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