Course Content
Introduction to Datascience
- What is Datascience
- What is machine learning
- What is deep learning
- What is AI
- Data Analytics and its types
Introduction to Python
- What is Python
- Why Python
- Installing Python
- Python IDE's
- Spyder Overview
- Jupyter Notebook Overview
Python Packages
- Pandas
- Numpy
- Sci-kit learn
- Mat-plot library
Importing data
- Reading csv files
- loading and storing data with clipboard
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
Manipulating data
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
Statistics Basics
Central Tendency
* Mean
* Median
* Mode
* Skweness
* Normal distribution
Probability Basics
* What does mean by probability
* Types of probability
* ODDS Ratio
Standard Deviation
* Data deviation & distribution
* Variance
Bias variance Trade off
* Underfitting
* Overfitting
Distance metrics
* Eucliclean Distance
* Manhattan Distance
Outlier analysis
* What is an Outlier
* Inter Quartile Range
* Box and Whisker Plot
* Upper Whisker
* lower Whisker
* Scatter Plot
* Cook's Distance
Missing Value treatments
* What is NA
* Central Imputation
* KNN imputation
* Dummification
Correlation
* Pearsons Correlation
* Positive and Negative Correlation
Error metrics
Classification
* Confusion matrix
* Precision
* Recall
* Specificity
* F1 Score
Regression
* MSE
* RMSE
* MAPE
Introduction to Machine learning
- Link for ISLR
- Introduction to Machine Learning
- Machine Learning with Python
Supervised learning
Linear Regression
* Linear Equation
* Slope
* Intercept
* R Square value
Logistics regression
* ODDS ratio
* Probability of success
* Probability of failure
* ROC curve
Unsupervised learning
- K-Means
- K- Means ++
- Hierarchical Clustering
Other Machine Learning Algorithms
- K.Nearest Neighbour
- Naive Bays Classifier
- Decision Tree - CART
- Decision tree - C50
- Random Forest
