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Practical Machine Learning Bootcamp With Real World Projects

person icon AKHIL VYDYULA


Practical Machine Learning Bootcamp With Real World Projects

A comprehensive course of 25+ video content hours and Downloadable files.

updated on icon Updated on Jun, 2024

language icon Language - English

person icon AKHIL VYDYULA

English [CC]

category icon Machine Learning,Development

Lectures -38

Resources -47

Duration -20.5 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:


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

Data Cleaning and Preprocessing


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


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

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

  1. 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.

  2. 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.


  • 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


  • A background in engineering/science/Maths/Stats to understand the theory and the techniques used.

  • Good grasp of mathematics

Practical Machine Learning Bootcamp With Real World Projects


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

Python Primer: A Beginner's Journey into Python's Fundamentals
3 Lectures
  • play icon Python Essentials: Exploring Data Structures and String Operations 51:51 51:51
  • play icon Python Mastery: Harnessing the Power of Lambda, Recursion, and Functions 50:47 50:47
  • play icon 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



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