Full Stack Data Science Machine Learning BootCamp Course

Full Stack Data Science & Machine Learning BootCamp Course, available at $54.99, has an average rating of 4.6, with 69 lectures, based on 25 reviews, and has 2442 subscribers.

You will learn about Build a portfolio of data science projects to apply for jobs in the industry Learn how to create pie, bar, line, area, histogram, scatter, regression, and combo charts Create your own neural networks and understand how to use them to perform deep learning Understand and apply data visualisation techniques to explore large datasets Use data science algorithms to analyse data in real life projects such as Mushroom classification and image recognition Understand how to use the latest tools in data science, including Tensorflow, Matplotlib, Numpy and many more This course is ideal for individuals who are If you want to take ONE COURSE and learn everything you need to know about data science and machine learning then take this course. or If you are a seasoned programmer, take this course to get up to speed quickly with the workflow of a data scientist. or If you want to learn how to build machine learning algorithms such as deep learning and neural networks. or The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills It is particularly useful for If you want to take ONE COURSE and learn everything you need to know about data science and machine learning then take this course. or If you are a seasoned programmer, take this course to get up to speed quickly with the workflow of a data scientist. or If you want to learn how to build machine learning algorithms such as deep learning and neural networks. or The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills.

Enroll now: Full Stack Data Science & Machine Learning BootCamp Course

Summary

Title: Full Stack Data Science & Machine Learning BootCamp Course

Price: $54.99

Average Rating: 4.6

Number of Lectures: 69

Number of Published Lectures: 69

Number of Curriculum Items: 69

Number of Published Curriculum Objects: 69

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Build a portfolio of data science projects to apply for jobs in the industry

Learn how to create pie, bar, line, area, histogram, scatter, regression, and combo charts

Create your own neural networks and understand how to use them to perform deep learning

Understand and apply data visualisation techniques to explore large datasets

Use data science algorithms to analyse data in real life projects such as Mushroom classification and image recognition

Understand how to use the latest tools in data science, including Tensorflow, Matplotlib, Numpy and many more

Who Should Attend

If you want to take ONE COURSE and learn everything you need to know about data science and machine learning then take this course.

If you are a seasoned programmer, take this course to get up to speed quickly with the workflow of a data scientist.

If you want to learn how to build machine learning algorithms such as deep learning and neural networks.

The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

Target Audiences

If you want to take ONE COURSE and learn everything you need to know about data science and machine learning then take this course.

If you are a seasoned programmer, take this course to get up to speed quickly with the workflow of a data scientist.

If you want to learn how to build machine learning algorithms such as deep learning and neural networks.

The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

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 comprehensivedata 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 livesby getting jobs in the industry or starting their own tech startup.

You’ll save yourself over $12,000by 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

Course Curriculum

Chapter 1: Introduction to the Full Stack Data Science Course

Lecture 1: Introduction to the Full Stack Data Science Course

Chapter 2: Python Fundamentals: Introduction to Basics for Beginners

Lecture 1: Python Data Structures and String Manipulation: A Comprehensive Guide

Lecture 2: Python Functions Mastery: Lambda, Recursion, and Implementation Techniques

Lecture 3: Python for Data Analysis: Libraries, Exploratory Data Analysis, and Descriptive

Chapter 3: Data Analysis with Business Statistics: Techniques and Applications

Lecture 1: Introduction to statistics and Measures of central tendencies

Lecture 2: The Central Limit Theorem (CLT): Understanding Sampling and Distribution

Lecture 3: Exploring Distributions and Correlations: Statistical Analysis in Python

Lecture 4: PDF & CDF and Hypothesis Testing

Lecture 5: Time Series Analysis & Forecasting

Lecture 6: Probability Theory and Statistical Analysis

Lecture 7: Capstone Project – UK Road Accident Analysis : Part – 1

Lecture 8: Capstone Project – UK Road Accident : Part -2

Chapter 4: Machine Learning Fundamentals: Concepts, Algorithms, and Applications

Lecture 1: Logistic Regression in Machine Learning: Theory, Implementation, and Application

Lecture 2: Word Embedding Techniques in Machine Learning: Bag-of-Words, TF-IDF, Word2Vec

Lecture 3: Text Cleaning and Preprocessing for Machine Learning: Analyzing Amazon Reviews

Lecture 4: Linear Regression in Machine Learning: Theory, Implementation, and Applications

