VisionNLP | Learn to Implement AI, Data Science

Course Description

Would you like to understand why machine learning is so popular? What are the challenging approaches we can solve using machine learning? In the technology world, everything has become data-driven. Huge amount of data is produced every second in multiple formats numbers, text, audio, Images and this implies that the need of data scientists has also grown who can analyse large amounts of data. Data Science is the fuel of the 21st century! Highest paid salary!

Course Description

What is machine learning

Applications of machine learning
Supervised learning and Unsupervised learning
Machine learning approach for real life use cases

Data Manipulation and Visualisation Techniques

Feature selection techniques

Principal component analysis
Wrapper method
Random forest variable selection criteria
Filter method

Linear regression analysis

Ordinary Least square methods
Multiple Linear regression
advanced regression techniques
R^2 value and Adjusted R^2 value

All about Matrices

Model improvement

Overfitting
Bias-variance tradeoff
Imbalanced Dataset problem
L1 and l2 regularization

Classification algorithms

Logistic Regression:
Concepts of MLE, Drawback of Linear Regression fails, Sigmoid function, log odds ratio, Cost Function, real-life case study with python
k-Nearest Neighbors and Naive Bayes:
KNN detailed algorithm, bayes theorem
Decision Tree and Random Forest:
Construction of tree, terminologies, gini index, information gain, optimising performance, variable importance
SVM:
Rewind of basic calculus, LaGrange’s Theorem, SVM hyperplane, Hinge loss
Bagging & Boosting