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Courses
Machine Learning
Machine Learning
Course Content :
Python Introduction
Understand basic Python syntax
Set up Python development environment
Run simple Python programs
Python NumPy package
NumPy data structures
NumPy data manipulation procedures
SciPy algorithms and packages like cluster, spatial useful for Machine learning
Understand other Python packages like Pandas and Scikit-learn
Understand key features of Python Pandas and Scikit-learn packages useful for Machine Learning
Data structures and data analysis tools
Introduction to Classification, regression and clustering algorithms implemented in Scikit
Machine Learning introduction
Understand what is machine learning
Understand difference between supervised and unsupervised learning
Statistics and Visualization
Descriptive Statistics
Different types of Distributions
Inferential statistics and correlation
Statistical Hypothesis Testing
Linear regression
Linear classifiers; logistic regression
Data Visualization—Histograms, Scatter-plots and box-plots
Short assignment on developing Python programs for implementing statistical techniques
Hands-on development of simple Python programs for statistical solutions
Machine learning models and algorithms - KNN
Introduction to Machine Learning models
Model performance evaluation techniques
KNN, clustering and classification
Decision Tree construction
Structure of decision trees
Data input
Decision tree construction: A simplified example
Information Gain
The Concept Learning System (CLS)
Training, testing and predictive accuracy
Bayesian statistics and Bayesian Classification
Scientific data gathering
Data Analysis
Bayesian inference
Microsoft Certified Trainer
Industry Expert Trainer
Practical Hand-on Session
Real World Examples
Microsoft Certification
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