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Data Science

Course include practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

Level 01

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  • Part 1 - Python Concepts: Data Types and operation including (list,tuple,dictionary and string), Flow control ,Functions

  • Part 2 - Introduction to DataScience

  • Part 3- Data Preprocessing

  • Part 4 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

  • Part 5 - Classification: Logistic Regression, K-NN, SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Level 02

Part 1 - Association Rule Learning: Apriori

Part 2 - Clustering: K-Means

Part 3 - Natural Language Processing: Bag-of-words model and algorithms for NLP

Part 4 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks 

Part 5 - Dimensionality Reduction: PCA, LDA  

Part 6 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, XGBoost

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Level 3

Hardware Integration and project building

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