Machine Learning Classification Bootcamp in Python from Udemy

What's inside

Learning objectives

Apply advanced machine learning models to perform sentiment analysis and classify customer reviews such as amazon alexa products reviews
Understand the theory and intuition behind several machine learning algorithms
Implement classification algorithms in scikit-learn for k-nearest neighbors, (svm), decision trees, random forest, naive bayes, and logistic regression
Build an e-mail spam classifier using naive bayes classification technique
Apply machine learning models to healthcare applications such as cancer and kyphosis diseases classification
Develop models to predict customer behavior towards targeted facebook ads
Classify data using k-nearest neighbors, support vector machines (svm), decision trees, random forest, naive bayes, and logistic regression
Build an in-store feature to predict customer's size using their features

Develop a fraud detection classifier using machine learning techniques
Master python seaborn library for statistical plots
Understand the difference between machine learning, deep learning and artificial intelligence
Perform feature engineering and clean your training and testing data to remove outliers
Master python and scikit-learn for data science and machine learning
Learn to use python matplotlib library for data plotting
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Apply advanced machine learning models to perform sentiment analysis and classify customer reviews such as amazon alexa products reviews
Understand the theory and intuition behind several machine learning algorithms
Implement classification algorithms in scikit-learn for k-nearest neighbors, (svm), decision trees, random forest, naive bayes, and logistic regression
Build an e-mail spam classifier using naive bayes classification technique
Apply machine learning models to healthcare applications such as cancer and kyphosis diseases classification
Develop models to predict customer behavior towards targeted facebook ads
Classify data using k-nearest neighbors, support vector machines (svm), decision trees, random forest, naive bayes, and logistic regression
Build an in-store feature to predict customer's size using their features
Develop a fraud detection classifier using machine learning techniques
Master python seaborn library for statistical plots
Understand the difference between machine learning, deep learning and artificial intelligence
Perform feature engineering and clean your training and testing data to remove outliers
Master python and scikit-learn for data science and machine learning
Learn to use python matplotlib library for data plotting
Show more
Show less

Syllabus

Introduction

Introduction and Welcome Message

Introduction and Welcome Message [Course Material Download]

EXTRA: Learning Paths

Updates on Udemy Reviews

Course Overview

Get the Materials

What is Machine Learning? The Big Picture

What is Machine Learning? The Big Picture Part #1

What is Machine Learning? The Big Picture Part #2

Installation & Setup [Optional][Skip if you are familiar with Jupyter Notebooks]

What is Anaconda and How to download it?

What are Jupyter Notebooks?

How to run a Jupyter Notebook?

