ML Time
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Indian Flag with Turtle using Python
This Republic day I tried to do something creative and made an Indian Flag with Turtle using Python. Turtle is a pre-installed Python library. It enables users to create pictures and shapes by providing them with a virtual canvas.
You can also refer to the YouTube video tutorial for better understanding: https://lnkd.in/dUrctgE
GitHub Repo: https://lnkd.in/disintQ
Blog link: https://lnkd.in/dcUuTgg
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ML Interview Questions
BudgetML
1-Introduction to Time Series and Forecasting https://lnkd.in/gqZbe7p
2-Statistics and Analysis of Scientific Data https://lnkd.in/g-hbjWZ
3-Linear Algebra Done Right https://lnkd.in/gqCT-6g
4-Linear Algebra https://lnkd.in/gJSPXQd
5-Algebra https://lnkd.in/gc-H3Kk
6-Understanding Analysis https://lnkd.in/g_RXeVk
7-Understanding Statistics Using R https://lnkd.in/g27NnMy
8-An Introduction to Statistical Learning https://lnkd.in/gyjbQ-A
9-Statistical Learning from a Regression Perspective https://lnkd.in/g2gFWfi
10-Robotics https://lnkd.in/gJDEgec
11-Regression Modeling Strategies https://lnkd.in/gR3Mufh
12-A Modern Introduction to Probability and Statistics https://lnkd.in/gCgbX6m
13-The Python Workbook https://lnkd.in/gw4tAf7
14-Machine Learning in Medicine — a Complete Overview https://lnkd.in/g43uZiT
15-Introduction to Data Science https://lnkd.in/gSvtBCs
16-Applied Predictive Modeling https://lnkd.in/gcHus2e
Data Science Process https://pos.li/2fki4i
Data Visualization in Business https://pos.li/2fki4j
Know Machine Learning Key Terminology https://pos.li/2fki4k
Understand Machine Learning Implementation https://pos.li/2fki4l
Machine Learning Applications on Marketing https://pos.li/2fki4m and Retail https://pos.li/2fki4f
TOP 10 SQL Concepts for Job Interview
Aggregate Functions (SUM/AVG)
Group By and Order By
JOINs (Inner/Left/Right)
Union and Union All
Date and Time processing
String processing
Window Functions (Partition by)
Subquery
View and Index
Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
Sampling
Experiments (A/B tests)
Descriptive Statistics
p-value
Probability Distributions
t-test
ANOVA
Correlation
Linear Regression
Logistics Regression
TOP 10 Python Concepts for Job Interview
Reading data from file/table
Writing data to file/table
Data Types
Function
Data Preprocessing (numpy/pandas)
Data Visualisation (Matplotlib/seaborn/bokeh)
Machine Learning (sklearn)
Deep Learning (Tensorflow/Keras/PyTorch)
Distributed Processing (PySpark)
Functional and Object Oriented Programming
Data Science Topics: CRISP – DM - Project Management Methodology Exploratory Data Analytics (EDA) / Descriptive Analytics Statistical Data Business Intelligence and Data Visualization Plots & Inferential Statistics Probability Distributions (Continuous & Discrete) Hypothesis Testing - The ‘4’ Must Know Hypothesis Tests Data Mining Supervised Learning – Linear Regression, OLS Predictive Modelling – Multiple Linear Regression Lasso and Ridge Regressions Logistic Regression – Binary Value Prediction, MLE Multinomial Regression Advanced Regression for Count Data Data Mining Unsupervised Learning - Clustering Data Mining Unsupervised Learning - Dimension Reduction (PCA) Data Mining Unsupervised Learning - Association Rules Recommendation Engine Network Analytics Machine Learning - k - NN Classifier Decision Tree & Random Forest Ensemble Techniques - Bagging and Boosting AdaBoost & Extreme Gradient Boosting Text Mining & Natural Language Processing (NLP) Machine Learning Classifier Technique - Naive Bayes Introduction to Perceptron, Multilayer Perceptron Building Blocks of Neural Network Deep Learning Black Box Technique - Neural Network Deep Learning Black Box Technique - SVM Survival Analytics Forecasting/Time Series – Model Driven Algorithms Forecasting/Time Series – Data Driven Algorithms
http://gvv.mpi-inf.mpg.de/projects/NHRR/ http://gvv.mpi-inf.mpg.de/projects/NHRR/data/1415.pdf
Machine Learning with Graphs, Leskovec
1-The Elements of Statistical Learning https://lnkd.in/gd-PpCa
2-Introductory Time Series with R https://lnkd.in/gemsHzv
3-A Beginner’s Guide to R https://lnkd.in/gwQ6m_B
4-Data Structures and Algorithms with Python https://lnkd.in/g9_djwr
5-Introduction to Statistics and Data Analysis https://lnkd.in/gUUJMCj
6-Principles of Data Mining https://lnkd.in/gMgmc-P
7-Computer Vision https://lnkd.in/gftqxf9
8-Data Mining https://lnkd.in/gJ6iugA
9-Robotics, Vision and Control https://lnkd.in/gq-q9i5
10-Statistical Analysis and Data Display https://lnkd.in/grCNSYq
11-Statistics and Data Analysis for Financial Engineering https://lnkd.in/gRn8zWd
12-Stochastic Processes and Calculus https://lnkd.in/gnWaWW6
13Statistical Analysis of Clinical Data on a Pocket Calculator https://lnkd.in/gM7VCgG
14-Clinical Data Analysis on a Pocket Calculator https://lnkd.in/gvxBvg6
15-The Data Science Design Manual https://lnkd.in/gKxgaRD
16-An Introduction to Machine Learning https://lnkd.in/gykPC2B
The Laplace Transform: A Generalized Fourier Transform
’Introduction to Statistics for Data Science, Exploratory Data Analysis in Python‘
Distributions
Probability Mass Functions
Cumulative distribution functions
Modeling distributions
Probability density functions
Relationships between variables
Estimation
Hypothesis testing
Linear least squares
Regression
Time series analysis
Survival analysis
Analytic methods
Credit: Allen B. Downey
XGBoost Tutorials:
https://lnkd.in/g3dSRxz 1-Introduction to Boosted Trees 2-Distributed XGBoost with AWS YARN 3-Distributed XGBoost with XGBoost4J-Spark 4-DART booster 5-Monotonic Constraints 6-Random Forests in XGBoost 7-Feature Interaction Constraints 8-Text Input Format of DMatrix 9-Notes on Parameter Tuning 10-Using XGBoost External Memory Version (beta)
https://github.com/rakesh4real/fastapi-template https://realtime-forex.herokuapp.com/
https://www.mit.edu/~amidi/teaching/data-science-tools/ https://github.com/shervinea/mit-15-003-data-science-tools
https://www.python-machinelearning.com/
https://guide.allennlp.org/overview
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