Time Complexity of Most Popular ML Algorithms
.
.
When selecting a machine learning model, understanding its time complexity is crucial for efficient processing, especially with large datasets.

For instance,
1️⃣ Linear Regression (OLS) is computationally expensive due to matrix multiplication, making it less suitable for big data applications.

2️⃣ Logistic Regression with Stochastic Gradient Descent (SGD) offers faster training times by updating parameters iteratively.

3️⃣ Decision Trees and Random Forests are efficient for training but can be slower for prediction due to traversing the tree structure.

4️⃣ K-Nearest Neighbours is simple but can become slow with large datasets due to distance calculations.

5️⃣ Naive Bayes is fast and scalable, making it suitable for large datasets with high-dimensional features.

𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 𝘆𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝗶𝗻 𝟮𝟬𝟮𝟱?😍

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𝗗𝗲𝗹𝗼𝗶𝘁𝘁𝗲 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 😍

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Data Science Roadmap – Step-by-Step Guide 🚀

1️⃣ Programming & Data Manipulation

Python (Pandas, NumPy, Matplotlib, Seaborn)

SQL (Joins, CTEs, Window Functions, Aggregations)

Data Wrangling & Cleaning (handling missing data, duplicates, normalization)


2️⃣ Statistics & Mathematics

Denoscriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)

Probability Theory (Bayes' Theorem, Conditional Probability)

Hypothesis Testing (T-test, ANOVA, Chi-square test)

Linear Algebra & Calculus (Matrix operations, Differentiation)


3️⃣ Data Visualization

Matplotlib & Seaborn for static visualizations

Power BI & Tableau for interactive dashboards

ggplot (R) for advanced visualizations


4️⃣ Machine Learning Fundamentals

Supervised Learning (Linear Regression, Logistic Regression, Decision Trees)

Unsupervised Learning (Clustering, PCA, Anomaly Detection)

Model Evaluation (Confusion Matrix, Precision, Recall, F1-Score, AUC-ROC)


5️⃣ Advanced Machine Learning

Ensemble Methods (Random Forest, Gradient Boosting, XGBoost)

Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV)

Deep Learning Basics (Neural Networks, TensorFlow, PyTorch)


6️⃣ Big Data & Cloud Computing

Distributed Computing (Hadoop, Spark)

Cloud Platforms (AWS, GCP, Azure)

Data Engineering Basics (ETL Pipelines, Apache Kafka, Airflow)


7️⃣ Natural Language Processing (NLP)

Text Preprocessing (Tokenization, Lemmatization, Stopword Removal)

Sentiment Analysis, Named Entity Recognition

Transformers & Large Language Models (BERT, GPT)


8️⃣ Deployment & Model Optimization

Flask & FastAPI for model deployment

Model monitoring & retraining

MLOps (CI/CD for Machine Learning)


9️⃣ Business Applications & Case Studies

A/B Testing & Experimentation

Customer Segmentation & Churn Prediction

Time Series Forecasting (ARIMA, LSTM)


🔟 Soft Skills & Career Growth

Data Storytelling & Communication

Resume & Portfolio Building (Kaggle Projects, GitHub Repos)

Networking & Job Applications (LinkedIn, Referrals)

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Difference between linear regression and logistic regression 👇👇

Linear regression and logistic regression are both types of statistical models used for prediction and modeling, but they have different purposes and applications.

Linear regression is used to model the relationship between a dependent variable and one or more independent variables. It is used when the dependent variable is continuous and can take any value within a range. The goal of linear regression is to find the best-fitting line that describes the relationship between the independent and dependent variables.

Logistic regression, on the other hand, is used when the dependent variable is binary or categorical. It is used to model the probability of a certain event occurring based on one or more independent variables. The output of logistic regression is a probability value between 0 and 1, which can be interpreted as the likelihood of the event happening.

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Understanding Bias and Variance in Machine Learning

Bias refers to the error in the model when the model is not able to capture the pattern in the data and what results is an underfit model (High Bias).

Variance refers to the error in the model, when the model is too much tailored to the training data and fails to generalise for unseen data which refers to an overfit model (High Variance)

There should be a tradeoff between bias and variance. An optimal model should have Low Bias and Low Variance so as to avoid underfitting and overfitting.

Techniques like cross validation can be helpful in these cases.

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There are no patterns.

Data is incorrect and doesn’t make sense.

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A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

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𝟯 𝗙𝗿𝗲𝗲 𝗧𝗖𝗦 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗘𝘃𝗲𝗿𝘆 𝗙𝗿𝗲𝘀𝗵𝗲𝗿 𝗠𝘂𝘀𝘁 𝗧𝗮𝗸𝗲 𝘁𝗼 𝗚𝗲𝘁 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆😍

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“The Best Public Datasets for Machine Learning and Data Science” by Stacy Stanford

