Search results for:
No result found! Try with different keywords!
Advertisement
Complete Data Science Mastery Bootcamp

Complete Data Science Mastery Bootcamp

Sep 27, 2021 - Feb 28, 2022

Complete Data Science Mastery Bootcamp

Time Mon Sep 27 2021 at 06:00 am to Mon Feb 28 2022 at 09:00 am

(add to calendar)

Online

Find Tickets
USD 286
Advertisement

Complete Data Science Mastery Bootcamp | Online Event | AllEvents.in Complete Data Science Mastery Bootcamp
COMPLETE DATA SCIENCE MASTERY BOOTCAMP

About this Event

Learn Data Science and Machine Learning from scratch doing 50+ real-time hands-on experience-based Projects, get hired, and have fun along the way with the most modern, up-to-date Data Science course (we use the latest version of Python, TensorFlow 2.0 and other libraries). We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere.


About Organization: Imbuedesk Educational Networking Solutions Private Limited, ISO 9001:2015 Certified, Start-Up India, CSI, MSME ,NSP Registered company is conducting a "COMPLETE DATA SCIENCE MASTERY BOOTCAMP" to upskill students and employees in DATA SCIENCE to help them advance in their careers.


A Complete Data Science with Deep Learning, Neural Networks, Machine Learning Models, NLP, Computer Vision models, Artificial Intelligence, Reinforcement Learning, REST APIs, Databases (SQL, MongoDB), Docker, GIT, AWS Integrations and lot more.

This comprehensive and project based bootcamp will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real-world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on GitHub, so that you can put them on your portfolio right away! We believe this bootcamp solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.


We cover a large number of important data science and machine learning topics, such as:

• Data Cleaning and Pre-Processing

• Data Exploration and Visualisation

• Linear Regression

• Multivariable Regression

• Optimisation Algorithms and Gradient Descent

• Naive Bayes Classification

• Descriptive Statistics and Probability Theory

• Neural Networks and Deep Learning

• Model Evaluation and Analysis

• Serving a TensorFlow Model

• NLP

• Neural Networks, Deep Learning models like ANNs, CNNs, RNNs, SSD, GANs

• Artificial Intelligence using Reinforcement Learning

• REST API Creation for ML Models, Deep Learning Models, Computer Vision Models

• Databases like SQL, MongoDB

• Integrating models using Docker, GitHub, AWS Cloud Platform


Python programming concepts:

• 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


Pandas:

• Section Overview

• Downloading Workbooks and Assignments

• Pandas Introduction

• Series, Data Frames and CSVs

• Data from URLs

• Describing Data with Pandas

• Selecting and Viewing Data with Pandas

• Selecting and Viewing Data with Pandas Part 2

• Manipulating Data

• Manipulating Data 2

• Manipulating Data 3

• Assignment: Pandas Practice

Numpy:

• NumPy Introduction

• Quick Note: Correction In Next Video

• NumPy Datatypes and Attributes

• Creating NumPy Arrays

• NumPy Random Seed

• Viewing Arrays and Matrices

• Manipulating Arrays

• Manipulating Arrays 2

• Standard Deviation and Variance

• Reshape and Transpose

• Dot Product vs Element Wise

• Exercise: Store Sales

• Comparison Operators

• Sorting Arrays

• Turn Images Into NumPy Arrays


Matplotlib:

• Section Overview

• Matplotlib Introduction

• Importing And Using Matplotlib

• Anatomy Of A Matplotlib Figure

• Scatter Plot And Bar Plot

• Histograms And Subplots

• Subplots Option 2

• Quick Tip: Data Visualizations

• Plotting From Pandas DataFrame

• Quick Note: Regular Expressions

• Plotting From Pandas DataFrame 2

• Plotting from Pandas DataFrame 3

• Plotting from Pandas DataFrame 4

• Plotting from Pandas DataFrame 5

• Plotting from Pandas DataFrame 6

• Plotting from Pandas DataFrame 7

• Customizing Your Plots

• Customizing Your Plots 2

• Saving And Sharing Your Plots


Machine Learning Classifier and Regressor Models:

• Section Overview

• Scikit-learn Introduction

• Refresher: What Is Machine Learning?