Logistic Regression

Logistic Regression Introduction and Learning Outcomes

Logistic Regression Intuition

Confusion Matrix Overview

Logistic Regression - Project #1 - Project Overview

Logistic Regression - Project #1 - Loading Data

Logistic Regression - Project #1 - Visualization

Logistic Regression - Project #1 - Data Cleaning

Logistic Regression - Project #1 - Data Cleaning part 2

Logistic Regression - Project #1 - Training

Logistic Regression - Project #1 - Testing

Logistic Regression - Project #2 Overview

Logistic Regression - Project #2 - Importing data

Logistic Regression - Project #2 - Data visualization

Logistic Regression - Project #2 - Cleaning data

Logistic Regression - Project #2 - Training/Testing

Logistic Regression - Project #2 - Testing/Visualization

Support Vector Machines

Support Vector Machines Intro and Learning Outcomes

Support Vector Machines - Intuition

Support Vector Machines - Project #1 - Project Overview

Support Vector Machines - Project #1 - Importing data

Support Vector Machines - Project #1 - Data Visualization

Support Vector Machines - Project #1 - Training

Support Vector Machines - Project #1 - Testing

Support Vector Machines - Project #1 - Improvements 1

Support Vector Machines - Project #1 - Improvements 2

Project #2 Overview

Support Vector Machines - Project #2 - Data import and visualization

Support Vector Machines - Project #2 - Training and evaluating the model

Support Vector Machines - Project #2 - Improvements 1

Support Vector Machines - Project #2 - Improvements 2

K-Nearest Neighbors

K-Nearest Neighbors Intro and Learning Outcomes

K-Nearest Neighbors - Intuition

KNN - Project #1 - Project overview

KNN - Project #1 - Data import and cleaning

KNN - Project #1 - Training/Testing

KNN - Project #1 - Model visualization

KNN - Project #2 Overview

KNN - Project #2 - Data Visualization

KNN - Project #2 - Training

KNN - Project #2 - Evaluation

Decision Trees and Random Forest

Decision Trees and Random Forest Intro and Learning Outcomes

Decision Trees - Intuition

Random Forest - Intuition

Decision Trees & Random Forest - Project #1 - Project Overview

Decision Trees & Random Forest - Project #1 - Importing data

Decision Trees & Random Forest - Project #1 - Visualization

Decision Trees & Random Forest - Project #1 - Feature Engineering 1

Decision Trees & Random Forest - Project #1 - Feature Engineering 2

Decision Trees & Random Forest - Project #1 - Training

Decision Trees & Random Forest - Project #1 - Evaluation

Decision Trees & Random Forest - Project #1 - Improvements

Decision Trees & Random Forest - Project #2 Overview

Decision Trees & Random Forest - Project #2 - Overview and set up

Decision Trees & Random Forest - Project #2 - Data cleaning/Model training

Decision Trees & Random Forest - Project #3 - Training/Testing

Decision Trees & Random Forest - Project #2 - Evaluation

Naive Bayes Classifiers

Naive Bayes Intro and Learning Outcomes

Naive Bayes Intuition

Naive Bayes - Mathematics

Project #1 - Project overview

Project #1 - Data Visualization

Project #1 - Count vectorizer

Project #1 - Training part 1

Project #1 - Training part 2

Project #1 - Testing

Project #2 - Overview

Project #2 - Importing data

Project #2 - Training/Testing

Project #2 - Testing

Project #2 - Improvements

Congratulations!! Don't forget your Prize :)

Bonus: How To UNLOCK Top Salaries (Live Training)

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by a team with over 10 years of industry experience in machine learning and deep learning

Develops advanced machine learning models for spam classification, sentiment analysis, and disease prediction

Offers 10 practical projects using real-world datasets to build a strong portfolio

Provides clear and simple explanations of complex machine learning concepts

Covers key machine learning algorithms in Python's Scikit-Learn library

Teaches feature engineering for optimal model performance

Assumes no intimidating mathematics, making the course accessible to beginners

Course material is available on Jupyter Notebooks for convenient coding practice

Not suitable for students seeking a comprehensive foundation in machine learning theory and mathematics

Reviews summary

Good start for beginners

Learners say this course is a good start for those who want to begin in the field of machine learning. However, they caution that much more is needed to land a job in the field.

Suitable for those starting out.

"Great Course to Begin with"

Not enough to get a job.

"you have to learn much more before thinking to land for a job in this field of machine learning"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning Classification Bootcamp in Python with these activities:

Join a study group or online forum to discuss machine learning classification techniques

Show steps

Enhance understanding through collaboration and exchange of ideas with peers.

Browse courses on Machine Learning

Show steps

Identify and join a study group or online forum focused on machine learning classification techniques.
Participate actively in discussions, sharing knowledge and insights.
Seek clarification and support from other members when needed.
Engage in peer review and constructive feedback on projects or assignments.

Follow tutorials on machine learning classification techniques

Show steps

Gain practical insights into machine learning classification techniques through guided instruction.

Browse courses on Logistic Regression

Show steps

Identify reputable online platforms or resources that offer tutorials on machine learning classification techniques.
Choose a tutorial that aligns with your learning goals and experience level.
Follow the tutorial step-by-step, taking notes and experimenting with the code provided.
Apply the concepts learned in the tutorial to your own projects or datasets.

Practice using different classification algorithms

Show steps

Develop fluency in employing numerous classification algorithms for various data science applications.

Browse courses on Logistic Regression

Show steps

Select a classification algorithm to practice with.
Gather a dataset appropriate for the algorithm.
Implement the algorithm using a programming language such as Python.
Test the algorithm's performance on the dataset.
Evaluate the results and adjust the algorithm's parameters as needed.

