Practical Machine Learning Bootcamp With Real World Projects
A comprehensive course of 25+ video content hours and Downloadable files.
Development ,Data Science,Machine Learning
Lectures -38
Resources -47
Duration -19.5 hours
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Course Description
Two professional Data Scientists have designed this course so that they can share knowledge and help you learn complex theory, algorithms, and coding libraries. It creates a road map connecting several crucial concepts in Machine Learning, teaches them, and introduces tools to perform them.
Machine Learning Bootcamp Course Overview
In this course, you will learn the Basics of Python along with crucial techniques of Deep Learning models. Python plays a major role in this training as 57% of data scientists and machine learning developers use it and 33% prioritize it for development.
You gain a complete understanding of all the concepts in Artificial Intelligence such as Python, Python for Data Science, Machine Learning, Deep Learning, and Time Series Analysis with the 4 different sections in this course.
It is structured in 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.
The course provides practical exercises based on real-life examples giving you hands-on practice building your own models.
Applications of Machine Learning in Practical Real World
Machine Learning assists in formulating a diagnosis or recommending a treatment option. Many physicians try to discern patterns in symptoms using chatbots with speech recognition capabilities.
Google Maps uses machine learning in combination with various data sources to predict traffic. This includes aggregate location data, historical traffic patterns, local government data, and real-time feedback from users.
Who this Course is for:
Students with a minimum high school knowledge in maths and passionate to learn Machine Learning.
Those who know the basics of Machine Learning and classical algorithms like linear regression or logistic regression.
Anyone who wishes to learn Machine Learning and apply it easily on datasets, even if they are not comfortable with coding.
Students who wish to start a career in Data Science.
Data analysts who wish to level up in Machine Learning.
Anyone who wishes to become a Data Scientist.
Anyone who wishes to use powerful Machine Learning tools.
Goals
Implement real-world ML projects with proof of concept
Master Python, Machine learning, Deep Learning, and Time series.
A solid grasp of ML for Data Scientists.
5 practical Data Science projects along with Python Notebooks
Prerequisites
A background in engineering/science/Maths/Stats to understand the theory and the techniques used.
Good grasp of mathematics.

Curriculum
Check out the detailed breakdown of what’s inside the course
Python Primer: A Beginner's Journey into Python's Fundamentals
3 Lectures
-
Python Essentials: Exploring Data Structures and String Operations 51:50 51:50
-
Python Mastery: Harnessing the Power of Lambda, Recursion, and Functions 50:47 50:47
-
Python for Data Analysis: Libraries, Exploratory Data Analysis, and Descriptive 44:38 44:38
Machine Learning Foundations: A Beginner's Guide to the Basics of ML
8 Lectures

Deep Learning Demystified: An Introduction to the Basics of Deep Learning
6 Lectures

Time Series Insights: An Introduction to the Basics of Time Series Analysis
3 Lectures

Flight Fare Prediction Project-1: Predicting and Analyzing Flight Ticket Prices
4 Lectures

Mushroom Classification Project-2: Exploratory Data Analysis for Insightful
3 Lectures

Nursery School Application Classification Project-3: Regression Analysis
3 Lectures

Toxic Comments Classification Project-4 : Identifying and Analyzing Toxic
5 Lectures

UK Road Accident Timeseries Analysis: Exploratory Data Analysis for Forecasting
3 Lectures

Instructor Details

AKHIL VYDYULA
Data Scientist | Data & Analytics Specialist | EntrepreneurHello, 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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