Certified Training on Data Science and Machine Learning using Python

Certified Training on Data Science and Machine Learning using Python

This is a specialization course which will help you to get a break into Data Science and Machine Learning with a project.

course at a glance

  • Date : 8 Oct - 5 Dec 2022
  • No. of Classes/ Sessions : 33
  • Total Hours : 66
  • Last Date of Registration : 6 Oct 2022
  • Class Schedule :
    • Saturday - 6:30 PM - 8:30 PM
    • Friday - 6:30 PM - 8:30 PM
  • venue : BASIS Institute of Technology & Management Limited BDBL Bhaban (3rd Floor - East), 12 Kawran Bazar, Dhaka -1215.

Price: TK. 18,000
(including VAT & TAX)

Data Science and Machine Learning using Python 

An Introduction to Python.
A Brief History of Python Versions
Installing Python

Local & Global Variables

Data Types
Dynamic Types
Python Reserved Words
Naming Conventions

Your First Python Program

How Python Code Gets Executed

Difference between Compiler and Interpreter

Basic Python Syntax

Basic Syntax Comments

Receiving Input

Type Conversion/Casting

Numeric Data Types

Formatted Float

Boolean Data Types



Formatted Strings

String Methods

Language Components
Arithmetic Operations

Operator Precedence

Math Functions


If Statements

Logical Operators

Letter Grade Program

Comparison/Relational/Conditional Operators

Leap Year Program

Assignment Operators

Ternary Operators

Weight Converter Program


While Loops

Break & Continue Statement

Sum of n Numbers Program

Building a Guessing Game

Building the Car Game

For Loops

For-While Comparison

For with Range Function

Nested Loops



Bubble Sort 

List Methods

Range Function in a list

2D Lists/Matrix



Set (Union/Intersection/Difference)





Keyword Parameters/Arguments

Default Parameter Value

xargs and xxargs

Return Statement

Lambda Function

Map and Filter function

List Comprehensions

Zip Function



Exception Handling      

Classes, Objects & Method

Object Oriented Programing (OOP)



Introducing Method

Default Constructors
Parameterized Constructor

Pass Statement

Class/Static Variable

Instance Variable

Intro to Inheritance
Single Inheritance
Hierarchical Inheritance
Multilevel Inheritance
Multiple Inheritance
Method Overloading
Method Overriding


Data Analysis using NumPy-Part 1

A brief introduction

Installation instructions.

NumPy arrays

Built-in methods

Array methods and attributes.

Indexing, slicing 

Data Analysis using NumPy-Part 2


Boolean masking

Arithmetic Operations

Universal Functions   

Exercises Overview

Exercises Solutions

Data Analysis using Pandas-Part 1

A brief introduction and installation instructions.

Pandas Introduction.

Pandas Data Structures - Series

Pandas Data Structures – DataFrame

Data Analysis using Pandas-Part 2

Hierarchical Indexing

Handling Missing Data

Data Wrangling - Combining, Merging, Joining, Group by

Useful Methods and Operations

Capstone Project- Data Analysis using Pandas

Project 1

Customer Purchases Data (Overview)

Customer Purchases Data (Solutions)

Project 2

Chicago Payroll Data (Overview)

Chicago Payroll Data (Solutions)

Basics of Statistics

Quantitative Analysis

Frequency Distribution

Data Presentation

Bar Graph vs Histogram

Methods of Center Measurement (Mean, Median, Mode)

Methods of Variability Measurement

Range, Variance, Standard deviation

Quartiles, Deciles, Percentiles, Coefficient of Variation

Five Number Summary/Box Plot

Data Visualization using Matplotlib-Part 1

Basic Plotting

Creating multiple plot on the same canvas

Matplotlib "Object Oriented" approach

Creating inset plot

Creating a figure and a set of subplots

Saving figures, Decorating figures

Project 1

Data Analysis & Visualization (Overview)

Data Analysis & Visualization (Solutions)

Data Visualization using Matplotlib-Pandas-Part 2

Pandas Built-in Data Visualization

Style Sheets

Area Plot, Bar/Barh Chart

Histogram, Line Chart

Scatter Plot, Box Plot

Hexagonal Bin Plot, Pie Chart                          

Kernel Density Estimation Plot (KDE)

Project 2

Data Analysis & Visualization (Overview)

Data Analysis & Visualization (Solutions)

Introduction to Machine Learning

Introduction to ML - What, Why

Machine Learning Applications

Supervised Learning

Unsupervised Learning

What is Machine Learning Model?

