Build RealWorld Machine Learning Projects With Python

4.4 ★★★★ ★

Build RealWorld Machine Learning Projects With Python

Name: Build RealWorld Machine Learning Projects With Python - Online Course
Rating: 4.4 (213 reviews)
Author: AKHIL VYDYULA

Learn to create Classical Machine learning Learning Algorithms in Python

updated on icon Updated on Jun, 2025

language icon Language - English

person icon AKHIL VYDYULA

English [CC]

category icon Development ,Data Science,Machine Learning

Lectures -5

Duration -1.5 hours

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4.4 ★★★★ ★

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Course Description

In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku).

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.

More and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.

In This Course, We Are Going To Work On 2 Real World Projects Listed Below:

Project-1: Toxic Comment Classification
Project-2: UK_Road_Accident_Timeseries_Forecasting

The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career

Note (Read This): This Course Is Worth Of Your Time And Money, Enroll Now Free

Goals

Learn to perform Classification and Regression modeling
Master Machine Learning and use it on the job
Use Seaborn to create beautiful statistical plots with Python.
Get set-up quickly with the Anaconda data science stack environment.

Prerequisites

There is no specific prerequisite to learn machine learning. But you need to be from engineering/science/Maths/Stats background to understand the theory and the techniques used. You need to be good in mathematics. If you are not, still you can machine learning, but you will face difficulty when solving complex real world problems. Many say you need to know Linear algebra, Calculus etc. but I have never learnt it, yet I am able to work on machine learning.

Build RealWorld Machine Learning Projects With Python

Curriculum

Check out the detailed breakdown of what’s inside the course

Project-1 : UK_Road_Accident_Timeseries_Forecasting

2 Lectures

UK_Road_Accident_Timeseries_Forecasting_EDA 32:20 32:20
Forecast UK Accident rates based on Number of Casualties on SARIMA,FbP,LSTM's 28:54 28:54

Project-2 : Toxic_Comments_Classification

3 Lectures

Instructor Details

AKHIL VYDYULA

Data Scientist | Data & Analytics Specialist | Entrepreneur

Hello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.

My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.

I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.

Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.

Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.

I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!

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