Looking for a clear and concise introduction to machine learning? This book by Laurent Younes provides a solid foundation in ML concepts, from theory to practical applications.
Perfect for students, researchers, and enthusiasts aiming to build a strong understanding of the core principles behind modern machine learning.
#MachineLearning #ML #AI #DeepLearning #DataScience #Python #MathForML #Books #LearningResources #MLBooks #OpenSourceKnowledge
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Supervised Learning: Classification and Regression
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
#SupervisedLearning #MachineLearning #Classification #Regression #MLNotes #DataScience #AIResources #MLTheory #MLLectures #LearnML
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👨🏻💻 Real learning means implementing ideas and building prototypes. It's time to skip the repetitive training and get straight to real data science projects!
┌
└
#DataScience #PythonProjects #MachineLearning #DeepLearning #AIProjects #RealWorldData #OpenSource #DataAnalysis #ProjectBasedLearning #LearnByBuilding
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𝗬𝗼𝘂𝗿_𝗗𝗮𝘁𝗮_𝗦𝗰𝗶𝗲𝗻𝗰𝗲_𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄_𝗦𝘁𝘂𝗱𝘆_𝗣𝗹𝗮𝗻.pdf
7.7 MB
1. Master the fundamentals of Statistics
Understand probability, distributions, and hypothesis testing
Differentiate between descriptive vs inferential statistics
Learn various sampling techniques
2. Get hands-on with Python & SQL
Work with data structures, pandas, numpy, and matplotlib
Practice writing optimized SQL queries
Master joins, filters, groupings, and window functions
3. Build real-world projects
Construct end-to-end data pipelines
Develop predictive models with machine learning
Create business-focused dashboards
4. Practice case study interviews
Learn to break down ambiguous business problems
Ask clarifying questions to gather requirements
Think aloud and structure your answers logically
5. Mock interviews with feedback
Use platforms like Pramp or connect with peers
Record and review your answers for improvement
Gather feedback on your explanation and presence
6. Revise machine learning concepts
Understand supervised vs unsupervised learning
Grasp overfitting, underfitting, and bias-variance tradeoff
Know how to evaluate models (precision, recall, F1-score, AUC, etc.)
7. Brush up on system design (if applicable)
Learn how to design scalable data pipelines
Compare real-time vs batch processing
Familiarize with tools: Apache Spark, Kafka, Airflow
8. Strengthen storytelling with data
Apply the STAR method in behavioral questions
Simplify complex technical topics
Emphasize business impact and insight-driven decisions
9. Customize your resume and portfolio
Tailor your resume for each job role
Include links to projects or GitHub profiles
Match your skills to job descriptions
10. Stay consistent and track progress
Set clear weekly goals
Monitor covered topics and completed tasks
Reflect regularly and adapt your plan as needed
#DataScience #InterviewPrep #MLInterviews #DataEngineering #SQL #Python #Statistics #MachineLearning #DataStorytelling #SystemDesign #CareerGrowth #DataScienceRoadmap #PortfolioBuilding #MockInterviews #JobHuntingTips
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Over the last year, several articles have been written to help candidates prepare for data science technical interviews. These resources cover a wide range of topics including machine learning, SQL, programming, statistics, and probability.
1️⃣ Machine Learning (ML) Interview
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
2️⃣ SQL Interview Preparation
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
3️⃣ Programming Questions
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
4️⃣ Statistics
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
5️⃣ Probability
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
🔜 All links are available in the GitHub repository:
https://lnkd.in/djcgcKRT
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
https://lnkd.in/djcgcKRT
#DataScience #InterviewPrep #MachineLearning #SQL #Python #Statistics #Probability #CodingInterview #AIBootcamp #DeepLearning #LLMs #ComputerVision #GitHubResources #CareerInDataScience
✉️ Our Telegram channels: https://yangx.top/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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If you are doing regression modeling in Python for explanatory purposes, don't use scikit-learn - it's not set up for explanatory modeling. Use #statsmodels. It's set up much better for immediately showing you all the underlying parameters of your model and helping you interpret your results..