https://datasimplifier.com/best-data-analyst-projects-for-freshers/

https://toolbox.google.com/datasetsearch

https://www.kaggle.com/datasets

http://mlr.cs.umass.edu/ml/

https://www.visualdata.io/

https://guides.library.cmu.edu/machine-learning/datasets

https://www.data.gov/

https://nces.ed.gov/

https://www.ukdataservice.ac.uk/

https://datausa.io/

https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html

https://www.kaggle.com/xiuchengwang/python-dataset-download

https://www.quandl.com/

https://data.worldbank.org/

https://www.imf.org/en/Data

https://markets.ft.com/data/

https://trends.google.com/trends/?q=google&ctab=0&geo=all&date=all&sort=0

https://www.aeaweb.org/resources/data/us-macro-regional

http://xviewdataset.org/#dataset

http://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php

http://image-net.org/

http://cocodataset.org/

http://visualgenome.org/

https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html?m=1

http://vis-www.cs.umass.edu/lfw/

http://vision.stanford.edu/aditya86/ImageNetDogs/

http://web.mit.edu/torralba/www/indoor.html

http://www.cs.jhu.edu/~mdredze/datasets/sentiment/

http://ai.stanford.edu/~amaas/data/sentiment/

http://nlp.stanford.edu/sentiment/code.html

http://help.sentiment140.com/for-students/

https://www.kaggle.com/crowdflower/twitter-airline-sentiment

https://hotpotqa.github.io/

https://www.cs.cmu.edu/~./enron/

https://snap.stanford.edu/data/web-Amazon.html

https://aws.amazon.com/datasets/google-books-ngrams/

http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm

https://code.google.com/archive/p/wiki-links/downloads

http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/

https://www.yelp.com/dataset

https://news.1rj.ru/str/DataPortfolio/2

https://archive.ics.uci.edu/ml/datasets/Spambase

https://bdd-data.berkeley.edu/

http://apolloscape.auto/

https://archive.org/details/comma-dataset

https://www.cityscapes-dataset.com/

http://aplicaciones.cimat.mx/Personal/jbhayet/ccsad-dataset

http://www.vision.ee.ethz.ch/~timofter/traffic_signs/

http://cvrr.ucsd.edu/LISA/datasets.html

https://hci.iwr.uni-heidelberg.de/node/6132

http://www.lara.prd.fr/benchmarks/trafficlightsrecognition

http://computing.wpi.edu/dataset.html

https://mimic.physionet.org/

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💯 Top 100+ Google Data Science Interview Questions

🌟 Essential Prep Guide for Aspiring Candidates

Google is known for its rigorous data science interview process, which typically follows a hybrid format. Candidates are expected to demonstrate strong programming skills, solid knowledge in statistics and machine learning, and a keen ability to approach problems from a product-oriented perspective.

To succeed, one must be proficient in several critical areas: statistics and probability, SQL and Python programming, product sense, and case study-based analytics.

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Feature Scaling is one of the most useful and necessary transformations to perform on a training dataset, since with very few exceptions, ML algorithms do not fit well to datasets with attributes that have very different scales.

Let's talk about it 🧵

There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are:
▪️ Normalization
▪️ Standardization

The 2 techniques perform the same task, but in different ways. Moreover, each one has its strengths and weaknesses.

Normalization (min-max scaling) is very simple: values are shifted and rescaled to be in the range of 0 and 1.

This is achieved by subtracting each value by the min value and dividing the result by the difference between the max and min value.

In contrast, Standardization first subtracts the mean value (so that the values always have zero mean) and then divides the result by the standard deviation (so that the resulting distribution has unit variance).

More about them:
▪️Standardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms.
▪️Standardization is robust to outliers.
▪️Normalization is sensitive to outliers. A very large value may squash the other values in the range 0.0-0.2.

Both algorithms are implemented in the Scikit-learn Python library and are very easy to use. Check below Google Colab code with a toy example, where you can see how each technique works.

https://colab.research.google.com/drive/1DsvTezhnwfS7bPAeHHHHLHzcZTvjBzLc?usp=sharing

Check below spreadsheet, where you can see another example, step by step, of how to normalize and standardize your data.

https://docs.google.com/spreadsheets/d/14GsqJxrulv2CBW_XyNUGoA-f9l-6iKuZLJMcc2_5tZM/edit?usp=drivesdk

Well, the real benefit of feature scaling is when you want to train a model from a dataset with many features (e.g., m > 10) and these features have very different scales (different orders of magnitude). For NN this preprocessing is key.

Enable gradient descent to converge faster

𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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Key Concepts for Data Science Interviews

1. Data Cleaning and Preprocessing: Master techniques for cleaning, transforming, and preparing data for analysis, including handling missing data, outlier detection, data normalization, and feature engineering.

2. Statistics and Probability: Have a solid understanding of denoscriptive and inferential statistics, including distributions, hypothesis testing, p-values, confidence intervals, and Bayesian probability.

3. Linear Algebra and Calculus: Understand the mathematical foundations of data science, including matrix operations, eigenvalues, derivatives, and gradients, which are essential for algorithms like PCA and gradient descent.

4. Machine Learning Algorithms: Know the fundamentals of machine learning, including supervised and unsupervised learning. Be familiar with key algorithms like linear regression, logistic regression, decision trees, random forests, SVMs, and k-means clustering.

5. Model Evaluation and Validation: Learn how to evaluate model performance using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, and confusion matrices. Understand techniques like cross-validation and overfitting prevention.

6. Feature Engineering: Develop the ability to create meaningful features from raw data that improve model performance. This includes encoding categorical variables, scaling features, and creating interaction terms.

7. Deep Learning: Understand the basics of neural networks and deep learning. Familiarize yourself with architectures like CNNs, RNNs, and frameworks like TensorFlow and PyTorch.

8. Natural Language Processing (NLP): Learn key NLP techniques such as tokenization, stemming, lemmatization, and sentiment analysis. Understand the use of models like BERT, Word2Vec, and LSTM for text data.

9. Big Data Technologies: Gain knowledge of big data frameworks and tools like Hadoop, Spark, and NoSQL databases that are used to process large datasets efficiently.

10. Data Visualization and Storytelling: Develop the ability to create compelling visualizations using tools like Matplotlib, Seaborn, or Tableau. Practice conveying your data findings clearly to both technical and non-technical audiences through visual storytelling.

11. Python and R: Be proficient in Python and R for data manipulation, analysis, and model building. Familiarity with libraries like Pandas, NumPy, Scikit-learn, and tidyverse is essential.

12. Domain Knowledge: Develop a deep understanding of the specific industry or domain you're working in, as this context helps you make more informed decisions during the data analysis and modeling process.

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