EBS Corp.


Regular price $5,199.00 Sale price $5,400.00
Tax included.

DURATION: 36 HOURS ONSITE TRAINING + 3 Month Subscription to Online Support after completion

Method: 1 on 1

The schedule is tentative; finalized after enrollment according to student/instructor availability. CALL TO SCHEDULE +1 (888) 251 6979


  • Introduction to Python programming
  • Data science jobs
  • Print Hello World
  • Azure Notebooks & Jupyter Notebooks –
  • Google Colab
  • Functions (Arguments and Return)
  • Python built-in Data types Concept of mutability and theory of different Data structures
  • Control flow statements: If, Elif and Else Definite and Indefinite loops: For and While loops
  • Writing user-defined functions in Python
  • Loops (For While) If else List/Dictionary
  • Nested Loops with if else List/Dictionary (JSON) Class Lambda Functions List Comprehension
  • File Handling Web Scraping Exception handling SQLite Python Classes
  • Python Read and write Text and CSV files with python List comprehensions and Lambda.
  • Classes and inheritance.
  • Automate the Boring Stuff
  • Matplotlib Numpy Pandas Scipy Python Lambdas Python Regular
  • Expressions Collection of powerful, open-source, tools needed to analyze data and to conduct data science.
  • Working with jupyter anaconda notebooks pandas numpy matplotlib git and many other tools.
  • Data Loading, Storage, and File Formats
  • Data Cleaning and Preparation
  • Data Wrangling: Join, Combine, and Reshape
  • Plotting and Visualization
  • Data Aggregation and Group Operations
  • Time Series
  • Machine learning and data mining techniques are used for in a simple example in Python.
  • Run machine learning models on data of choice
  • Supervised vs Unsupervised Learning
  • Regression vs Classification models
  • Categorical vs Continuous feature spaces
  • Python Scikit-learn Library
  • Modeling Fundamentals: Test-train split, Cross-validation (CV), Bias–variance tradeoff, Precision and Recall, Ensemble models
  • Interpreting Results of Regression and Classification Models
  • Built your own model and start with your own Data from Kaggle.
  • Select your project, download data, clean wrangle and massage your data and make it ready for analysis for Titanic, Iris, and other common Data sets.
  • Model DATA
  • Run Machine Learning Models and select the best model
  • Tweak Model parameters for Titanic Iris Dataset
  • Regression analysis K-Means Clustering Principal Component
  • Analysis Train/Test and cross-validation Bayesian Methods
  • Decision Trees and Random Forests Multivariate Regression
  • Multi-Level Models Support Vector Machines K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Understanding and Interpreting results of Regression and Logistic Regression using Google

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