Real World Auto Machine Learning Bootcamp- Build 14 Projects

Real World Auto Machine Learning Bootcamp: Build 14 Projects, available at $59.99, has an average rating of 4.3, with 119 lectures, based on 30 reviews, and has 908 subscribers.

You will learn about Understand the full product workflow for the machine learning lifecycle. Write clean, maintainable and performant code Have a great intuition of many Auto Machine Learning models Master Machine Learning and use it on the job Learn to perform Classification and Regression modelling This course is ideal for individuals who are Beginners in machine learning It is particularly useful for Beginners in machine learning.

Enroll now: Real World Auto Machine Learning Bootcamp: Build 14 Projects

Summary

Title: Real World Auto Machine Learning Bootcamp: Build 14 Projects

Price: $59.99

Average Rating: 4.3

Number of Lectures: 119

Number of Published Lectures: 112

Number of Curriculum Items: 119

Number of Published Curriculum Objects: 112

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the full product workflow for the machine learning lifecycle.

Write clean, maintainable and performant code

Have a great intuition of many Auto Machine Learning models

Master Machine Learning and use it on the job

Learn to perform Classification and Regression modelling

Who Should Attend

Beginners in machine learning

Target Audiences

Beginners in machine learning

Automated machine learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Applying traditional machine learning methods to real-world business problems is time-consuming, resource-intensive, and challenging. It requires experts in several disciplines, including data scientists – some of the most sought-after professionals in the job market right now.

Automated machine learning changes that, making it easier to build and use machine learning models in the real world by running systematic processes on raw data and selecting models that pull the most relevant information from the data – what is often referred to as “the signal in the noise.” Automated machine learning incorporates machine learning best practices from top-ranked data scientists to make data science more accessible across the organization.

“Data science is the transformation of data using mathematics and statistics into valuable insights, decisions, and products”

As data scienceevolves and gains new “instruments” over time, the core business goal remains focused on finding useful patterns and yielding valuable insights from data. Today, data science is employed across a broad range of industries and aids in various analytical problems. For example, in marketing, exploring customer age, gender, location, and behavior allows for making highly targeted campaigns, evaluating how much customers are prone to make a purchase or leave. In banking, finding outlying client actions aids in detecting fraud. In healthcare, analyzing patients’ medical records can show the probability of having diseases, etc.

The data science landscape encompasses multiple interconnected fields that leverage different techniques and tools.

There’s a difference between data mining and very popular machine learning. Still, machine learning is about creating algorithms to extract valuable insights, it’s heavily focused on continuous use in dynamically changing environments and emphasizes adjustments, retraining, and updating of algorithms based on previous experiences. The goal of machine learning is to constantly adapt to new data and discover new patterns or rules in it. Sometimes it can be realized without human guidance and explicit reprogramming.

Machine learning is the most dynamically developing field of data science today due to a number of recent theoretical and technological breakthroughs. They led to natural language processing, image recognition, or even the generation of new images, music, and texts by machines. Machine learning remains the main “instrument” of building artificial intelligence.

Machine Learning Workflow

Generally, the workflow follows these simple steps:

1. Collect data.Use your digital infrastructure and other sources to gather as many useful records as possible and unite them into a dataset.

2. Prepare data.Prepare your data to be processed in the best possible way. Data preprocessing and cleaning procedures can be quite sophisticated, but usually, they aim at filling the missing values and correcting other flaws in data, like different representations of the same values in a column (e.g. December 14, 2016and 12.14.2016won’t be treated the same by the algorithm).

3. Split data. Separate subsets of data to train a model and further evaluate how it performs against new data.

4. Train a model.Use a subset of historic data to let the algorithm recognize the patterns in it.

5. Test and validate a model.Evaluate the performance of a model using testing and validation subsets of historic data and understand how accurate the prediction is.

6. Deploy a model.Embed the tested model into your decision-making framework as a part of an analytics solution or let users leverage its capabilities (e.g. better target your product recommendations).

7. Iterate.Collect new data after using the model to incrementally improve it.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction To The Course

Lecture 2: Course Outline Video

Lecture 3: Course Bonuses: Cheat Sheets, Downloads, Mind maps, Guides.

Lecture 4: Udemy Course Feedback

Chapter 2: Project-1: Heart Attack Risk Predictor Using Auto ML

Lecture 1: Introduction

Lecture 2: Importing Libraries and Datasets

Lecture 3: Data Analysis

Lecture 4: Model Building Part 1

Lecture 5: Model Building Part 2

Lecture 6: Model building and Predictions using Auto ML (Eval ML)

Lecture 7: Download the project files

Chapter 3: Project-2: Credit card fraud detection

Lecture 1: Introduction to the Project

Lecture 2: Importing Libraries and DataSet

Lecture 3: Data Analysis

Lecture 4: Model Building using ML

Lecture 5: Model Building and Prediction using PyCaret(AutoML)

Lecture 6: Download the project files

Chapter 4: Project-3 : Flight Fare Prediction Using Auto SK Learn (Auto ML)

Lecture 1: Introduction to the Project.

