Machine Learning & Statistics

Machine Learning

-->Machine Learning

Machine Learning Introduction
ML Fundamentals
ML Common Use Cases
Understanding Supervised and Unsupervised Learning Techniques

Similarity Metrics
Distance Measure Types: Euclidean, Cosine Measures
Creating predictive models
Understanding K-Means Clustering
Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
Case study
Implementing Association rule mining
Case study
Understanding Process flow of Supervised Learning Techniques
Decision Tree Classifier
How to build Decision trees
Case study
Random Forest Classifier
What is Random Forests
Features of Random Forest
Out of Box Error Estimate and Variable Importance
Case study
Naive Bayes Classifier
Case study
Project Discussion
Problem Statement and Analysis
Various approaches to solving a Data Science Problem
Pros and Cons of different approaches and algorithms
Linear Regression
Case study
Logistic Regression
Case study
Text Mining
Case study
Sentimental Analysis
Case study



What is Statistics?
Descriptive Statistics
Central Tendency Measures
The Story of Average
Dispersion Measures
Data Distributions
Central Limit Theorem
What is Sampling
Why Sampling
Sampling Methods
Inferential Statistics
What is Hypothesis testing
Confidence Level
Degrees of freedom
what is pValue
Chi-Square test
What is ANOVA
Correlation vs Regression
Uses of Correlation and Regression

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