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Brief Introduction

A complete guide to learn data science for beginners.

This learning path is intended for everyone who wants to learn data science and build a career in data field especially data analyst and data scientist. In this guide, there is a corresponding link in each section that will help you to learn (at least to start) in each chapter.

Table of Contents

Programming

• Basic Python
• Object-oriented Programming
• Intro to DBMS
• SQL Data Manipulation
• Git
• Code Versioning Platform: Github | Bitbucket | Gitlab
• Shell Script
• Competitive Programming: Hackerrank | Leetcode | Kattis

Mathematics & Statistics

• Linear Algebra
• Calculus
• Descriptive Statistics
• Data Distributions
• Statistical Testing
• Exploratory Data Analysis
• Regression
• TOOLBOX: Pandas
• TOOLBOX: Numpy
• TOOLBOX: Matplotlib
• TOOLBOX: Seaborn

Machine Learning

• ### Supervised Learning

• K-NN (K-Nearest Neighbors)
• Naive Bayes
• Support Vector Machine
• Random Forest
• AdaBoost
• Gradient Boosting
• XGBoost
• CatBoost
• Bagging Classifier
• Voting Classifier
• Stacking Classifier
• TOOLBOX: Scikit Learn
• TOOLBOX: statsmodels
• CASE STUDY: House Pricing
• CASE STUDY: Titanic
• CASE STUDY: Credit Scoring

• ### Unsupervised Learning

• K-Means Clustering
• DBSCAN
• Hierarchical Clustering

Evaluation Metrics

• ### Supervised Learning

• Confusion Matrix
• Accuracy
• Precision
• Recall
• F Score
• Hamming Loss
• ROC (Receiver Operating Characteristic)
• ROC AUC (Area Under Curve)
• Top K Accuracy
• MAE
• MSE
• MRR
• DCG
• NDCG
• PSNR
• SSIM
• IoU
• Perplexity
• BLEU score

• ### Unsupervised Learning

• Elbow Method>)
• Silhouette Coefficient

Deep Learning

• Activation Functions
• Linear Layer
• CNN (Convolutional Neural Networks)
• RNN (Recurrent Neural Networks)
• Optimization
• Loss Functions / Objective Functions
• Dropout
• Batchnorm
• Learning Rate Scheduler
• TOOLBOX: PyTorch
• TOOLBOX: Tensorflow
• TOOLBOX: Keras

ML Applications

• Timeseries
• Recommendation System
• Netwok Analysis

Computer Vision

• Image Classification
• Object Detection
• Object Segmentation
• Instance Segmentation

NLP & NLU

• Tokenization
• Sequence
• Padding
• Stemming
• Lemmatization
• Feature Extraction
• Feature Selection
• Term Weighting
• Embedding
• Part of Speech Tagging
• Named Entity Recognition
• Popular NLP & NLU Architecture
• STUDY CASE: News Classification
• STUDY CASE: Sentiment Analysis
• STUDY CASE: Machine Translation

Speech Recognition

🠥🠥 Back to Table of Contents 🠥🠥

Model Deployment

🠥🠥 Back to Table of Contents 🠥🠥

Book References

• Practical Deep Learning for Coders
• Dive Into Deep Learning
• Interpretable Machine Learning
• An Introduction to Statistical Learning with Applications in R
• Natural Language Processing with Python

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