Lecture 2: Importing Libraries and DataSet

Lecture 3: Data Analysis

Lecture 4: Feature Engineering 1

Lecture 5: Feature Engineering 2

Lecture 6: Feature Selection

Lecture 7: Model Building using ML

Lecture 8: Model Building and Prediction using Auto SK Learn

Lecture 9: Download the project files

Chapter 5: Project-4 : Petrol Price Forecasting Using Auto Keras

Lecture 1: Introduction to the Project

Lecture 2: Importing Libraries and Data Set

Lecture 3: Data Analysis and splitting of Data

Lecture 4: Data Preprocessing

Lecture 5: Model Building and Prediction using LSTM model

Lecture 6: Model Building and prediction using ARIMA and Auto Keras

Lecture 7: Download the project files

Chapter 6: Project-5 : Bank Customer Churn Prediction Using H2O Auto ML

Lecture 1: Introduction to the Project.

Lecture 2: 2a Importing Libraries and Data Set

Lecture 3: Data Analysis

Lecture 4: 4a Feature Engineering

Lecture 5: Model Building and Prediction using ANN

Lecture 6: Model Building and Prediction using H2O Auto ML(Auto ML)

Lecture 7: Download the project files

Chapter 7: Project-6 : Air Quality Index Predictor Using TPOT With End-To-End Deployment (A

Lecture 1: Introduction to the Project

Lecture 2: Importing Libraries and Data sets

Lecture 3: Data Analysis

Lecture 4: feature Engineering

Lecture 5: Model Building using ML- 1

Lecture 6: Model Building using ML- 2

Lecture 7: Model Building and Predictions using TPOT Library(AUto ML)

Lecture 8: Deployment of Model using Flask API

Lecture 9: Download the project files

Chapter 8: Project-7 : Rain Prediction Using ML models & PyCaret With Deployment (Auto ML)

Lecture 1: Introduction to the Project

Lecture 2: Importing Libraries and DataSet

Lecture 3: Data Analysis and Handling Missing Values- 1

Lecture 4: Data Analysis and Handling Missing Values- 2

Lecture 5: Feature Engineering

Lecture 6: Model Building using ML Algorithms

Lecture 7: Model Building and Prediction using PyCaret(AutoML)

Lecture 8: Using FLASK API

Lecture 9: Deploying model using Heroku

Lecture 10: Download the project files

Chapter 9: Project-8 : Pizza Price Prediction Using ML And EVALML(Auto ML)

Lecture 1: Introduction to the project

Lecture 2: Importing Libraries and DataSet

Lecture 3: Data Analysis

Lecture 4: Feature Engineering

Lecture 5: Model Building using ML models

Lecture 6: Model Building and Prediction using EVAL ML(Auto ML)

Lecture 7: Download the project files

Chapter 10: Project-9 : IPL Cricket Score Prediction Using TPOT (Auto ML)

Lecture 1: Introduction to the Project

Lecture 2: Importing Libraries and DataSet

Lecture 3: Data Analysis and Cleaning

Lecture 4: Data Preprocessing

Lecture 5: Model Building using ML Algorithms

Lecture 6: Model Building using TPOT Auto ML Library-1

Lecture 7: Model Building using TPOT Auto ML Library-2

Lecture 8: Download the project files

Chapter 11: Project-10 : Predicting Bike Rentals Count Using ML And H2O Auto ML

Lecture 1: Introduction to the Project

Lecture 2: Importing libraries and DataSet

Lecture 3: Data Analysis and Cleaning -1

Lecture 4: Data Analysis and Cleaning -2

Lecture 5: Data Preprocessing

Lecture 6: Splitting the Data

Lecture 7: Model Building and Prediction using ML

Lecture 8: Model Building and Prediction using H2O Auto ML Library

Lecture 9: Download the project files

Chapter 12: Project-11 : Concrete Compressive Strength Prediction Using Auto Keras (Auto ML)

Lecture 1: Introduction to the Project

Lecture 2: Importing Libraries and Data Set

Lecture 3: Data Analysis

Lecture 4: Feature Engineering

Lecture 5: Model Building and Prediction using Deep Learning

Instructors

Pianalytix ? 75,000+ Students Worldwide
Projects in Data Science, Machine Learning, Power BI, & More

Rating Distribution

1 stars: 0 votes

2 stars: 2 votes

3 stars: 2 votes

4 stars: 1 votes

5 stars: 25 votes

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