20+ End-To-End Machine Learning Projects & Deployment 2021

What you'll learn

• Master Machine Learning With Python

• Learn Through More Than 20+ Projects and Assignments

• Learn How to Deploy Machine Learning Models

• Practical Hands-On Data Science Projects Mastery

• Build And Deploy On Flask, Heroku, Streamlit, AWS,Google Cloud, Microsoft Azure

• Create Robust Machine Learning Models

• Learn Different Machine Learning Algorithms such as Linear And Logistic regression, Naive Bayes,KNN,SVM,K-means, etc.

• Deal With Data Imbalance (Upsampling/Downsampling/SMOTE)

• Beginner To Advance

• Gain Confidence In Performing Exploratory Data Analysis (EDA)

• Choose The Right Machine Learning Model For Your Problem Statement

• Access To Exclusive Community To Learn With Others And Answer Your Queries

• Learn The Necessary Statistics

• Master Data Analysis

• Introduction to Jupyter Notebook 16:26

  • 1: Lecture resources 01:00
  • 2: Getting started with Anaconda Preview 04:11
  • 3: Introduction To Jupyter Notebook: Code Vs Markdown Vs Raw Preview 05:00
  • 4: Introduction To Jupyter Notebook: Working With Text Preview 02:51
  • 5: Jupyter Notebook: (Saving & Exporting Notebook) Preview 03:24

• Introduction to Google Colab 14:37

  • 6: Lecture Resources 01:00
  • 7: Introduction To Google Colab: Overview 04:48
  • 8: Introduction To Google Colab: Working With Text 04:49
  • 9: Introduction To Google Colab: Saving & Exporting Notebook 03:00
  • 10: Loading Dataset In Colab 01:00

• Breast Cancer detection using SVM and KNN 1:27:38

  • 11: Lecture Resources 01:00
  • 12: Introduction 02:57
  • 13: Summary Of Dataset 06:37
  • 14: Data Preprocessing 21:38
  • 15: Exploratory Data Analysis (EDA) 19:58
  • 16: Model Building 35:28

• Bank Note Analysis 1:38:05

  • 17: Lecture Resources 01:00
  • 18: Introduction 02:33
  • 19: Bank Note Analysis-EDA 20:22
  • 20: Bank Note Analysis-Getting Our Dataset Ready For Model Building 20:52
  • 21: Model Building 1 19:57
  • 22: Model Building 2 18:20
  • 23: Model Building 3 15:01

• Predicting Compressive Strength of Concrete -( Compare performance of 18 models) 1:57:07

  • 24: Lecture Resources 01:00
  • 25: Introduction 02:49
  • 26: Data Preprocessing 09:48
  • 27: Exploratory Data Analysis (EDA) 1 20:38
  • 28: Exploratory Data Analysis (EDA) 2 12:42
  • 29: Feature Engineering 07:15
  • 30: Model Building 1 36:35
  • 31: Model Building 2 26:20

• Credit Card Fraud Detection - (Dealing with Data Imbalance) 1:01:26

  • 32: Introduction 02:36
  • 33: Exploratory Data Analysis (EDA) 27:13
  • 34: Model Building 1 13:49
  • 35: Model Building 2 17:48

• Stock Market Clustering Using K-Means Algorithm 55:50

  • 36: Lecture Resources 01:00
  • 37: Introduction 02:30
  • 38: Data Extraction And Analysis 20:29
  • 39: Model Building 31:51

• BigMart Sales Prediction 1:52:15

  • 40: Lecture Resources 01:00
  • 41: Introduction 02:56
  • 42: Exploratory Data Analysis (EDA) 49:48
  • 43: Feature Engineering, Selection And Transformation 44:36
  • 44: Model Building 13:55

• Amazon Employee Access Challenge 1:17:21

  • 45: Lecture Resources Preview 01:00
  • 46: Introduction 02:07
  • 47: Exploratory Data Analysis (EDA) 34:39
  • 48: Model Building 1 19:10
  • 49: Model Building 2 20:25

