This class is for those students who want to build their career in data science.
Education criteria: BE/B.Tech , BCS, BSC
Skills you learn:
Python
- Python Introduction
- Variables
- Operators
- Conditional Statements
- DataTypes:
- Int
- Float
- Complex
- String
- List
- Tuple
- Set and FrozenSet
- Dict
- Bool
- Loops in Python:
- For Loop
- While Loop
- Functions
- Python Functions
- Types of Arguments
- Recursive Function
- Lambda Functions
- Built-in Functions
- OS (Operating System)
- Regular Expressions
- File Handling
- Exception Handling
- Object-Oriented Programming:
- Inheritance
- Encapsulation
- Polymorphism
- Abstraction
- Iterator and Generator
- Decorator
Machine Learning
- Data Science Libraries:
- Numpy
- Pandas
- Scipy
- Sklearn
- Matplotlib
- Seaborn
- Preprocessing and Model Building
- Visualization
❖ Machine Learning Algorithms:
Supervised Machine Learning:
- Linear Regression
- Logistic Regression
- K-Nearest Neighbour
- Decision Tree
- Random Forest
- AdaBoost
- Support Vector Machine
- Naive Bayes Classifier
- Gradient Boost
- Extreme Gradient Boost
Unsupervised Machine Learning:
- K-Means Clustering
- Principal Component Analysis
- Preprocessing:
- Handling of Missing Values
- Handling of Outliers:
- Encoding(Label Encosing, One Hot Encoding)
- Feature scaling (Normalization,Standardization)
- Feture Selection:
- Filter Method
- Wrapper Method
- Embedded Method
- Hypothesis Testing:
- P-value
- Z Test
- T-Test
- ANOVA
- Chi-Square Test
Deep Learning
NLP
SQL