Machine Learning in Python - Nov 2020 - nsarrows
nsarrows
Sign In
Sign In
Reset Password
Register
  • Home
  • Our Trainings
  • Testimonials
  • Blogs
  • Reach Us
Free
Machine Learning in Python – Nov 2020

Description

This is what you will be learning in the course:

  • Introduction to Python
  • Inferential Statistics
    • Probability
    • Statistics (Central Tendency, Spread of Data, Data Distributions, ANOVA, CHI SQUARE Test)
    • Univariate Analysis
    • Bivariate Analysis
    • Hypothesis Testing (t-test, z-test, p-value)
    • Quiz
  • Introduction to Machine Learning
    • Scikit Learn Package
    • Supervised and Unsupervised concepts
  • Linear Regression
    • Understanding the concept Linear Regression
    • Maths behind Linear Regression
    • Lasso Regression and Ridge Regression
    • Case Study 1 (Theory + Practicals + Reporting)
    • Case Study 2 (Theory + Practicals + Reporting)
    • Quiz
  • Logistic Regression
    • Understanding the concept of Logistic Regression
    • Maths behind Logistic Regression
    • AUC and ROC Curves, Confusion Matrix (Accuracy, Sensitivity and Specificity, Precision and Recall)
    • Cutoff Methods in Logistic Regression (Min Distance method, KS Method, Lift Method, F1 Beta Method)
    • Case Study 1 (Theory + Practicals + Reporting)
    • Case Study 2 (Theory + Practicals + Reporting)
    • Quiz
  • Capstone Project 1 for Certification
  • Clustering
    • Hierarchical Clustering (Maths + Case Study + Practicals + Reporting)
    • K-Means Clustering (Maths + Case Study + Practicals + Reporting)
    • DB Scan (Maths + Case Study + Practicals + Reporting)
    • Quiz
  • Decision Trees
    • Maths behind decision trees – Information Gain, Remainder,  Loss
    • C 4.5 (Maths + Case Study + Practicals + Reporting)
    • Random Forest (Maths + Case Study + Practicals + Reporting)
    • Quiz
  • Time Series
    • Understanding Time Series and its concepts
    • Exponential Smoothening 1 (Maths + Case Study + Practicals + Reporting)
    • Exponential Smoothening 2 (Maths + Case Study + Practicals + Reporting)
    • Exponential Smoothening 3 (Maths + Case Study + Practicals + Reporting)
    • Test for Stationarity
    • ARIMA (Maths + Case Study + Practicals + Reporting)
    • Quiz
  • Introduction to Boosting, Bagging and Cross Validation
  • Dimensionality Reduction
    • PCA (Principal Component Analysis)
    • LDA (Linear Discriminant Analysis)
  • Ensemble Techniques
  • Master Quiz for Certification
  • Capstone Project 2 for Certification
Take This Course

Students

Related Courses

Natural Language Processing and Chat Bots
Deep Learning and Computer Vision
Machine Learning in Python
Machine Learning in R
Academy Theme © 2021
  • Home
  • Our Courses
  • Testimonials
  • Blogs
  • Privacy Policy
  • Terms and Conditions
  • Reach Us