Machine Learning Using Python

Machine learning using python is a comprehensive training course that helps you take a deep dive into machine learning basics and understand how Python programming language integrates to the core

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Course Highlights

  • • Instructor-led classroom and online training modes
  • • Best-in-class training curriculum
  • • Beginner to expert level training
  • • Hands-on programming practice
  • • Python projects for hands-on exposures
  • • Practice activities
  • • Unlimited access - online or offline
  • • Flexible guaranteed to run schedules
  • • Self-paced learning
Course Description

Machine learning using python is a comprehensive training course that helps you take a deep dive into machine learning basics and understand how Python programming language integrates to the core. The machine learning with python course is focused on delivering best-in-class learning on statistical modelling, regression and clustering algorithms and have an in-depth understanding of the interoperability between python and machine learning and the magical applications it can create.

Brillica services is a prominent leader in offering you an opportunity to transform your theoretical knowledge on machine learning and python, into the much preferred and job-oriented practical skill through multiple projects included in machine learning using python training and certification course. Our certified and seasoned instructors work to build a solid foundation on developing algorithms, creating functions, exception handling, data analysis etc.

Our diverse training delivery modes of machine learning using python course, offered in custom schedules, help corporate and individual learners find a best-fit learning solution mapping their career and training goals.

Course objectives

Upon completion of the course, the learners would learn:

  • Using Python for Data Analysis and Machine Learning
  • Implementing Machine Learning algorithms
  • Using Matplotlib for Python Plotting
  • Learning the implication of Seaborn for statistical plots
  • Comprehending interactive dynamic visualizations
  • Using SciKit-Learn for Machine Learning Tasks
  • AI Debugging & Testing
  • Working with K-Means Clustering
  • Basics of Logistic and Linear Regression
  • Practically working on Random Forest and Decision Trees
  • Natural Language Processing and functioning on Spam Filters
  • Working with Neural Networks
  • Supporting Vector Machines
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An individual aspiring for in-depth knowledge in Machine learning using power-packed programming is expected to possess:

  • Basic programming knowledge is necessary
  • Good understanding of mathematical concepts
Course Content
  1. Basics of Python – Syntax, Conditional/Loop statements
  2. Data Structures – List, Tuple, Dictionary
  3. Creating Functions and Classes in Python
  4. Exception Handling
  5. Using Modules in python
  6. Database Connectivity – SQL (SQLite), No-SQL (Firebase – Realtime Database)
  7. Introduction to HTML, CSS and Web scrapping
  8. File Handling. Library – Tkinter, BeautifulSoup, Requests, SQLite, Firebase


  1. Calculator
  2. Student Management System with SQLite Database Connectivity
  3. Restaurant Management System with No-SQL Database Connectivity
  4. Extract the details of all the quotation and their authors
  5. Book Management System – 4 parts:
  • Data Scraped from an online website
  • Publishing your data online using threading and modules
  • Visualizing data using Tkinter GUI

a) Data Analysis using CSV, SQL.

  1. Creating/Converting your data into Panda’s Data Frame, Series
  2. Filtering and Sorting.
  3.  Applying, Grouping and Statistical Analysis

b) Data Visualization.

c) Regular Expression for Data Analysis.

d) Time Series Analysis using ARIMA Model.


Framework/Libraries – Panda, Matplotlib, Seaborn


Projects : –

a) Time Series Analysis –

  1. New Year resolution trend analysis
  2. [Competition] – Jet Rail Investment
  3. [Competition] – Web Traffic Forecasting.

b) Data Analysis and Visualization –

  • Titanic Data set
  • Olympics Data set
  • Telecom Churn Analysis
  • [Competition] – San Francisco Salary insights
  • [Competition] – 911 Calls Emergency analysis

a) Supervised Learning – Regression, Classification.

b) Unsupervised Learning – Classification.

c) AI Debugging & Testing.

Framework/Libraries – Scikit Learn.


  1.  Logistic Regression.
  2.  KNN (K-Nearest Neighbors), Decision Tree.
  3.  SVM (Support Vector Machine), Hyper Parameter Tuning, Cross-Validation.
  4.  Linear Regression.
  5.  Ensemble Learning (Bagging & Boosting).
  6.  Dimensionality Reduction (Principal Component Analysis),
  7. g) Recommendation System.


Projects : –

  1. [Competition] – Titanic Passenger Survival Prediction.
  2.  Country’s GDP Estimate, Estimating sales for the company based on different factors.
  3.  Employee Hiring, Wine Quality Classification.
  4.  Iris Flower Classification, Handwriting classification.
  5.  Employee Hiring, Wine Quality Classification.
  6.  Iris Flower Classification, Handwriting classification.
  7.  Cancer Prediction.
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