Data Science and Machine Learning Zero to Job Ready
MD JASIM UDDIN

MD JASIM UDDIN

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Data Science and Machine Learning Zero to Job Ready (4th Batch)

Online: TK 9999

Start Date : 2021-09-25  
End Date : 2022-01-25

Total Class : 30   Total Hours: 60

Location : 102/1 Shukrabad, Mirpur Road, Dhanmondi, Dhaka-1207

Monday : 3:00 PM - 5:00 PM

Thursday : 3:00 PM - 5:00 PM

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Course Description

Python is a general-purpose programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. Unlike any other Python tutorial, this course focuses on Python specifically for data science. In our Intro to Python class, you will learn about powerful ways to store and manipulate data as well as cool data science tools to start your own analyses

Data Science and Machine Learning

This course will enable you to gain the skills and knowledge that you need to successfully carry-out real-world data science and machine learning projects.

The first part of the course covers data analysis and visualization. You will be working on real datasets using Python’s Numpy, Pandas, Matplotlib and Seaborn libraries.

The second part of the course focuses on machine learning. We will be covering both supervised and unsupervised learning. We will be working on case studies from a wide range of verticals including finance, heath-care, real estate, sales, and marketing. Some of the algorithms that will be discussed include Linear Regression, Logistic Regression, Support Vector Machines (SVM), and K-means clustering. This course is the foundation for Deep Learning courses in this specialization.

Course Content

Course Details

Becoming a Data Science Engineer puts you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond. Data Science will impact all segments of daily life by 2025, with applications in a wide range of industries such as healthcare, transportation, insurance, transport and logistics and even customer service. The need for Data Science specialists exists in just about every field as companies seek to give computers the ability to think, learn and adapt.

 

Total course duration 60 hrs

Course Introduction

1

Introduction of Machine Learning and its application in Day to Day life

2

Course overview and Dashboard description

Python Core

1

Introduction to python and comparison with other programming language

2

Installation of Anaconda Distribution and other python IDE

3

Python Objects, Numbers & Booleans, Strings, Container objects, Mutability of objects

4

Operators -Arithmetic, Bitwise, comparison and Assignment operators, Operators Precedence and associativity

5

Conditions(If else, if-elif)

6

Loops(While ,For)

7

Break and Continue statements

8

Range functions

String Objects and Collections

1

String Object Basics

2

String Methods

3

Splitting and Joining Strings

4

String format functions

5

List Object Basics

6

List Methods

7

List as Stack and Queues

8

List Comprehensions

Tuples, Set, Dictionaries & Functions

1

Tuples, Sets, Dictionary Object basics, Dictionary Object methods, Dictionary View Objects

2

Functions Basics, Parameter passing, Iterators

3

Generator functions

4

Lambda functions

5

Map, Reduce & filter functions

OOPS Concepts &Working with Files

1

OOPS Basic Concepts

2

Creating Classes and Objects

3

Inheritance & Multiple Inheritance

4

Working with files

5

Reading and Writing files

6

Buflered read and BuPered write

7

Other File methods

Modules ,Exception Handling & Database Programming

1

Using Standard Module

2

Creating new modules

3

Exceptions Handling with Try-except

4

Creating ,Inserting and Retrieving Table

5

Updating and Deleting the data

Visualization

1

Matplotlib

2

Seaborn

3

Plotly

4

Cuflinks

Rest API

1

Flask Introduction

2

Flask Application

3

Open link Flask

4

App Routing Flask

5

URL Building Flask

6

HTTP Methods Flask

7

Templates Flask

8

Django end to end

Database

1

Mongo DB

2

SQL lite

3

Python SQL

Python project

1

Web crawlers for image data sentiment analysis and product review sentiment analysis

2

Integration with web portal

3

Integration with rest API ,web portal and mongo db on Azure

4

Deployment on web portal on Azure

5

Text mining

6

Social media data churn

Exploratory Data Analysis

1

Feature Engineering and Feature Selection

2

Building Tuning and Deploying Models

3

Analyzing Bike Sharing Trends

4

Analyzing Movie Reviews Sentiment

5

Customer Segmentation and Elective Cross Selling

6

Analyzing Wine Types and Quality

7

Analyzing Music Trends and Recommendations

8

Forecasting Stock and Commodity Prices

Machine Learning -1

1

Introduction

2

Supervised, Unsupervised, Semi-supervised & Reinforcement

3

Train, Test & Validation Split

4

Performance

5

Over fitting & Under fitting

6

OLS

7

Linear Regression

8

Assumptions

9

R-square & adjusted R-square

10

Intro to Scikit learn

11

Training Methodology

12

Hands on Linear Regression

13

Ridge Regression

          14

Logistics Regression

15

Precision Recall

16

ROC-curve

17

F-Score

Machine Learning -2

1

Decision Tree

2

Cross Validation

3

Bias vs Variance

4

Ensemble Approach

5

Bagging & Boosting

6

Random Forest

7

Variable Importance

Machine Learning -3

1

XG Boost

2

Hands on XG Boost

3

K Nearest Neighbor

4

Lazy learners

5

Curse of Dimensionality

6

KNN Issues

7

Hierarchical Clustering

8

K-Means

9

Performance Measurement

10

Principal Component Analysis

11

Dimensionality Reduction

12

Factor Analysis

Machine Learning Project

1

Healthcare analytics prediction of medicines based on FIT BIT Band

2

Revenue forecasting for startups

3

Prediction of order cancellation at the time of ordering inventories

4

Anamoly detection in inventory packaged material

5

Fault detection in wafers based on sensor data

6

Demand forecasting for FMCG product

7

Threat identification in security system

8

Defect detection in vehicle engine

9

Food price forecasting with zomato dataset

Deployment

1

Deployment of all the project In cloud foundry , AWS ,AZURE and google cloud platform

2

Expose API to web browser and mobile application

3

Retraining approach of Machine learning model

4

Devops infrastructure for machine learning model

5

Database integration and scheduling of machine learning model and retraining

6

Custom machine learning training approach

7

AUTO ML

8

Discussion on infra cost and Data volume

9

Prediction based on Streaming data

 

                              Best of Luck