[2019] Machine Learning Classification Bootcamp in Python

[2019] Machine Learning Classification Bootcamp in Python

image description

Course Description

Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn

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

  • <

    strong>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

What you will 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

Curriculum

Section 1: Introduction

Section 2: What is Machine Learning? The Big Picture

Section 3: Installation & Setup [Optional][Skip if you are familiar with Jupyter Notebooks]

Section 4: Logistic Regression

Section 5: Support Vector Machines

Section 6: K-Nearest Neighbors

Section 7: Decision Trees and Random Forest

Section 8: Naive Bayes Classifiers