Introduction to Machine learning with Python and R


Python and R are the two most popular programming languages used by data Scientists. Learning both these languages will help anyone interested in machine learning or creating complex data visualizations.


Introduction to Data Science

  • Growing importance of Data Sciences
  • Importance of Machine Learning and AI
  • Objectives of the course and how to be a practical data scientist.


  • Probability and Statistics Probabilistic
    • Calculation & Sampling Conditional Probability, Bayesian Statistics
  • Linear Algebra
    • Scalars and Vectors
    • Matrices
    • Eigen Values and Eigen Vectors,
    • Linear Regression, Logistic regression, Analysis of Variance, Case Study on Regression, Distance Functions
  • Descriptive Statistics
    • Mean, Median, Median and Quartile, Variance, Skewness and Kurtosis
    • Binomial, Poisson and Normal Distributions Correlation and Covariance
  • Inferential Statistics, Hypothesis Testing and Confidence Interval
  • Optimization Techniques
    • Gradient Descent Algorithm
    • Stochastic Gradient Descent Algorithm
    • Testing Convexity
  • Dimensionality Reduction
  • SVD and PCA

Programming Languages

  • Programming Concepts
    • Python
      • Python 3.7 Overview
      • Environment Setup : Pycharm, Jupyter
      • Basic Syntax
      • Variable Types
      • Loops
      • Data Structures : Lists, Dictionaries, Tuples
      • Functions
      • Libraries and Packages : NumPy, Pandas, Matplotlib and Seaborn
      • File Handling
      • Exception Handling
    • R
      • Introduction to R
      • Language Constructs
      • Data structures
      • Data frames
      • Packages
      • Data Visualization in R: plotting using ggplot lgraph
      • Data Visualization Case Study
      • Data Sourcing and Analysis in R

Machine Learning & AI

  • Introduction to Machine Learning Tools and Landscape for ML
  • Supervised Learning
    • Tree based Algorithms
    • Support Vector machines
    • Naïve Bayes
    • Random Forest
    • Gradient Boosting
    • K Nearest Neighbors
  • Unsupervised Learning
    • K Means Clustering
    • Hierarchical Clustering
  • Artificial Neural Networks
    • Introduction to ANN
    • Tensor Flow and Keras
    • Back Propagation Algorithm
    • Activation Functions
    • Regularization
    • Dropouts

Case Study Scenarios Covered

  • Programming exercises in Python & R
  • Data Visualization in R and Python
  • Case Study on Tree Based Algorithms
  • Case Study on Regression
  • Case Study on Data Analysis
  • Case Study on Dimensionality Reduction
  • Case Study on Naive Bayes
  • Case Study on KNN and SVM
  • Case Study on Unsupervised Learning
  • Case Study on Gradient Descent Algorithm
  • Case Study on Stochastic Gradient Descent
  • Case Study on ANN

Advanced Course Overview

Capstone Project

DEMO CLASSES - 06:30 PM PST. Demo classes will help you to get a good feel of our courses.



Every Wednesday 06:30 - 09:30 PM PST
Sunday 05:00 - 08:00 PM PST
Length - 12 Weeks


Class Location

1837 156th Ave NE Suite A303,
Bellevue, WA 98007.

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