Data Science – [ Machine Learning] using R / Python and Visualization using Tableau
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Detailed Topic Description:
- Descriptive Statistics Introduction to the course Descriptive Statistics Probability Distributions
- Types of data
- Measures of Central Tendency
- Measures of Variance
- Probability Rules
- Probability Distributions: Normal Distribution/ Binomial Distribution/Poison Distribution
- Estimations and Proportions
- Inferential Statistics Inferential Statistics through hypothesis tests Permutation & Randomization Test
- Hypothesis Testing Basics
- Error Types
- Hypothesis test for one sample population mean
- Hypothesis test for two sample population mean
- Paired T test
- Test for Variance / correlation
- Confidence Intervals
- Chi-sq test
- Analysis of variance
- One way Analysis of Variance
- Two way Analysis of Variance
- Basics of R Programming
- How to install R , R studio
- R Data Types , variables and operators
- R Data Frame , Basic functions in R
- Subletting, merging, recoding , aggregating , ordering , binding data in R
- User Defined functions
- Packages in R
- Apply(), Lapply(),Sapply(),tapply() R
- Missing Value Imputation in R
- Removing Outliers from data
- Machine Learning: Introduction and Concepts Differentiating algorithmic and model based frameworks Regression: Ordinary Least Squares in R
- Difference between Supervised and Unsupervised Learning
- Visualization techniques
- To formulate simple and multiple regression models
- To give an account of the principle of least squares
- To carry out tests of linear hypothesis
- To perform validation of a regression model
- To perform Cross Validation / Stepwise Regression
- To select the important explanatory variables
- To use R for analyzing real data sets
- To be able to interpret the results in practical examples.
- Binary Logistic Regression in R
- The log odds ratio Transformation
- Logistic models and Logit models
- Implementation of BLR in R
- Interpretation of results
- ROC curve
- Confidence Matrix
- K nearest Neighbors Regression & Classification in R
- KNN algorithm
- Distance Measure Methods
- Maximum Vote Concept
- KNN Regression in R
- KNN Classification in R
8 . Decision Tree and Random Forest in R
- Classification Trees
- Regression Trees
- Regularization and pruning
- Ensemble Models
- Bagging / Boosting / Out of Bag Error
- Random Forest and Decision Tree implementation in R
- Support Vector Machine
- Construction of SVM
- Support Vector and Hyperplanes
- Kernel Trick
- Hard vs Soft Margin SVM
- Implementation and Result Interpretation in R
- Naïve Bayes
- Conditional Probability
- Bayes Theorem
- Implementation and Result Interpretation in R
- Introduction To Artificial Neural Network
- Working of ANN
- Similarity between ANN and biological Neural System
- Single and Multilayer networks
- Unsupervised Machine Learning
- Intra cluster and inter cluster analysis
- Hierarchical Clustering
- K means Clustering
- Dimension Reduction Technique
- Principle Component Analysis
- Principle Components Regression
- Association Rule Mining
- Market Basket Analysis
- Understanding Support, confidence, lift
- Implementation and interpretation of Market Basket Analysis in R
- Introduction and Setting Up Your Integrated Analysis Environment
- Python Shell
- Custom environment settings
- Jupyter Notebooks
- Script editor
- Packages: NumPy, SciPy, scikit-learn, Pandas, Matplotlib, Seaborn, etc.
- Using Python to Control and Document Your Data Science Processes
- Python Essentials
- Data types and objects
- Loading packages, namespaces
- Reading and writing data
- Simple plotting
- Control flow
- Debugging
- Code profiling
- Accessing and Preparing Data
- Loading from CSV files
- Accessing SQL databases
- Cleansing Data with Python
- Stripping out extraneous information
- Normalizing data
- Formatting data
- Numerical Analysis, Data Exploration, and Data Visualization with NumPy Arrays, Matplotlib, and Seaborn
- NumPy Essentials
- The NumPy array
- N-dimensional array operations and manipulations
- Memory mapped files
- Data Visualization
- 2D plotting with Matplotlib
- Advanced data visualization with Seaborn
19 . V. Exploring Data with Pandas
- Searching for Gold in a Pile of Pyrite
- Data manipulation with Pandas
- Statistical analysis with Pandas
- Time series analysis with Pandas
- Machine Learning with scikit-learn
- Predicting the Future Can Be Good for Business
- Input: 2D, samples, and features
- Estimator, predictor, transformer interfaces
- Pre-processing data
- Regression
- Classification
- Model selection
- Data Visualization using Tableau
For Further Details and Enrollment mail to admin@vibrantcomputers.com