Show all three activities

Career center

Learners who complete Machine Learning Classification Bootcamp in Python will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, implement, and maintain machine learning models at scale to solve real-world problems. This Machine Learning Classification Bootcamp in Python can be helpful for a Machine Learning Engineer because it provides an understanding of key aspects of state-of-the-art classification techniques, including hands-on experience with real-world datasets.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts gather, clean, analyze, and interpret data to help organizations understand trends, patterns, and opportunities. This Machine Learning Classification Bootcamp in Python can be helpful for a Data Analyst because it provides an understanding of key aspects of state-of-the-art classification techniques, including how to clean and prepare data.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists help organizations make better decisions by leveraging algorithms and data, with the goal of better understanding customers, products, systems, or services. This Machine Learning Classification Bootcamp in Python can be helpful for a Data Scientist because it provides an understanding of key aspects of state-of-the-art classification techniques, including hands-on experience with real-world datasets.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

Quantitative Analysts use mathematical and statistical modeling to analyze financial data, identify trends and patterns, and make investment decisions. This Machine Learning Classification Bootcamp in Python can be helpful for a Quantitative Analyst because it provides an understanding of key aspects of state-of-the-art classification techniques, such as logistic regression, decision trees, and random forests.

See salaries and explore the career path for Quantitative Analyst

Business Intelligence Analyst

Business Intelligence Analysts use data to identify trends and patterns that can help businesses make better decisions, optimize performance, and implement strategies. This Machine Learning Classification Bootcamp in Python can be helpful for a Business Intelligence Analyst because it provides an understanding of key aspects of state-of-the-art classification techniques, such as predicting and classifying customer behavior.

See salaries and explore the career path for Business Intelligence Analyst

Financial Analyst

Financial Analysts use financial data to make investment decisions and provide recommendations to clients. This Machine Learning Classification Bootcamp in Python can be helpful for a Financial Analyst because it provides an understanding of key aspects of state-of-the-art classification techniques, such as predicting customer behavior and classifying financial data.

See salaries and explore the career path for Financial Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. This Machine Learning Classification Bootcamp in Python can be helpful for a Software Engineer because it provides hands-on experience with Python and Scikit-Learn for data science and machine learning.

See salaries and explore the career path for Software Engineer

Data Engineer

Data Engineers design, build, and maintain data pipelines and systems to support data-driven decision-making. This Machine Learning Classification Bootcamp in Python can be helpful for a Data Engineer because it provides an understanding of key aspects of state-of-the-art classification techniques, such as data cleaning and feature engineering.

See salaries and explore the career path for Data Engineer

Health Data Analyst

Health Data Analysts analyze health data to identify trends and patterns, improve patient care, and develop new treatments. This Machine Learning Classification Bootcamp in Python can be helpful for a Health Data Analyst because it provides an understanding of key aspects of state-of-the-art classification techniques, such as classifying diseases and predicting patient outcomes.

See salaries and explore the career path for Health Data Analyst

Marketing Analyst

Marketing Analysts use data to understand customer behavior, develop marketing campaigns, and measure the effectiveness of marketing efforts. This Machine Learning Classification Bootcamp in Python can be helpful for a Marketing Analyst because it provides an understanding of key aspects of state-of-the-art classification techniques, such as predicting and classifying customer behavior.

See salaries and explore the career path for Marketing Analyst

Risk Analyst

Risk Analysts assess and manage risks to an organization. This Machine Learning Classification Bootcamp in Python can be helpful for a Risk Analyst because it provides an understanding of key aspects of state-of-the-art classification techniques, such as fraud detection and classification.

See salaries and explore the career path for Risk Analyst

User Experience Researcher

User Experience Researchers conduct research to understand how users interact with products and services, with the goal of improving the user experience. This Machine Learning Classification Bootcamp in Python may be useful for a User Experience Researcher because it provides hands-on experience with Python and Scikit-Learn for data science and machine learning.

See salaries and explore the career path for User Experience Researcher

Product Manager

Product Managers lead the development and launch of new products. This Machine Learning Classification Bootcamp in Python may be useful for a Product Manager because it provides an understanding of key aspects of state-of-the-art classification techniques, such as predicting customer behavior and classifying customer reviews.

See salaries and explore the career path for Product Manager

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve complex problems in a variety of industries. This Machine Learning Classification Bootcamp in Python can be helpful for an Operations Research Analyst because it provides an understanding of key aspects of state-of-the-art classification techniques, such as optimization and forecasting.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians collect, analyze, interpret, and present data to help organizations understand trends, patterns, and opportunities. This Machine Learning Classification Bootcamp in Python can be helpful for a Statistician because it provides an understanding of key aspects of state-of-the-art classification techniques, such as statistical modeling and inference.

See salaries and explore the career path for Statistician