Machine Learning Course:
>Understand Python environment and coding.
>Understand Machine Learning Algorithms.
Who can attend?
>Freshers with Python Programming knowledge.
>Proffessions with Programming background.
>Working/experienced who want to explore Python Environment.
Machine Learning Syllabus:
SECTION 1: Introduction
Why Machine Learning?
Problems Machine Learning Can Solve
Knowing Your Task and Knowing Your Data
Essential Libraries and Tools
Generalization, Overfitting, and Underfitting
SECTION 2: Supervised Learning Algorithms
Some Sample Datasets
Data and Engineering Features.
One-Hot-Encoding (Dummy Variables)
Naive Bayes Classifiers
Ensembles of Decision Trees
Kernelized Support Vector Machines
SECTION 3: Unsupervised Learning Algorithms and Preprocessing
Model Evaluation and Improvement
Cross-Validation in scikit-learn
Benefits of Cross-Validation
Stratified k-Fold Cross-Validation and Other Strategies