#analytics #peopleanalytics #datascience #rstats #python
#analytics #peopleanalytics #datascience #rstats #python
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#DataScience #SQL #Python #MachineLearning #Statistics #BusinessAnalytics #ProductCaseStudies #DataScienceProjects #InterviewPrep #LearnDataScience #YouTubeLearning #CodingInterview #MLInterview #SQLProjects #PythonForDataScience
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Topic: Handling Datasets of All Types – Part 1 of 5: Introduction and Basic Concepts
---
1. What is a Dataset?
• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.
---
2. Types of Datasets
• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
• Unstructured Data: Images, text, audio, video.
• Semi-structured Data: JSON, XML files containing hierarchical data.
---
3. Common Dataset Formats
• CSV (Comma-Separated Values)
• Excel (.xls, .xlsx)
• JSON (JavaScript Object Notation)
• XML (eXtensible Markup Language)
• Images (JPEG, PNG, TIFF)
• Audio (WAV, MP3)
---
4. Loading Datasets in Python
• Use libraries like
• Use libraries like
---
5. Basic Dataset Exploration
• Check shape and size:
• Preview data:
• Check for missing values:
---
6. Summary
• Understanding dataset types is crucial before processing.
• Loading and exploring datasets helps identify cleaning and preprocessing needs.
---
Exercise
• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
---
#DataScience #Datasets #DataLoading #Python #DataExploration
https://yangx.top/DataScienceM
---
1. What is a Dataset?
• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.
---
2. Types of Datasets
• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
• Unstructured Data: Images, text, audio, video.
• Semi-structured Data: JSON, XML files containing hierarchical data.
---
3. Common Dataset Formats
• CSV (Comma-Separated Values)
• Excel (.xls, .xlsx)
• JSON (JavaScript Object Notation)
• XML (eXtensible Markup Language)
• Images (JPEG, PNG, TIFF)
• Audio (WAV, MP3)
---
4. Loading Datasets in Python
• Use libraries like
pandas for structured data:import pandas as pd
df = pd.read_csv('data.csv')
• Use libraries like
json for JSON files:import json
with open('data.json') as f:
data = json.load(f)
---
5. Basic Dataset Exploration
• Check shape and size:
print(df.shape)
• Preview data:
print(df.head())
• Check for missing values:
print(df.isnull().sum())
---
6. Summary
• Understanding dataset types is crucial before processing.
• Loading and exploring datasets helps identify cleaning and preprocessing needs.
---
Exercise
• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
---
#DataScience #Datasets #DataLoading #Python #DataExploration
https://yangx.top/DataScienceM
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Topic: Handling Datasets of All Types – Part 2 of 5: Data Cleaning and Preprocessing
---
1. Importance of Data Cleaning
• Real-world data is often noisy, incomplete, or inconsistent.
• Cleaning improves data quality and model performance.
---
2. Handling Missing Data
• Detect missing values using
• Strategies to handle missing data:
* Remove rows or columns with missing values:
* Impute missing values with mean, median, or mode:
---
3. Handling Outliers
• Outliers can skew analysis and model results.
• Detect outliers using:
* Boxplots
* Z-score method
* IQR (Interquartile Range)
• Handle by removal or transformation.
---
4. Data Normalization and Scaling
• Many ML models require features to be on a similar scale.
• Common techniques:
* Min-Max Scaling (scales values between 0 and 1)
* Standardization (mean = 0, std = 1)
---
5. Encoding Categorical Variables
• Convert categorical data into numerical:
* Label Encoding: Assigns an integer to each category.
* One-Hot Encoding: Creates binary columns for each category.
---
6. Summary
• Data cleaning is essential for reliable modeling.
• Handling missing values, outliers, scaling, and encoding are key preprocessing steps.
---
Exercise
• Load a dataset, identify missing values, and apply mean imputation.
• Detect outliers using IQR and remove them.
• Normalize numeric features using standardization.
---
#DataCleaning #DataPreprocessing #MachineLearning #Python #DataScience
https://yangx.top/DataScienceM
---
1. Importance of Data Cleaning
• Real-world data is often noisy, incomplete, or inconsistent.
• Cleaning improves data quality and model performance.
---
2. Handling Missing Data
• Detect missing values using
isnull() or isna() in pandas.• Strategies to handle missing data:
* Remove rows or columns with missing values:
df.dropna(inplace=True)
* Impute missing values with mean, median, or mode:
df['column'].fillna(df['column'].mean(), inplace=True)
---
3. Handling Outliers
• Outliers can skew analysis and model results.
