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Levana Technologies

Embedded Linux, Python, IoT & Machine Learning Training

Artificial INTELLIGENCE, Machine Learning and Data Science Workshop with Python

Course duration: 5 full days

Course Outline:

Module 1: Python Basics

  • Python Introduction
  • Using the Interpreter
  • Python Scripting
  • Working with Variables in Python
  • Numeric Operations in Python
  • Python Compound Statements
  • Python String Types
  • Python's Tuples
  • Python's Lists
  • Python Dictionaries
  • Creating Python Functions
  • Classes and Objects
  • Modules and Packages

Module 2: Data Science, Machine Learning & Deep Learning

  • Introduction to Artificial Intelligence and Machine Learning 
  • Applications of Machine Learning
  • Machine learning examples
  • Setting up Anaconda & Python Notebooks.
  • Working on notebooks for Data Science
  • Intro to Data Science
  • Working with arrays & matrix (using NumPy)
  • Working with Series & DataFrames
    • Accessing/Importing and Exporting Data
    • Data Manipulation - Cleaning-Munging
  • Data Visualization
    • Univariate Analysis (Distribution of data & Graphical Analysis)
    • Bivariate Analysis( Cross Tabs, Distributions & Relationships, Graphical Analysis}
    • Creating Graphs:
      • Bar plot
      • Pie plot
      • Count plot
      • Line chart
      • Histogram
      • Boxplot 
      • Scatter
      • Density
      • Violine Plot
      • Swarmplot
      • Distplot
      • Pair plot
      • Heatmap
  • Statistics Overview
    • Concepts of linear algebra 
    • Euclidean and Non-Euclidean geometry
    • Introduction to Calculus 
    • Probability and statistics
    • Distributions, CDF, PDF
    • Mean, Median, Mode
    • Standard Deviation, quartiles, percentiles
    • Variable Relationships & Estimation
    • Hypothesis Testing
  • Predictive Models
  • Machine Learning
  • Supervised Learning
    • Linear Regression, Multiple Regression, Polynomial Regression
    • Logistic Regression
    • Decision Trees
    • Ensemble Learning - Bagging, Boosting, Ada Boost, XGBoost
    • Random Forest
    • Naive Bayes
    • Kernel Learning
      • Support Vector Machines
      • Principal Component Analysis
      • Ridge Regression
      • Spectral Clustering
      • Time Series Forecasting - ARIMA
  • Unsupervised Learning: Segmentation
    • What is the segmentation & the Role of ML in Segmentation?
    • Clustering algorithms 
    • Concept of Distance and related math background
    • K-Means Clustering
    • Hierarchical Clustering
  • Dealing with Real-World Data
    • Experimental Design
    • Model Evaluation, Improvements & Performance Metrics
    • Data Split Practices 
    • Cross-Validation 
    • K-Fold Validation 
    • Confusion Matrix 
    • ROC Curves 
    • Mean Absolute/Square Errors & R-Square
    • Ensemble Learning & Model Stacking 
  • Natural Language Processing 
    • What is NLP & How to solve NLP problems?
    • NLP Feature Engineering & Modelling 
    • How to process any raw data file. 
    • Build models for solving practical real-world problems.
  • Deep Learning - Artificial Neural Networks (ANN)
    • The motivation for Neural Networks and Its Applications
    • Perceptron and Single Layer Neural Network, and Hand Calculations
    • Learning In a Multi-Layered Neural Net: BackPropagation and Conjugant Gradient Techniques
    • Introducing & Using Tensorflow
    • Neural Networks for Regression
    • Neural Networks for Classification
    • Interpretation of Outputs and Fine-tune the models with hyper-parameters
    • Validating ANN models
  • End to End ML Implementation and Use Case specific discussions.