Machine Learning

🔖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

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📋 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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🚨 Cambridge has just released a real bombshell this time. 📚 A whole collection of classic textbooks on AI and machine learning is now available for free in PDF format. If you want to really understand machine learning and don't want to waste money on overpriced courses, these ten books will be enough to build a very solid foundation. From simple to complex. 1️⃣ Understanding Machine Learning One of the best books for beginners. It covers the basic theoretical algorithms of machine learning. 🔗 https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf 2️⃣ Mathematical Foundations of Machine Learning If you're not very confident in your math skills, I would start here. 🔗 https://mml-book.github.io/book/mml-book.pdf 3️⃣ Mathematical Analysis of Machine Learning Algorithms A more in-depth look at the mathematical principles of machine learning algorithms. 🔗 https://tongzhang-ml.org/lt-book/lt-book.pdf 4️⃣ Theoretical Principles of Deep Learning The theoretical foundations of deep learning and an understanding of why it all works. 🔗 https://arxiv.org/pdf/2106.10165 5️⃣ Neural Networks and Learning Machines A systematic analysis of neural networks and the principles of their training. 🔗 https://arxiv.org/pdf/1901.05639 6️⃣ Graph Deep Learning A good starting point for those who want to understand graph neural networks. 🔗 https://yaoma24.github.io/dlg_book/dlg_book.pdf 7️⃣ Machine Learning: A Probabilistic Perspective It allows you to look at machine learning from a probabilistic and algorithmic perspective. 🔗 https://people.csail.mit.edu/moitra/docs/bookexv2.pdf 8️⃣ Probability Theory: Theory and Examples Fundamental theory of probability. Very useful if you want to understand machine learning beyond the level of using ready-made libraries. 🔗 https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf 9️⃣ Fundamentals of Applied Probability More focus on the practical application of probability theory. 🔗 https://sites.math.duke.edu/~rtd/EP4A/EP4A_April2021.pdf 🔟 Advanced Data Analysis An advanced level for those who want to seriously improve their data analysis skills. 🔗 https://stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf #AI #MachineLearning #FreeBooks #DataScience #DeepLearning #Tech ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A