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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
Why Python?
Installing scikit-learn
Essential Libraries and Tools
Generalization, Overfitting, and Underfitting

SECTION 2: Supervised Learning Algorithms
Some Sample Datasets
Linear Models
Data and Engineering Features.
Categorical Variables
One-Hot-Encoding (Dummy Variables)
k-Nearest Neighbors
Naive Bayes Classifiers
Decision Trees
Ensembles of Decision Trees
Kernelized Support Vector Machines

SECTION 3: Unsupervised Learning Algorithms and Preprocessing
k-Means Clustering
Model Evaluation and Improvement
Cross-Validation in scikit-learn
Benefits of Cross-Validation
Stratified k-Fold Cross-Validation and Other Strategies