Machine Learning

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🔖Computer Science Fundamentals from MIT We found the textbook Mathematics for Computer Science – covering the mathematics that underlies algorithms and computer science. Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures – all in one place. ⛓️ Link to the textbook https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/mit6_042js15_textbook.pdf

Try it, it's free, your AI assistant

📋 3 Main Steps for a Proper EDA in Python 🔑 Exploratory Data Analysis Cheat Sheet

From raw data to real insight EXPLORE. UNDERSTAND. TURN DATA INTO INSIGHTS.

Good analysis starts with good questions. 🟦 STEP 1 — Data Collection & Structuring Goal: Get the data in, shape it, and understand its skeleton. 1.1 Import Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Optional display settings
pd.set_option('display.max_columns', None)
sns.set_style('whitegrid')

1.2 Load the Data File

df = pd.read_csv('data.csv')
# df = pd.read_excel('data.xlsx')
# df = pd.read_sql(query, connection)

df.head()       # first rows
df.info()       # schema, types, nulls
df.shape        # (rows, cols)
df.describe()   # quick stats

🟧 STEP 2 — Issue Identification Goal: Spot problems — missing values, duplicates, wrong types, outliers. 2.1 Missing Values

df.isna().sum()                    # count per column
df.isna().mean() * 100             # % missing

# Fill
df['age'] = df['age'].fillna(df['age'].median())
df['city'] = df['city'].fillna('Unknown')

# Drop
df = df.dropna(subset=['critical_col'])

2.2 Duplicates

df.duplicated().sum()
df = df.drop_duplicates()

2.3 Data Types & Conversions

df.dtypes
df['date'] = pd.to_datetime(df['date'])
df['id']   = df['id'].astype(int)
df['name'] = df['name'].str.strip().str.lower()

2.4 Outliers (IQR method)

Q1 = df['col'].quantile(0.25)
Q3 = df['col'].quantile(0.75)
IQR = Q3 - Q1
outliers = df[(df['col'] < Q1 - 1.5*IQR) | (df['col'] > Q3 + 1.5*IQR)]

sns.boxplot(x=df['col'])

🟩 STEP 3 — Understand & Decide Goal: Reveal patterns and turn data into decisions. 3.1 Descriptive Statistics

df.describe()                    # numeric
df['category'].value_counts()    # categorical

df['category'].value_counts().plot(kind='bar')

3.2 Data Visualization

# Histogram — distribution
df['age'].hist(bins=30)

# Countplot — categories
sns.countplot(x='category', data=df)

# Scatter — relationships
plt.scatter(df['x'], df['y'])

# Heatmap — correlations
sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')

3.3 Ask the Right Questions ✅ What's the distribution of each column? ✅ Are there correlations between variables? ✅ Do groups behave differently? ✅ Does the data match business reality? ✅ What story does the data tell? 🟢 Key Notes

💡 Context matters — numbers without meaning mislead 🧹 Quality over quantity — clean data beats big data 🧠 Understand before you predict — EDA is the foundation of every successful data project

🎯 The 3-Step Formula Step Focus Key Action 1 Collect & Structure Load + shape the data 2 Identify Issues Missing, duplicates, outliers, types 3 Understand & Decide Stats + viz + right questions

EXPLORE. UNDERSTAND. TURN DATA INTO INSIGHTS. Better data → Better decisions*

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"Linear Algebra with Applications" is a free and comprehensive textbook that introduces the computational, theoretical, and applied aspects of linear algebra. The book covers topics such as systems of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalization, inner product spaces, orthogonality, and many more. The explanations are accompanied by over 330 worked examples, exercises, and practical applications in geometry, electrical networks, dynamic systems, probability theory, and optimization. A particularly interesting section discusses how Google's PageRank algorithm uses the dominant eigenvector to rank web pages. The links between websites are represented as a connectivity matrix, and the components of its dominant eigenvector provide an estimate of the relative importance of each page. This is a very clear example of how an apparently abstract idea from linear algebra can underlie a real-world technology used on a massive scale. The 2023 edition is available under a Creative Commons license. This is another excellent resource that is worth keeping as a reference. https://collection.bccampus.ca/textbook/qTj4b4Ey

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