Training and Test Sets: Splitting Data

K Fold Cross Validation

Underfitting and Overfitting

Confusion Matrix (Precision, Recall, f1 score)

Linear Regression- K Nearest Neighbors

Linear Regression Theory

Linear Regression Algorithm

Linear Regression Pen & Paper Exercise

Multiple Linear Regression Theory

Multiple Linear Regression Algorithm

Logistic Regression Theory

Binary Logistic Regression Algorithm

Scikit-learn - Linear Regression Model

Training and Testing Data Separation

Simple Linear Regression Model - Hands-on

Multiple Linear Regression Model - Hands-on

Binary Logistic Regression Model - Hands-on


Insurance Data Project (Overview)

Insurance Data Project (Solutions)

Scikit-learn - K Nearest Neighbors

Feature Scaling Theory

Feature Scaling - Hands-on

K Nearest Neighbors Theory

K Nearest Neighbors Algorithm

K Nearest Neighbors Pen & Paper Exercise

K Nearest Neighbors - Hands-on

Principal Component Analysis (PCA) Theory

Principal Component Analysis (PCA) - Hands-on


K Nearest Neighbors (Overview)

K Nearest Neighbors (Solutions) 

Scikit-learn - Naive Bayes Classification & Principal Component Analysis (PCA)

Naive Bayes Classification Theory

Naive Bayes Classification Algorithm

Naive Bayes Classification Pen & Paper Exercise

Naive Bayes Classification Hands-on

K-Fold Cross Validation Implementation

How to save and load your trained Machine Learning

Label Encoding, One Hot Encoding 

Scikit-learn - Decision Tree

Decision Tree Theory

Decision Tree Algorithm

Entropy, IG

Decision Tree Pen & Paper Exercise

Decision Tree Hands-on

Scikit-learn - Random Forests

Ensemble Learning, Bagging, Random Forest, Boosting Theory

Random Forests Algorithm

Bootstrap, Bagging

Random Forests Hands-on


Decision Tree and Random Forests (Overview)

Decision Tree and Random Forests (Solutions)

Scikit-learn - Support Vector Machines (SVM)

Support Vector Machines Theory

Support Vector Machines Algorithm

Support Vector Machines (SVMs)

Support Vector Machines - Hands-on (SVMs)


Support Vector Machines (Overview)

Support Vector Machines (Solutions)

Scikit-learn - K Means Clustering

K Means Clustering Theory

K Means Clustering Algorithm

K Means Clustering Pen & Paper Exercise

K Means Clustering - Hands-on


K Means Clustering (Overview)

K Means Clustering (Solutions) 

NLTK – Spacy - Natural Language Processing (NLP)

What is NLP?

NLP Pipeline

Application of NLP

NLP- Hands-on: Segmentation, Tokenization, Stemming,

Lemmatization, POS Tagging,

Punctuation, Regex, Stop_words

Project: Email-Ham or Spam

Tensorflow - Deep Learning-Artificial Neural Network (ANN)

What is Neurons?

Biological Neural Network (BNN)

Artificial Neural Network (ANN)

The Architecture of Artificial Neural Network (ANN)

How do Artificial Neural Network (ANN) works

Backward Error Propagations 

Project: Handwritten digits classification using neural network


Certified Training on Data Science and Machine Learning using Python Certified Training on Data Science and Machine Learning using Python 65 Hrs

Tentative Class Start

8th October, 2022

Available Seat

10 / 20

who can join

People who have basic computer knowledge. Who want to learn about Data Science and Machine Learning.

Meet the Instructor