

For this week, i decided to revise some descriptive statistics since i was a little rusty. I went over the following topics:

• Measures of central Tendency ( Mean, Median, Mode, Weighted mean )
• Measures of dispersion ( Variance, Standard Deviation, Range, Interquartile ranges, coefficient of variation )
• Central Limit Theorm

Udacity's Introduction to Descriptive Statistics is a great source for either revising or learning descriptive statistics from scratch

2. Numpy

I got my hands dirty on Numpy, which was actually my first time using it. I got introduced to how powerful and useful Numpy can be with all it's arrays and various functions. So, because i wanted to become familiarized with it's capabilities and build up my skills, i spent this week practising

For excercising, i found this extremely helpful 101 Numpy Excercises Website.

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- Week 2:

• Probability
• Pandas

1. Probabilty

For this week, i revised on some of the topics in Probabilty

• An intro to Probability (Types of Probability, Sample spaces, Bayes' Theorm)
• Random Variables (Discrete and Continous random variables)
• Discrete Probabilty Distribution (Binomial, Bernoulli, Geometric,Hypergeometric, and Poisson distributions)

2. Pandas

As my first time hitting Pandas, it took me the whole week learning and exercising. Fortunately, i got to learn all the basics and gained the nencessary knowledge for Machine Learning

For excercising, i found this extremely helpful 101 Pandas Exercises Website.