• Scikit-learn Cheat sheet

• Typical scikit-learn Workflow

• Debugging Warnings InJupyter

• Getting Your Data Ready: Splitting Your Data

• How to: Clean, Transform, Reduce

• Getting Your Data Ready: Convert Data To Numbers

• Getting Your Data Ready: Handling Missing Values With Pandas

• Extension: Feature Scaling

• Getting Your Data Ready: Handling Missing Values With Scikit-learn

• Choosing The Right Model For Your Data

• Choosing The Right Model For Your Data 2 (Regression)

• Decision Trees

• How ML Algorithms Work

• Choosing The Right Model For Your Data 3 (Classification)

• Fitting A Model To The Data

• Making Predictions With Our Model

• predict() vs predict_proba()

• Making Predictions With Our Model (Regression)

• Evaluating A Machine Learning Model (Score)

• Evaluating A Machine Learning Model 2 (Cross Validation)

• Evaluating A Classification Model 1 (Accuracy)

• Evaluating A Classification Model 2 (ROC Curve)

• Evaluating A Classification Model 3 (ROC Curve)

• Reading Extension: ROC Curve + AUC

• Evaluating A Classification Model 4 (Confusion Matrix)

• Evaluating A Classification Model 5 (Confusion Matrix)

• Evaluating A Classification Model 6 (Classification Report)

• Evaluating A Regression Model 1 (R2 Score)

• Evaluating A Regression Model 2 (MAE)

• Evaluating A Regression Model 3 (MSE)

• Machine Learning Model Evaluation

• Evaluating A Model With Cross Validation and Scoring Parameter

• Evaluating A Model With Scikit-learn Functions

• Improving A Machine Learning Model

• Tuning Hyperparameters

• Metric Comparison Improvement

• Correlation Analysis

• Saving And Loading A Model

• Putting It All Together

• Scikit-Learn Practice

Project: Supervised Learning (classification)

Project: Supervised Learning (Time Series Model)


Artificial Neural Networks (ANNs)

Project: Multiclass image classification with ANN

Project: Binary Data Classification with ANN


Convolutional Neural Networks (CNNs)

Project: Object Recognition in Images with CNN

Project: Binary Image Classification with CNN

Project: Digit Recognition with CNN

Project: Breast Cancer Detection with CNN

Project: Predicting the Bank Customer Satisfaction

Project: Credit Card Fraud Detection with CNN


Recurrent Neural Networks (RNNs)

Project: IMDB Review Classification with RNN - LSTM

Project: Multiclass Image Classification with RNN - LSTM

Project: Google Stock Price Prediction with RNN and LSTM


Transfer Learning


Natural Language Processing

• Basics of Natural Language Processing

Project: Movie Review Classification with NLTK


Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs:

• Learn by completing 26 advanced computer vision projects including Emotion, Age & Gender Classification, London Underground Sign Detection, Monkey Breed, Flowers, Fruits , Simpsons Characters and many more!

• Advanced Deep Learning Computer Vision Techniques such as Transfer Learning and using pre-trained models (VGG, MobileNet, InceptionV3, ResNet50) on ImageNet and re-create popular CNNs such as AlexNet, LeNet, VGG and U-Net.

• Understand how Neural Networks, Convolutional Neural Networks, R-CNNs , SSDs, YOLO & GANs with my easy to follow explanations

• Become familiar with other frameworks (PyTorch, Caffe, MXNET, CV APIs), Cloud GPUs and get an overview of the Computer Vision World

• How to use the Python library Keras to build complex Deep Learning Networks (using Tensorflow backend)

• How to do Neural Style Transfer, DeepDream and use GANs to Age Faces up to 60+

• How to create, label, annotate, train your own Image Datasets, perfect for University Projects and Start-ups

