ML Time
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.
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TOP 10 SQL Concepts for Job Interview
- 1.Aggregate Functions (SUM/AVG)
- 2.Group By and Order By
- 3.JOINs (Inner/Left/Right)
- 4.Union and Union All
- 5.Date and Time processing
- 6.String processing
- 7.Window Functions (Partition by)
- 8.Subquery
- 9.View and Index
- 10.Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
- 1.Sampling
- 2.Experiments (A/B tests)
- 3.Descriptive Statistics
- 4.p-value
- 5.Probability Distributions
- 6.t-test
- 7.ANOVA
- 8.Correlation
- 9.Linear Regression
- 10.Logistics Regression
TOP 10 Python Concepts for Job Interview
- 1.Reading data from file/table
- 2.Writing data to file/table
- 3.Data Types
- 4.Function
- 5.Data Preprocessing (numpy/pandas)
- 6.Data Visualisation (Matplotlib/seaborn/bokeh)
- 7.Machine Learning (sklearn)
- 8.Deep Learning (Tensorflow/Keras/PyTorch)
- 9.Distributed Processing (PySpark)
- 10.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
Machine Learning with Graphs, Leskovec
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)
Last modified 1yr ago