Machine Learning
This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction.
Course Contents
This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural …
About This Course
About This Course
Basic Stat
- Data Types
- Random Variable
- Probability
- Probability Distribution
- Sampling Funnel
- Measure Of central tendency
- Measures of Dispersion
- Expected Value
- Graphical Techniques
- Introduction to R
- R Studio
Python
- Introduction to Python (Installation basic commands)
Basic Stat continued
- Python & R Contd…Skewness & Kurtosis
- Box Plot
- Normal Distribution
- Sampling Variation
- CLT
- Confidence interval
Hypothesis Testing
- Intro to HT, 2 sample t test, 1 sample tests
- Other parametric and non parametric tests
- By R Code
Linear Regression
- Scatter Diagram
- Corr Analysis
- Principles of Regression
- Intro to Simple Linear Regression
- Multiple Linear Regression
Logistic Regression
- Principles of Logistic regression
- Multiple Logistic Regression
- ROC curve
- Gain char
Disc Prob Distribution
- Binomial
- Neg Binomial
- Possion
Adv Regression
- Poission
- Neg Binomial
- Models with Excessive ‘0’s
Multinomial Regression
- Multinomial Regression
Supervised Classifiers
- KNN
- Decision Tree & Random Forest
- Bagging and boosting
Supervised BlackBox
- ANN & SVM
JOB Oriented Training
Interview questions
Interview tips
Resume preparation tips