• Project Assignment 03:00

  • 50: Assignment 03:00

• Streamlit Project 4:41:05

  • 51: Lecture Resources 01:00
  • 52: Demo Preview 17:04
  • 53: Introduction to Streamlit 1 21:27
  • 54: Introduction to Streamlit 2 48:38
  • 55: Introduction to Streamlit 3 24:42
  • 56: Building Your First Streamlit App 54:45
  • 57: Building Your First Streamlit App-Advance 1 38:42
  • 58: Building Your First Streamlit App-Advance 2 20:06
  • 59: Building Your First Streamlit App-Advance 3 52:41
  • 60: Project Assignment 02:00

• Flask Deployment 2:34:35

  • 61: Lecture Resources 03:00
  • 62: Flask Introduction Preview 03:09
  • 63: Introduction to Dataset 07:52
  • 64: Exploratory Data Analysis (EDA) 47:04
  • 65: Model Building 23:00
  • 66: Introduction to Flask 11:23
  • 67: Creating The Necessary Folders 23:49
  • 68: Creating Folder Contents 16:20
  • 69: Final Deployment 17:58
  • 70: Project Assignment 01:00

• Heroku Deployment 1:59:07

  • 71: Lecture Resources 01:00
  • 72: Heroku Introduction Preview 05:42
  • 73: Exploratory Data Analysis (EDA) 16:16
  • 74: Feature Engineering 14:56
  • 75: Further Data Preparation 20:10
  • 76: Model Building And Hyperparameter Tuning 20:55
  • 77: Heroku Deployment 1 28:31
  • 78: Heroku Deployment 2 10:37
  • 79: Project Assignment 01:00

• Google Cloud Deployment 40:48

  • 80: Lecture Resources 01:00
  • 81: Google Cloud Introduction Preview 01:24
  • 82: GCP Deployment Lesson 38:24

• Amazon Web Service (AWS) Deployment 1:22:13

  • 83: Lecture Resources 01:00
  • 84: AWS Introduction Preview 02:49
  • 85: Dataset 02:14
  • 86: App.py File 16:08
  • 87: PART 1 45:47
  • 88: PART 2 14:15

• Microsoft Azure Deployment 24:10

  • 89: Lecture Resources 01:00
  • 90: Azure Deployment 23:10

• Final Project 07:00

  • 91: Introduction 01:00
  • 92: Final Machine Learning Project 06:00

Interested in the field of Data Science and Machine Learning?

Interested in Building Data Science and Machine Learning Projects and Deploy on Platforms such as AWS, Google Cloud, Microsoft Azure, Heroku, Flask, Streamlit?

Interested in learning it the practical way?

Then this course is for you!!

This course has been practically and carefully designed by industry experts to offer the best way of learning Data Science and Machine Learning the practical way with hands-on projects throughout the course.

This course will help you learn complex Data Science concepts and machine learning algorithms the practical way for easier understanding.

We will walk you through step-by-step on each topic explaining each line of code for your understanding.

There is going to be a lot of fun, exciting, and robust projects to better understand each concept under each topic.

We have structured the course in this way:

1. Machine Learning: Concepts | Lab Session | Exercises

2. Hands-on projects on each topic

3. Projects Deployment Tutorial

• Streamlit project tutorial

• Flask deployment project tutorial

• Heroku deployment project tutorial

• Google cloud deployment project tutorial

• AWS deployment project tutorial

• Microsoft Azure deployment project tutorial

This course aims to help beginners, as well as intermediate data science enthusiasts, learn both the concepts while practicing on the projects to gain a better understanding of the course.

• Anyone interested in Data Science and Machine Learning.
• Anyone who wants to Build And Deploy machine learning models on Flask, Heroku, AWS,Google Cloud, Microsoft Azure
• Any student interested in a career in Data Science and Machine Learning
• Any beginner level interested to kick-start their career in Data Science and Machine Learning
• Any intermediate level learner who know the basics of python, statistics, machine Learning and want to learn more about it
• Anyone not that comfortable with coding but interested in Data Science and Machine Learning and want to easily understand the concepts
• Any data analysts who want to transition to Data Science and Machine Learning
• Any Business Analysts who want to transition to Data Science and Machine Learning
• Anyone not satisfied with their job and looking for a career transition to become a Data Scientist.
• Anyone who wants to leverage the power of data in his case scenario.
• Any curious mind

• This course is a beginner to advance level course with a step-by-step walkthrough and detail explanation of the concepts.

Currently there are no Q&As for this course. Be the first to ask a question.

Reed courses certificate of completion

Digital certificate - Included