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Data Science Using RData Science with Python

Python Programming

  • Data Types
  • Anaconda Installaon: Jupyter Notebook, Spyder
  • Collections: lists, tuples, sets, diconaries
  • Reading and Writing Data: Text files, Excel sheets
  • Control Structures
  • Creating Functions
  • Numpy operations
  • Pandas Operations
    • Sub setting the data
    • Reshaping the Data
  • Graphics Using matplotlib and seaborn
  • Object Oriented Programming in Python
    • Classes and Objects
    • Class Inheritence
  • Data Preprocessing:
    • Imputing Missing Values: Mean, Median, K-NN Imputers
    • Hot Encoding / Dummy
    • Ordinal Encoding

     

    Structured Query Language (SQL)

  • Creating Databases
  • Creating tables, views
  • Connecting the MYSQL database with Python

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    Machine Learning Algorithms

    Libraries covered: scikit-learn, mlxtend, h2o

    Supervised Learning

  • Naïve Bayes Algorithm
  • K-nearest Neigbour Classifiers and Regressors
  • Linear and Logistic Regression
  • Stochastic Gradient Descent Classifier and Regressor
  • Regularized Regression: Lasso, Ridge and Elasc Net
  • Decision Trees (Single Tree): Classifier and Regressor
  • Random Forest Classifier and Regressor
  • Support Vector Machines
  • Linear & Quadratic Discriminant Analysis
  • Neural Networks: MLP Classifier and Regressor
  • Model Ensembling
  • Bagging Classifier and Regressor
  • Boosting Classifier and Regressor
  • Stacking Classifier and Regressor
  • Using h2o Technology:
    • H2o Flow
    • Applying different models in h2o

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      Unsupervised Learning

    • Cluster Analysis
    • Hierarchical Clustering
    • K-means Clustering
    • Association Rules Mining (mlxtend library)
    • Principal Components Analysis

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      Time Series Analysis (statsmodels & sktime libraries)

    • Moving Average
    • Simple Exponential Smoothening
    • Holt-Winter's Method
    • ARIMA Models

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    Machine Learning Applications

  • Recommendation Systems (Library: scikit-surprise)
    • K-NN
    • SVD

      • Natural Language Processing (Libraries: scikit-learn, genism)

      • Term Document Matrix
      • Count and TF-IDF Vectorizaon
      • Word Cloud
      • Word embedding
      • Introducon to Prompt Engineering

       

      Deep Learning Algorithms

    • Deep Learning Basics
    • Introducon to TensorFlow
    • Introducon to Keras & PyTorch
    • Mullayer Perceptron using Keras
    • Saving and Loading a Model in Keras
    • Early Stopping for Prevenng Overfing
    • Dropout
    • Problems of Exploding and Vanishing gradients
    • Training Methods for Neural Network (High-Level Overviews only)
    • Classic Backpropagaon o Momentum Backpropagation
    • Update of weights with single training set element, Batch Training, Mini-batch Training, Stochastic Gradient Descent o ADAM
    • L1 and L2 Regularizaon
    • Convolutional Neural Network
      • Convolutional Concept
      • Inception Network
      • Transfer Learning
      • Data Augmentaon
      • Recurrent Neural Network (RNN)
        • RNN Concept
        • Types of RNNs
        • Long Short-Term Memory (LSTM) - (High-Level Overview only)

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          Machine Learning options in Cloud Computing with AWS SageMager

        • AWS Accounts Set-up
        • Billing, Billing Alerts
        • Configuring IAM Users
        • Seting up Notebook instance
        • Using built-in Machine Learning algorithms in SageMaker
          • XGBoost
          • PCA

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            Visualization with Power BI

          • Creating basic charts
          • Dashboards

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