Machine Learning

 

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HTML5, a markup language for the web has got its popularity due to the current internet era. CSS3, paves the way by giving good look and feel, adding animation, viewing the web pages in different devices etc. These two technologies together is a must learn language for any front end developer who wants to build websites or wants a niche career in UI technologies .

Prerequisites : Anyone can learn Web Designing and Development.

Level : Beginner  (By the end of your course become a Professional)

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    Machine Learning Developer Curriculum

    Duration :  __ Months

    Batches : Weekend and Weekday Batches Available

    Mode : Online and Offline

    Certificate : Available after completing the course and projects.

    Placement : Available

     

    • Module 1 Introduction to Machine Learning (ML)

      What is Machine Learning?, use Cases of Machine Learning, types of Machine Learning- Supervised to unsupervised methods, machine learning workflow

    • Module 2Statistics for Data Science

      Common charts used, inferential statistics, probability, central limit theorem, normal distribution and hypothesis testing.

    • Module 3Data Visualization

      Plotting basic statistical charts in python, data visualization with Matplotlib, statistical data visualization with Seaborn, Interactive data visualization with Bokeh.

    • Module 4Exploratory Data Analysis

      Introduction to Exploratory Data Analysis steps, Plots to explore relationship between two variables, histograms, box plots to explore a single variable, heat maps, pair plots to explore correlations.

    • Module 5Data Preprocessing

      Preprocessing techniques like missing value imputation, encoding categorical variables, scaling, too many nulls, same values/ skew, data types, missing value imputation, when column doesn't have missing values, categorical attributes, related attributes.

    • Module 6Linear Regression

      Introduction to Linear Regression, use cases of linear regression, How to fit a linear regression model?, evaluation and interpreting results from linear regression models, project

    • Module 7Logistics Regression

      Introduction to Logistics Regression, logistic regression uses cases, understand use of odds and Logit function to perform logistics regression, project.

    • Module 8Decision trees and random forests

      Introduction to decision trees and random forest, understanding criterion used in decision trees, using ensemble methods in decision trees, application of random forest, project.

    • Module 9Model evaluation techniques

      Introduction to evaluation metrics and model selection in Machine Learning, importance of confusion matrix for predictions, measures of model evaluation, use AUC-ROC curve to decide best model, K-fold cross validation, parameter tuning, grid search, XGBoost, project

    • Module 10Dimensionality reduction using PCA

      Introduction to KNN, Calculate neighbours using distance measures, find optimal values of K in KNN method, advantages and disadvantages of KNN, project

    • Module 11KNN (K - nearest neighbour)

      Unsupervised Learning: Introduction to curse dimensionality, What is dimensionality reduction?, techniques used in PCA to reduce dimensions, applications of principle component analysis, project

    • Module 12Naïve Bayes classifier

      Introduction to Naïve Bayes classifier, refresher on probability theory, application of Naïve Bayes algorithm on machine learning, project

    • Module 13K-means clustering techniques

      Introduction, decide clusters by adjusting centroids, find optimal 'k value' in kmeans, understanding application of clustering in machine learning, project.

    • Module 14Support vector machines(SVM)

      Introduction, figure decision boundaries using support vectors, find optimal 'k value' in kmeans, application of SVM in machine learning, project.

    • Module 15Time series forecasting

      Introduction to time series analysis, stationary vs non stationary data, components of time series data, interpreting autocorrelation and partial autocorrelation function, stationary data and implement ARIMA model, project.

    • Module 16Ensemble Learning

      Introduction to ensemble learning, what are bagging and boosting techniques?, what is bias variance trade off?

    • Module 17Stacking

      Introduction to stacking, use cases of stacking, how stacking improves machine learning models?, project

    • Module 18Optimization

      Introduction to optimization in ML, application of optimization methods, Optimization techniques: Linear programming using excel solver, How stochastics gradient descent(SGD) works?, project.