Machine Learning with Python and Statistics

Complete guide to Machine Learning and implementation of it through real time assignments

This course is specifically designed for students to learn the concepts in Python, Statistics and Maching Learning. We have tailored this curriculum so that even non-technical students can opt this course and understand the complex concepts. This course includes concepts in Python such as: Variables, functions,Pandas, Numpy, exception handling, web scraping, multithreading,connecting to database, matplotlib, modules, packages,files, flask,grammer correction and speech to text conversion. Projects in Python such as Hangman, Snake Game, Phonebook and Password Generator.

For Statistics it includes concepts such as Inferential statistics, Descriptive statistics,data types, population, Central Tendencies, Measures of Dispersion,Z-score, Min-max scaling, Co-variance, Correlation, Multi-collinearity, Anova, Kurtosis,Normal Distribution, Poisson Distribution,Bionominal Distribution,Hypothesis Testing, Central Limit Theorem, Degrees Of Freedom, Confidence Interval, P-value.

It also covers important Machine Learning algorithms such as Linear Regression, Logistic Regression,Confusion Matrix, Cost Matrix, Naive Bayes, K-Nearest Neighbors, Decision Tree Algorithm, Random Forest Algorithm,Support Vector Machine, Polynomial Regression, Unsupervised Learning, K-Means Clustering, Principal Component Analysis, DBSCAN, Linear Discriminant Analysis, Linear regression, Logistic Regression, Naive Bayes, KNN, Decision Tree, Support Vector Machine, K means Clustering, Principal Component Analysis, Hierarchical Clustering and Docker for Machine Learning. We have also included 'Deployment of Machine Learning' as one of the section so that user can learn to built the model from scratch and deploy it on its own.