Machine Learning & Deep Learning in Python & R

overview of the course and what to expect in machine learning and deep learning.

guidance on installing Python and Anaconda for your machine learning setup.

highlighting a significant step in the course setup process.

instructions on how to open and access Jupyter Notebook.

introduction to the features and functionalities of Jupyter Notebook.

exploring the use of arithmetic operators in Python programming.

understanding how to work with strings in Python.

learning about lists, tuples, and directories in Python.

using the Numpy library for numerical operations in Python.

applying the Pandas library for data manipulation in Python.

working with the Seaborn library for data visualization in Python.

steps for installing R and R Studio for data analysis.

overview of the basics of R programming and R Studio.

understanding how to use packages in R for enhanced functionality.

inputting data using built-in datasets available in R.

instructions for manual data entry in R.

guidance on importing data from CSV or text files into R.

creating barplots in R for visual data representation.

instructions for creating histograms in R.

overview of different types of data used in statistics.

explaining the various types of statistics and their uses.

techniques for graphically describing and visualizing data.

measures to determine the center of a data set.

understanding measures of dispersion in data analysis.

introduction to the fundamental concepts of machine learning.

learn how to build a machine learning model.

discover how to gather business knowledge for data analysis.

explore data through various techniques and methods.

understand the dataset and its accompanying data dictionary.

import data into Python for analysis and modeling.

import datasets into R for further analysis.

perform univariate analysis and exploratory data analysis (EDD).

conduct exploratory data analysis (EDD) using Python.

execute exploratory data analysis (EDD) using R.

learn techniques for treating outliers in data.

manage outlier treatment using Python.

handle outlier treatment in R.

impute missing values in datasets.

understand the importance of defining a problem statement for linear regression.

explore basic equations and the ordinary least squares (OLS) method in linear regression.

learn how to assess the accuracy of predicted coefficients in linear regression.

assess model accuracy using RSE and R squared in linear regression.

implement simple linear regression using Python.

implement simple linear regression using R.

learn about multiple linear regression and its applications.

understand the concept of the F-statistic in linear regression.

interpret the results of categorical variables in linear regression.

apply multiple linear regression techniques using Python.

apply multiple linear regression techniques using R.

learn how to split data into training and test sets.

learn about the data and data dictionary for classification models.

import data in Python for classification model preparation.

Learn how to import a dataset into R for analysis.

Understand the process of exploratory data analysis in Python.

Explore exploratory data analysis techniques using R.

Discover methods for treating outliers in Python.

Learn how to handle outliers in R.

Implement missing value imputation techniques in Python.

Perform missing value imputation using R.

Understand variable transformation and deletion methods in Python.

Learn about variable transformation techniques in R.

Create dummy variables in Python for categorical data.