Build RealWorld Machine Learning Projects With Python
Learn to create Classical Machine learning Learning Algorithms in Python
Python,Machine Learning,Big Data,Data Science and AI ML,Development,
Lectures -5
Duration -1.5 hours
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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.
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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
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Instructor Details
AKHIL VYDYULA
Hello, I'm Akhil, an Associate Consultant at Atos India with a focus on the Advisory Consulting practice, specializing in Data and Analytics. My professional journey has led me through various facets of data analysis and modeling, particularly in the BFSI sector, where I've had the privilege of overseeing the full lifecycle of development and execution.
My skill set encompasses a wide range of data-related tasks, including data wrangling, feature engineering, algorithm development, model training, and implementation. I thrive on leveraging data mining techniques such as statistical analysis, hypothesis testing, regression analysis, as well as both unsupervised and supervised machine learning processes to extract meaningful insights and drive data-informed decisions. I'm particularly passionate about risk identification through decision models, and I've honed my expertise in Machine Learning Algorithms, Data/Text Mining techniques, and Data Visualization to effectively address these challenges.
Currently, I'm immersed in an exciting Amazon cloud project that involves end-to-end development of ETL processing. In this role, I craft ETL processing code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, execute scripts using EMR services, and load consolidated data into Postgres SQL (RDS/Redshift) on a full, incremental, and live basis. To streamline this process, I've automated it by creating jobs in Step functions, which trigger EMR instances to run scripts in a specific order and send notifications upon execution status changes. The scheduling of these Step functions is achieved through event bridge rules.
Additionally, I've worked extensively with AWS Glue, using it to replicate source data from on-premises systems to raw-layer S3 buckets via AWS DMS services. One of my key strengths lies in my ability to understand the nuances of data and apply the right transformations to convert data from multiple tables into key-value pairs. Furthermore, I've optimized the performance of stored procedures in Postgres SQL to execute second-level transformations by efficiently joining multiple tables and loading the data into final tables.
I'm passionate about harnessing the power of data to drive actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, feel free to connect with me. Let's explore the endless possibilities that data analytics has to offer!
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