Mastering Data Science and Machine Learning: A Comprehensive Full Stack Boot camp
Learn Python, Excel,Deep Learning, Power BI, SQL, Artificial Intelligence,Business Statistics, Capstone Projects & more!
Python,Full Stack Development,Machine Learning,Deep Learning
Lectures -28
Resources -6
Duration -13.5 hours
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Course Description
Welcome to the Full Stack Data Science & Machine Learning BootCamp Course, the only course you need to learn Foundation skills and get into data science.
At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:
The course is taught by the lead instructor at the PwC, India's leading in-person programming bootcamp.
In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.
This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.
The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.
To date, I’ve taught over 10000+ students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.
You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.
We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.
The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.
In the curriculum, we cover a large number of important data science and machine learning topics, such as:
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 Application.
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 own models.
By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. We’ll be covering all of these Python programming concepts:
PYTHON -
Data Types and Variables
String Manipulation
Functions
Objects
Lists, Tuples and Dictionaries
Loops and Iterators
Conditionals and Control Flow
Generator Functions
Context Managers and Name Scoping
Error Handling
Power BI -
What is Power BI and why you should be using it.
To import CSV and Excel files into Power BI Desktop.
How to use Merge Queries to fetch data from other queries.
How to create relationships between the different tables of the data model.
All about DAX including using the COUTROWS, CALCULATE, and SAMEPERIODLASTYEAR functions.
All about using the card visual to create summary information.
How to use other visuals such as clustered column charts, maps, and trend graphs.
How to use Slicers to filter your reports.
How to use themes to format your reports quickly and consistently.
How to edit the interactions between your visualizations and filter at visualization, page, and report level.
By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer.
Sign up today, and look forward to:
178+ HD Video Lectures
30+ Code Challenges and Exercises
Fully Fledged Data Science and Machine Learning Projects
Programming Resources and Cheatsheets
Our best selling 12 Rules to Learn to Code eBook
$12,000+ data science & machine learning bootcamp course materials and curriculum
Goals
Welcome to the Full Stack Data Science & Machine Learning BootCamp Course, the only course you need to learn Foundation skills and get into data science.
At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:
The course is taught by the lead instructor at the PwC, India's leading in-person programming bootcamp.
In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.
This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.
The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.
To date, I’ve taught over 10000+ students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.
You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.
We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.
The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.
In the curriculum, we cover a large number of important data science and machine learning topics, such as:
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 Application.
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 own models.
By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. We’ll be covering all of these Python programming concepts:
PYTHON -
Data Types and Variables
String Manipulation
Functions
Objects
Lists, Tuples and Dictionaries
Loops and Iterators
Conditionals and Control Flow
Generator Functions
Context Managers and Name Scoping
Error Handling
Power BI -
What is Power BI and why you should be using it.
To import CSV and Excel files into Power BI Desktop.
How to use Merge Queries to fetch data from other queries.
How to create relationships between the different tables of the data model.
All about DAX including using the COUTROWS, CALCULATE, and SAMEPERIODLASTYEAR functions.
All about using the card visual to create summary information.
How to use other visuals such as clustered column charts, maps, and trend graphs.
How to use Slicers to filter your reports.
How to use themes to format your reports quickly and consistently.
How to edit the interactions between your visualizations and filter at visualization, page, and report level.
By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer.
Sign up today, and look forward to:
178+ HD Video Lectures
30+ Code Challenges and Exercises
Fully Fledged Data Science and Machine Learning Projects
Programming Resources and Cheatsheets
Our best selling 12 Rules to Learn to Code eBook
$12,000+ data science & machine learning bootcamp course materials and curriculum
Prerequisites
Welcome to the Full Stack Data Science & Machine Learning BootCamp Course, the only course you need to learn Foundation skills and get into data science.
At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:
The course is taught by the lead instructor at the PwC, India's leading in-person programming bootcamp.
In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.
This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.
The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.
To date, I’ve taught over 10000+ students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.
You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.
We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.
The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.
In the curriculum, we cover a large number of important data science and machine learning topics, such as:
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 Application.
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 own models.
By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. We’ll be covering all of these Python programming concepts:
PYTHON -
Data Types and Variables
String Manipulation
Functions
Objects
Lists, Tuples and Dictionaries
Loops and Iterators
Conditionals and Control Flow
Generator Functions
Context Managers and Name Scoping
Error Handling
Power BI -
What is Power BI and why you should be using it.
To import CSV and Excel files into Power BI Desktop.
How to use Merge Queries to fetch data from other queries.
How to create relationships between the different tables of the data model.
All about DAX including using the COUTROWS, CALCULATE, and SAMEPERIODLASTYEAR functions.
All about using the card visual to create summary information.
How to use other visuals such as clustered column charts, maps, and trend graphs.
How to use Slicers to filter your reports.
How to use themes to format your reports quickly and consistently.
How to edit the interactions between your visualizations and filter at visualization, page, and report level.
By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer.
Sign up today, and look forward to:
178+ HD Video Lectures
30+ Code Challenges and Exercises
Fully Fledged Data Science and Machine Learning Projects
Programming Resources and Cheatsheets
Our best selling 12 Rules to Learn to Code eBook
$12,000+ data science & machine learning bootcamp course materials and curriculum
Curriculum
Check out the detailed breakdown of what’s inside the course
Python Fundamentals: Introduction to Basics for Beginners
3 Lectures
- Python Data Structures and String Manipulation: A Comprehensive Guide 51:51 51:51
- Python Functions Mastery: Lambda, Recursion, and Implementation Techniques 50:47 50:47
- Python for Data Analysis: Libraries, Exploratory Data Analysis, and Descriptive Analysis 44:38 44:38
Data Analysis with Business Statistics: Techniques and Applications
3 Lectures
Machine Learning Fundamentals: Concepts, Algorithms, and Applications
4 Lectures
Flight Fare Prediction: Machine Learning Capstone Project
3 Lectures
Mushroom Classification: Machine Learning Capstone Project
2 Lectures
Nursery School Application Classification: Machine Learning Capstone Project
2 Lectures
Toxic Comments Classification: Machine Learning Capstone Project
3 Lectures
Structured Query Language (SQL)
4 Lectures
Microsoft Power BI (Business Intelligence Tool)
4 Lectures
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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