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Hands on Python Fundamentals

person icon AKHIL VYDYULA

4.4

Hands on Python Fundamentals

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory.

updated on icon Updated on Jun, 2025

language icon Language - English

person icon AKHIL VYDYULA

English [CC]

category icon Development ,Data Science,Python

Lectures -1

Duration -51 mins

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4.4

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Course Description

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

A Road map connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.

Below are a few Applications of Machine Learning in Practical Real World

  1. Machine learning can help with the diagnosis of diseases. Many physicians use chatbots with speech recognition capabilities to discern patterns in symptoms. Real-world examples for medical diagnosis: Assisting in formulating a diagnosis or recommending a treatment option.
  2. Google Maps uses machine learning in combination with various data sources including aggregate location data, historical traffic patterns, local government data, and real-time feedback from users, to predict traffic.

Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% Prioritising it for development. So, In this course also you will able to learn the Basics of Python to Advanced State of Art Techniques of Deep Learning Models.

There are 4 different sections in this course for a complete understanding of all the concepts in Artificial Intelligence such as Python, Machine Learning, Deep Learning, and Time Series Analysis.

This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

PYTHON -

Data Structures, List, Tuples, Dictionary, Libraries, Functions, Operators etc

Data Cleaning and Preprocessing

MACHINE LEARNING -

Regression: Simple Linear Regression, SVR, Decision Tree, Random Forest,

Clustering: K-Means, Hierarchical Clustering Algorithms

Classification: Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Natural Language Processing: Bag-of-words model and algorithms for NLP

DEEP LEARNING -

Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long short term Memory, Vgg16, Transfer learning, Web Based Flask Applications.

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your models.

Who this course is for:

  • Is anyone interested in Machine Learning?
  • Students who have at least high school knowledge in maths and who want to start learning Machine Learning.
  • Any intermediate-level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science.
  • Any data analysts who want to level up in Machine Learning.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any people who want to create added value to their business by using powerful Machine Learning tools.


I hope you will enjoy this course. I will see you in the course.

Goals

  • Machine learning
  • Data Structures, List, Tuples, Dictionary, Libraries, Functions, Operators etc
  • Regression: Simple Linear Regression, SVR, Decision Tree, Random Forest,
  • Clustering: K-Means, Hierarchical Clustering Algorithms
  • Classification: Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Prerequisites

  • No
Hands on Python Fundamentals

Curriculum

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

Python - Data Structures (Lists, Tuple, Dictionary) and String Manipulations

1 Lectures
  • play icon Python - Data Structures (Lists, Tuple, Dictionary) And String Manipulations 51:51 51:51

Instructor Details

AKHIL VYDYULA

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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