Data Science in Python ( Explore Predictive Modelling , Machine Learning and Deep Learning) - Dvanalytics
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Data Science in Python ( Explore Predictive Modelling , Machine Learning and Deep Learning)

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    Python is a high-level programming language and it is mainly used in the field of mathematics and complex program designing, web development, software development and system scripting. However we will learn Python program uses for data analytics here

    Look out our curriculum

    Course Curriculum

    Session - 1 (Data Preparation & Regression Analysis)
    Introduction to Machine Learning 00:00:00
    <ul> <li>Introduction to Machine Learning tools</li> <li>Introduction to Python</li> <li>Tools comparison</li> <li>The learning paths</li> </ul>
    Data Exploration, Validation and Sanitization 00:00:00
    <ul> <li>Raw Data - issues</li> <li>Data Exploration for continuous variables</li> <li>Data Exploration for categorical variables</li> <li>Data Validation</li> <li>Data sanitization techniques</li> <li>Missing value treatment</li> <li>Outlier treatment</li> <li>Loans data case study and data cleaning</li> </ul>
    Regression Analysis 00:00:00
    <ul> <li>Correlation Simple</li> <li>Regression models</li> <li>R-Square</li> <li>Multiple regression</li> <li>Multicollinearity</li> <li>Individual Variable Impact</li> <li>Air passenger’s data case study</li> <li>SAT score data case study</li> </ul>
    Session - 2 (Classification using Logistic Regression and Trees)
    Logistic Regression usnig python 00:00:00
    <ul> <li>Need of logistic Regression</li> <li>Logistic regression models</li> <li>Validation of logistic regression models</li> <li>Multicollinearity in logistic regression</li> <li>Individual Impact of variables</li> <li>Confusion Matrix</li> <li>Service Provider Attrition data case study</li> </ul>
    Decision Trees 00:00:00
    <ul> <li>Segmentation</li> <li>Entropy</li> <li>Information gain</li> <li>Building Decision Trees</li> <li>Validation of Trees</li> <li>Pruning the trees</li> <li>Fine tuning the trees</li> <li>Prediction using Trees</li> <li>Customer retention case study</li> </ul>
    Session - 3 (Model Validation Techniques and Neural Networks)
    Model Selection and Cross validation 00:00:00
    <ul> <li>How to validate a model?</li> <li>What is a best model?</li> <li>Types of data</li> <li>Types of errors</li> <li>The problem of over fitting</li> <li>The problem of under fitting</li> <li>Bias Variance Trade-off</li> <li>Cross validation</li> <li>Boot strapping</li> <li>Attrition data case study</li> </ul>
    Neural Networks 00:00:00
    <ul> <li>Neural network Intuition</li> <li>Neural network and vocabulary</li> <li>Neural network algorithm</li> <li>Math behind neural network algorithm</li> <li>Building the neural networks</li> <li>Validating the neural network model</li> <li>Neural network applications</li> <li>Image recognition using neural networks</li> </ul>
    Session - 4 (Random Forest, Boosting and NLP)
    Random Forest and Boosting 00:00:00
    <ul> <li>Introduction</li> <li>Ensemble Learning</li> <li>How ensemble learning works</li> <li>Bagging Building models using</li> <li>Bagging Random Forest algorithm</li> <li>Random Forest model building</li> <li>Finetuning parameters and model selection</li> <li>Boosting Introduction</li> <li>Boosting algorithm</li> <li>GBM Model building and Validating in python</li> </ul>
    Text Mining and NLP 00:00:00
    <ul> <li>What is text mining</li> <li>The NLTK package</li> <li>Preparing text for analysis</li> <li>Step by step guide to prepare text data</li> <li>Text summarisation</li> <li>Sentiment analysis</li> <li>Naïve bayes technique for sentiment analysis</li> <li>Movie review sentiment analysis</li> </ul>
    Session - 5 (Introduction to TensorFlow and CNN)
    Deep Learning tool – Tensor Flow and Keras (Wrapper on Tensor Flow) 00:00:00
    <ul> <li>Deep Learning tool TensorFlow</li> <li>Comparison with python libraries</li> <li>Introduction to TensorFlow</li> <li>TensorFlow made easy with Keras</li> <li>Setting up Keras</li> <li>Keras on TensorFlow</li> <li>Keras Basic Commands</li> </ul>
    CNN 00:00:00
    <ul> <li>CNN Introduction</li> <li>Issues with Standard ANN</li> <li>Kernel filter</li> <li>Convolution layer</li> <li>Pooling layer Fully connected dense layer</li> <li>Weights and number of parameters</li> <li>Back propagation</li> <li>CNN Model building</li> <li>CNN Hyperparameters</li> <li>CNN tips and tricks</li> </ul>
    Session - 6 Hands-on (18) Case studies
    List of Case Studies used in the MLP course 00:00:00
    <ol> <li> Bank loans data cleaning – Data exploration and cleaning</li> <li>Air passenger prediction and driver analysis -Regression</li> <li>SAT score prediction and driver analysis -Regression</li> <li>E-com Website sales prediction case study -Regression</li> <li>Product sales analysis – Logistic Regression</li> <li>Customer attrition analysis -Logistic Regression</li> <li>Customer Survey Segmentation and Drivers – Decision Trees</li> <li>Internet service provider customer segmentation – Decision Trees</li> <li>Customer attrition analysis – Model selection and cross validation</li> <li>Productivity data -Neural networks</li> <li>Image recognition -Neural networks</li> <li>Car Sensor IOT data -Random forest</li> <li>E-com product classification - Boosting</li> <li>Movie review data – Sentiment Analysis - NLP</li> <li>Restaurants review data analysis – NLP</li> <li>News group review text classification – NLP</li> <li>Object Recognition problems – CNN</li> <li>Digit recognizer – CNN</li> </ol>