S P L A S H
Machine Learning Training in Pallikaranai, Chennai
Course Content
Module 1 - Introduction to Data Science
Lets understand - What is Data Science?
Concepts of Machine Learning
Deep Learning VS Machine Learninig
Finally.. Artificial Intelligence(AI)
Module 2 – Introduction to Python
Why Python for Data Science
Creating Python Environment - Windows/Ubuntu/MacOS
Python Repositary - pip vs Conda
IDE - Pupyter Notebook Overview
Module 3 – Programming Fundamentals in Python
Python Basic Data types
List
Tuples
Dictionaries
Python Set
Indixing & Slicing
Selection by position & Labels
If Statements
Loops and Nested loops
Creating function in Python
Module 4 – Python Package for Data Science
Pandas
Numpy
MatPlot
Sci-Kit learn
SciPy
Module 5 – How to Load Machine Learning Data in Python
Importing data from CSV/TSV files
Exporting data to CSV files
Python Package - Pickle
Saving Python Objects
Loading data from Python Objects
Module 6 – Data Manipulation in Pandas
Selecting rows/ range of observations
Rounding / Absolute numbers
Selecting Columns / Attributes
Merging Data in Pandas
Data munging techniques
Module 7 – Data pre-processing
Missing Value analysis
What is NA / NaN / NULL values
Data Imputation
Mode
Data Normalization
Module 8 – Statistics Inference
Central Tendency
Mean
Median
Mode
Statistical Data Dispersion analysis
Range
Data Variance
Standard Deviation
Data Skewness
Outlier analysis
What is Outlier
Outlier influence in central tendency
Detecting Outliers
Seaborn - boxplot - box whisker plot
Using Inter Quartile Range(IQR)
Using Z-Score
How to Treat Outliers
What is data Transformation
Log Data Transformation
Winsorization Transformation
Drop out / Not to Drop out
Interpreting Correlation
Bivariate analysis
Pearson Correlation
Strength of the association
Direction of the relationship
Module 9 – Error Metric for Machine Learning Models
Regression
MAE
MSE
RMSE
MAPE
Classification
Confusion Matrix
Precision
Recall - Sensitivity
Specificity
F1-Score
Module 10 – Supervised Machine Learning
Liner Regression
When to use Liner Regression
Univariate Prediction
Live Project : Prediction
Multivariate analysis
Logistic regression
Binary Classification
Model evaluation
Probability of success / failure
Live Project : Anomaly Detection
Module 11 – UnSupervised Machine Learning
Understanding Unsupervised
What is Data Clustring analysis
KMeans clustring
Finding the centroid
Evaluate model with Test Data
Live Project : Customer spend analysis
Hierarchical Clustering
Agglomerative hierarchical clustering
Divisive hierarchical clustering
Live Project : Customer spend analysis
Kmeans VS Hierarchical - Result analysis
Module 12 – Classification algorithms in Machine Learning
K - Nearest Neighbour
Naive Bayes Classifier
Decision Tree
Support Vector Machines
Live Project : News Article Classification
Random Forest
Live Project : Twitter data analysis