• Detect outliers using:
* Boxplots
* Z-score method
* IQR (Interquartile Range)
• Handle by removal or transformation.
---
4. Data Normalization and Scaling
• Many ML models require features to be on a similar scale.
• Common techniques:
* Min-Max Scaling (scales values between 0 and 1)
* Standardization (mean = 0, std = 1)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df[['feature1', 'feature2']])
---
5. Encoding Categorical Variables
• Convert categorical data into numerical:
* Label Encoding: Assigns an integer to each category.
* One-Hot Encoding: Creates binary columns for each category.
pd.get_dummies(df['category_column'])
---
6. Summary
• Data cleaning is essential for reliable modeling.
• Handling missing values, outliers, scaling, and encoding are key preprocessing steps.
---
Exercise
• Load a dataset, identify missing values, and apply mean imputation.
• Detect outliers using IQR and remove them.
• Normalize numeric features using standardization.
---
#DataCleaning #DataPreprocessing #MachineLearning #Python #DataScience
https://yangx.top/DataScienceM
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Topic: Handling Datasets of All Types – Part 2 of 5: Data Cleaning and Preprocessing
---
1. Importance of Data Cleaning
• Real-world data is often noisy, incomplete, or inconsistent.
• Cleaning improves data quality and model performance.
---
2. Handling Missing Data
• Detect missing values using
• Strategies to handle missing data:
* Remove rows or columns with missing values:
* Impute missing values with mean, median, or mode:
---
3. Handling Outliers
• Outliers can skew analysis and model results.
• Detect outliers using:
* Boxplots
* Z-score method
* IQR (Interquartile Range)
• Handle by removal or transformation.
---
4. Data Normalization and Scaling
• Many ML models require features to be on a similar scale.
• Common techniques:
* Min-Max Scaling (scales values between 0 and 1)
* Standardization (mean = 0, std = 1)
---
5. Encoding Categorical Variables
• Convert categorical data into numerical:
* Label Encoding: Assigns an integer to each category.
* One-Hot Encoding: Creates binary columns for each category.
---
6. Summary
• Data cleaning is essential for reliable modeling.
• Handling missing values, outliers, scaling, and encoding are key preprocessing steps.
---
Exercise
• Load a dataset, identify missing values, and apply mean imputation.
• Detect outliers using IQR and remove them.
• Normalize numeric features using standardization.
---
#DataCleaning #DataPreprocessing #MachineLearning #Python #DataScience
https://yangx.top/DataScience4M
---
1. Importance of Data Cleaning
• Real-world data is often noisy, incomplete, or inconsistent.
• Cleaning improves data quality and model performance.
---
2. Handling Missing Data
• Detect missing values using
isnull() or isna() in pandas.• Strategies to handle missing data:
* Remove rows or columns with missing values:
df.dropna(inplace=True)
* Impute missing values with mean, median, or mode:
df['column'].fillna(df['column'].mean(), inplace=True)
---
3. Handling Outliers
• Outliers can skew analysis and model results.
• Detect outliers using:
* Boxplots
* Z-score method
* IQR (Interquartile Range)
• Handle by removal or transformation.
---
4. Data Normalization and Scaling
• Many ML models require features to be on a similar scale.
• Common techniques:
* Min-Max Scaling (scales values between 0 and 1)
* Standardization (mean = 0, std = 1)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df[['feature1', 'feature2']])
---
5. Encoding Categorical Variables
• Convert categorical data into numerical:
* Label Encoding: Assigns an integer to each category.
* One-Hot Encoding: Creates binary columns for each category.
pd.get_dummies(df['category_column'])
---
6. Summary
• Data cleaning is essential for reliable modeling.
• Handling missing values, outliers, scaling, and encoding are key preprocessing steps.
---
Exercise
• Load a dataset, identify missing values, and apply mean imputation.
• Detect outliers using IQR and remove them.
• Normalize numeric features using standardization.
---
#DataCleaning #DataPreprocessing #MachineLearning #Python #DataScience
https://yangx.top/DataScience4M
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