 top of page # R-Studio / Python /Machine Learning

Programming/ Codding solutions in R- Studio and Python.

• 55 minutes
• Online Meetup

## Service Description

We have a research scholar, who tutor coding/ programming in R-Studio and Python. He also tutor Machine Learning courses. Tutoring will happen on Google-meet. Programming: Python and R - Introduction- - Variables: How to define them, their data-types (integer, float, string and datetime) and their uses - String manuplations - Lists - If-else and exception handling - Loops : For loop and While loop. - Dictionaries, tuples and sets - Functions: Defining a function, its uses and execution - Recursion , map, and lambda functions - File handling: Open, read, and write in a file. - Important basic packages: - Python: Os, re, time, collections - R: dplyr, tidyr, data.table - Object-oriented Programming (Classes) - Final assignment 1 Exploratory Data Analysis (EDA) - Introduction - Statistics (Distributions, mean, median, mode, standard deviations, Z-score, standard error, accuracy, recall, precision, testing, odd rations, p-value etc.) - How to use inferential statistics using python and R programming? - Linear algebra - Data handling and curation: - Variable types: Continuous or discrete - Data-processing: Data-frame handling, imputation, dummy variables, avoid dummy trap etc ). - This will be thought using: - Python : Numpy and Pandas - R: dplyr - Sampling strategies: Random , stratified etc - Data representation: Various representation technique of data: - Histogram, density plots, bar graph, violin plots, pairplots, pie charts, box plots, line and scatter plots - This will be taught using: - Python: Matplotlib and Seaborn - R : ggplot, plotly - Final assignment 2 Machine Learning - Introduction (Supervised vs Unsupervised) - Machine learning Basic: - Learning Algorithms - Capacity, Overfitting, Underfitting, Hyperparameters, Validation Sets, Estimators, Bias and Variance - Maximum Likelihood Estimation - Supervised learning: - Regression Models: Linear regression, multiple linear regression, polynomial regression - Classification Models: Support vector Machines (SVM), Logistic regression, Navie bayes - Ensemble methods: Decision trees, Random Forest ,etc. - Unsupervised Learning: - Self-organisation Maps, Expectation maximisation, Gaussian Mixture models, Principal component analysis (PCA), Locally linear embedding (LLE), Factor analysis - Stochastic Gradient Descent - Building a Machine Learning Algorithm - All of above will be taught using Scikit-learn python package. - Each model will be taught in format Theory plus Practices