Machine Learning

Machine Learning is a technique behind innovations such as facial recognition, self-driving cars, predictive results in search, Predictive text etc. It is playing a significant role in developing new algorithms which permits software application to predict/achieve accurate results without being explicitly programmed.

Machine Learning is implemented where programming or developing an efficient algorithm is problematic or not feasible. Machine Learning is revolutionizing the computing ecosystem.

The objective of this training program is to understand how machines can learn from its past experience which is considered as the core of AI. We focus on creating strong fundamental knowledge sets on building algorithms by mastering the principles of statistical analysis.

What Will I Learn?

• Introduction to Machine Learning
• Programming with R
• R based projects development
• Programming with python
• Python based projects development
• Statistics Essentials
• Knowledge extraction using Statistics Essentials
• Pre program preparation
• Supervised Learning
• Classification Algorithms
• Decision Tree
• GINI
• Entropy
• KNN
• SVM
• Naïve Bayes Classifier
• Artificial Neural Network
• Regression Algorithms
• Logistic regression
• Regression Analysis
• Linear Regression
• Multiple Linear Regression
• Random Forest
• Poisson Regression
• Lasso & Ridge Regression
• Zero-Inflated Regression
• Multinomial Regression
• Artificial Neural Network
•  
• Unsupervised Learning
• Euclidean Distance
• Hierarchical clustering
• k-means clustering
• Manhattan Distance
• k-Medoids
• DBSCAN
• CLARA
• PCA
• Deep learning(Neural Networks)
• ANN for Classification
• ANN for Regression
• RNN for higher complexity problems
• RNN for time dependent problems
• Data Analysis
• Word cloud
• Web Extraction
• Survival Analysis
• Dimension Reduction
• Text mining and NLP