• How to use OpenCV with a FREE Optional course with almost 4 hours of video

• How to use CNNs like U-Net to perform Image Segmentation which is extremely useful in Medical Imaging application

• How to use TensorFlow's Object Detection API and Create A Custom Object Detector in YOLO

• Facial Recognition with VGGFace

• Use Cloud GPUs on PaperSpace for 100X Speed Increase vs CPU

• Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance


Artificial Intelligence: Reinforcement Learning

• The multi-armed bandit problem and the explore-exploit dilemma

• Ways to calculate means and moving averages and their relationship to stochastic gradient descent

• Markov Decision Processes (MDPs)

• Dynamic Programming

• Monte Carlo

• Temporal Difference (TD) Learning (Q-Learning and SARSA)

• Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm)

• How to use OpenAI Gym, with zero code changes

• Project: Apply Q-Learning to build a stock trading bot


Python REST APIs with Flask, Docker, MongoDB, and AWS DevOps:

• Understanding the Basics of the Python Flask Framework

• Understanding and Implementing a REST API

• Docker

• SQL, MongoDB

Project: Database as a Service Restful API

Project: Building a Restful API for similarity check using Natural Language Processing

Project: Building an Image Recognition Restful API using TensorFlow and Deep Learning

Project: Building a Restful API to Handle Bank Transactions

• Deploying Restful API into an AWS EC2 Instance


Requirements:

• No programming experience needed! we'll teach you everything you need to know.

• No statistics knowledge required! we’ll teach you everything you need to know.

• No calculus knowledge required! as long as you've done some high school maths, we'll take you step by step through the difficult parts.

• Also, no paid software required - all projects use free and open source software

• All you need is Mac or PC computer with access to the internet


Benefits:

1. 160+ Hours of Live Training (1.5 Hours a day)

2. 500+ Hours of Practice + Assignments to master from Basics to Advance

3. 5 Months Bootcamp

4. 2-3 Months Experienced based works (Internships/Part-time/Freelance)

5. Experience Letter + Certification from ImbueDesk ENS Pvt Ltd (ISO 9001:2015, MSME Registered, CSI Recognized, Start-up India recognized company)

6. 50+ real-time hands-on experience-based Projects to work on

7. A Complete Data Science with Deep Learning, Neural Networks, Machine Learning Models, NLP, Computer Vision models, Artificial Intelligence, Reinforcement Learning, REST APIs, Databases (SQL, MongoDB), Docker, GIT, AWS Integrations and lot more.

Timings:

6a.m. – 8a.m. Indian Standard Time (+5.30 Greenwich Time) (Weekdays)

or

3 p.m. – 5 p.m. Indian Standard Time (+5.30 Greenwich Time) (Weekdays)

or

7 p.m. – 9 p.m. Indian Standard Time (+5.30 Greenwich Time) (Weekdays)

or

6 a.m. – 10 a.m. Indian Standard Time (+5.30 Greenwich Time) (Weekends, Saturday + Sunday)

And

Doubt Clearing Sessions on every Sunday 8 p.m. – 10 p.m. + Community help during remaining days (from our experts and other student members through our discord channel)


Dates:

September 27th, 2021 Onwards (Weekdays batch Starts)

October 2nd, 2021 onwards (Weekend batch Starts)

Pricing:

INR 18150/- + 18% GST = INR 21,417/- per admit.

(Only 65 people per batch)




Also check out other Workshops in Online, Business Events in Online.

Tickets

Tickets for Complete Data Science Mastery Bootcamp can be booked here.

Get Tickets

Ticket Information Ticket Price
General Admission USD 286
Event Photos
Hosted by
About The Host: A Students Organization
Website Link: http://www.imbuedesk.com
Are you the host?
Advertisement
Event details from Report a problem ?

Event Information

Date & Time

Mon Sep 27 2021 at 06:00 am to Mon Feb 28 2022 at 09:00 am
Add to Calendar

Location

Online

Advertisement
Advertisement
Market Your Events Professionally!

Create an online presence of your events and make them easy to discover & share with the relevant audience.

Create Events