DATA SCIENCE (AI, ML, DL) Course Content
DATA SCIENCE (AI, ML, DL)
Need for Sampling?
Different types of Sampling
Simple random sampling
Systematic sampling
Stratified Sampling
Implementation of sampling techniques using python
e. Data distributions
Normal Distribution
Binomial Distribution
Binomial Approximated to Normal
Implementation of distributions using python
f. Inferential statistics
Why inferential statistics?
Z score calculation
Defining p value and implementations using python
Inferring from sample to population
Sampling distribution of sample means
g. Hypothesis testing
Confidence Interval
Testing the hypothesis
Type I error
Type II error
Null and alternate hypothesis
Reject or acceptance criterion
DATA SCIENCE (AI, ML, DL)
a. Getting to understand structure of Matplotlib
b. Configuring grid, ticks.
c. text, color map, markers, widths with Matplotlib
d. configuring axes, grid,
e. hist, scatterplots
f. bar charts
g. multiple plots
h. 3D plots
i. Correlation matrix plotting
DATA SCIENCE (AI, ML, DL)
Model validation – tests to validate assumptions
Multiple linear regression
Disadvantages of linear models
b. Logistic Regression
Need for logistic regression
Logit link function
Maximum likelihood estimation
Model development and interpretation
Confusion Matrix – error measurement
ROC curve
Measuring sensitivity and specificity
Advantages and disadvantages of logistic regression models
c. Time series analysis – Forecasting
d. Model validation and deployment
RMSE – Root Mean squared error
MAPE – Mean Average Percentage Error
Confusion matrix and Misclassification rate
Area under the curve (AUC) , ROC curve