Levana Technologies

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.