Data Science and AI
All the topics in data science will covered with following concept:
- Mathematics beside of each model
- Which scenario we want to use particular algorithm
- How to apply it in tool
- Inferential thing of each model
- Difference between each model
- Introduction to Machine Learning & Predictive Modelling
- Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Statistics
- Standard Deviation
- Variance
- Concept of hypothesis testing
- T-test
- Chi-square
- Anova
- Correlation
- Probability
- Outliers
- Drop highly correlated features
Machine Learning
Supervised Learning – Classification
- Support vector machines classifier
- Naïve Bayes
- Nearest Neighbour
- Logistic Classification
- Decision tree Classifier
- Random forest classifier
Supervised learning -Regression
- Linear Regression
- Multiple Regression
- Polynomial Regression
- Exponential Regression
- Decision tree Regression
- Random forest Regression
- Feature selection using regression
Unsupervised Learning -Clustering
- K means clustering
- Hierarchical clustering
Ensemble method Techniques
Introduction to Natural Language Processing
Pre-processing Text
- Get Synonyms from words
- Bag of Words
- Remove Punctuation
- Remove Stop Words
- Replace Characters
- Stemming Words
- Lemmatization with Python
- Gender finder
- Strip Whitespace
- Tokenize Text
- speech tagging
- Sentence Segmentation
Convert Text to Speech in PythonDeep Learning
A subset of ML, Deep Learning (DL) is re-branding of neural networks- a class of models inspired by biological neurons in our brain.
Overview of the neural network, ANN