[2019] Machine Learning Classification Bootcamp in Python
[2019] Machine Learning Classification Bootcamp in Python
Created by multiple instructors | Video: 1280x720 | Audio: AAC 48KHz 2ch | Duration: 11:41 H/M | Lec: 78 | 5.69 GB | Language: English | Sub: English [Auto-generated]
Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn
What you'll learn
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 such as K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression
Implement classification algorithms in Scikit-Learn for K-Nearest Neighbors, Support Vector Machines (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
Requirements
Basic knowledge of Python Programming
Experienced computer user
Description
Are you ready to master Machine Learning techniques and Kick-off your career as a Data Scientist?!
You came to the right place!
Machine Learning skill is one of the top skills to acquire in 2019 with an average salary of over $114,000 in the United States according to PayScale! The total number of ML jobs over the past two years has grown around 600 percent and expected to grow even more by 2020.
This course provides students with knowledge, hands-on experience of state-of-the-art machine learning classification techniques such as
Logistic Regression
Decision Trees
Random Forest
Naïve Bayes
Support Vector Machines (SVM)
In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques. We are going to build 10 projects from scratch using real world dataset, here's a sample of the projects we will be working on:
Build an e-mail spam classifier.
Perform sentiment analysis and analyze customer reviews for Amazon Alexa products.
Predict the survival rates of the titanic based on the passenger features.
Predict customer behavior towards targeted marketing ads on Facebook.
Predicting bank client's eligibility to retire given their features such as age and 401K savings.
Predict cancer and Kyphosis diseases.
Detect fraud in credit card transactions.
Key Course Highlights:
This comprehensive machine learning course includes over 75 HD video lectures with over 11 hours of video content.
The course contains 10 practical hands-on python coding projects that students can add to their portfolio of projects.
No intimidating mathematics, we will cover the theory and intuition in clear, simple and easy way.
All Jupyter noteboooks (codes) and slides are provided.
10+ years of experience in machine learning and deep learning in both academic and industrial settings have been compiled in this course.
Students who enroll in this course will master machine learning classification models and can directly apply these skills to solve real world challenging problems.
Who this course is for?
Data Science Enthusiasts wanting to enhance their machine learning skills
Python programmers curious about Machine Learning and Data Science
Programmers or developers who want to make a shift into the lucrative data science and machine learning career path
Technologists wanting to gain an understanding of how machine learning models work
Data analysts who want to transition into the Tech industry
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