Lecture 5: Decision Tree Classifier and Regression in Machine Learning: Theory

Lecture 6: MACHINE LEARNING – Geometric Intuition of Ensembles Models and Flask Project

Lecture 7: MACHINE LEARNING – Data Analysis on Loan Approval Status

Lecture 8: MACHINE LEARNING – Unsupervised Learning Algorithms K means Cluster Techniques

Chapter 5: Flight Fare Prediction: Machine Learning Capstone Project

Lecture 1: Flight Fare Prediction: Machine Learning Capstone Project

Lecture 2: Feature Engineering and Applying Classical ML Models

Lecture 3: Deploy the Model with Flask Framework

Chapter 6: Mushroom Classification: Machine Learning Capstone Project

Lecture 1: Mushroom Classification: Exploratory Data Analysis

Lecture 2: Mushroom Classification: Benchmark Model Building and Evaluation

Chapter 7: Nursery School Application Classification: Machine Learning Capstone Project

Lecture 1: Project_3_NurserySchool_Application_Classification

Lecture 2: Logistic Regression, SVM, Decision Tree Models & Evaluation Metrics

Chapter 8: ML Capstone Project 4 : Toxic_Comments_Classification

Lecture 1: Project_4_Toxic_Comments_Classification

Lecture 2: Tokenized Sequences Visualization in Natural Language Processing (NLP)

Lecture 3: Model Refinement – Optimize NB,SVM,LR with Feature Weight

Chapter 9: ML Capstone Project 5 : UK_Road_Accident_Timeseries_Forecasting

Lecture 1: Project_5_UK_Road_Accident_Timeseries_Forecasting_EDA

Lecture 2: Forecast UK Accident rates based on Number of Casualties on SARIMA,FbP,LSTMs

Chapter 10: Structured Query Language (SQL)

Lecture 1: Introduction to SQL – SQL Syntax and Download MySQL

Lecture 2: RDBMS – Data Integrity, Database Normalization

Lecture 3: Data Definition Language (DDL)

Lecture 4: Data Manipulation language (DML)

Lecture 5: Data Control Languages (DCL) and Domain Constraints

Lecture 6: Filtering Data and SET Operators in SQL

Lecture 7: Conditional Expressions in SQL

Lecture 8: Grouping Data

Lecture 9: Joining Multiple Tables (JOINS)

Lecture 10: SQL RANK Functions

Lecture 11: SQL Triggers and Stored Procedures

Lecture 12: SQL Capstone Project 1 : Data Analytics on Movie Reviews in SQL

Chapter 11: DEEP LEARNING

Lecture 1: DEEP LEARNING – Introduction to Neural Networks and Basics of MLP, BACKPROP

Lecture 2: DEEP LEARNING – In Depth Understanding of RNN and LSTM with Examples

Lecture 3: DEEP LEARNING – Intuition Behind the Computer Vision and CNN Algorithm

Lecture 4: DEEP LEARNING – Convolutional Neural Networks with Pizza and CIFAR Projects

Lecture 5: DEEP LEARNING – Practical Examples on Transfer Learning for Vgg16 Model

Lecture 6: DEEP LEARNING – Web Based Flask Framework for Wild Animal Recognition with CNN

Chapter 12: Microsoft Excel

Lecture 1: Introduction to Excel Workbook

Lecture 2: Hands on Excel Cells and Ranges

Lecture 3: Basic Formulae – Logical Operators

Lecture 4: Excel – Lookup and Reference Formulae

Lecture 5: Excel – Logical Formulae

Lecture 6: Text and Statistical Formulae

Lecture 7: Excel – Date & Time Formulae

Lecture 8: Excel – Sorting & Filtering

Lecture 9: Dynamic Charts With Examples

Lecture 10: Derive Insights with Pivot Tables

Chapter 13: Microsoft Power BI (Business Intelligence Tool)

Lecture 1: Installation Power BI Desktop and Applications of Power BI

Lecture 2: Understand the Concepts of Maps using Power BI

Lecture 3: Power BI – Tables and Matrix

Lecture 4: Different Types of Power BI Slicers

Lecture 5: Introduction to Power Query

Lecture 6: Hands on with Power Query Operations

Lecture 7: Manipulations with Power Query Operations

Lecture 8: Build a Super Store Sales Dashboard

Lecture 9: BI Capstone Project – Sales and Production Analysis

Instructors

Akhil Vydyula
Data Scientist | Data & Analytics Specialist | Entrepreneur

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 3 votes

4 stars: 4 votes

5 stars: 17 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!