Practical Machine Learning Bootcamp With Real World Projects
A comprehensive course of 25+ video content hours and Downloadable files.
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:
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 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
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 51:51 51:51
-
Python Mastery: Harnessing the Power of Lambda, Recursion, and Functions 50:47 50:47
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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
![Tutorialspoint](/market/public/assets/images/loader.gif)
Deep Learning Demystified: An Introduction to the Basics of Deep Learning
6 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Time Series Insights: An Introduction to the Basics of Time Series Analysis
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Flight Fare Prediction Project-1: Predicting and Analyzing Flight Ticket Prices
4 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Mushroom Classification Project-2: Exploratory Data Analysis for Insightful
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Nursery School Application Classification Project-3: Regression Analysis
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Toxic Comments Classification Project-4 : Identifying and Analyzing Toxic
5 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
UK Road Accident Timeseries Analysis: Exploratory Data Analysis for Forecasting
3 Lectures
![Tutorialspoint](/market/public/assets/images/loader.gif)
Instructor Details
![AKHIL VYDYULA](https://www.tutorialspoint.com/assets/profiles/562262/profile/200_3498386-1698323497.jpg)
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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