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The Machine Learning in Python Series: Level 1 (Beginners)

The Machine Learning in Python Series: Level 1 (Beginners)

Build a solid foundation in Machine Learning: Linear Regression, Logistic Regression and K-Means Clustering in Python

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What you'll learn

  • Machine Learning
  • The Machine Learning Process
  • Regression
  • Ordinary Least Squares
  • Simple Linear Regression
  • Multiple Linear Regression
  • R-Squared
  • Adjusted R-Squared
  • Classification
  • Maximum Likelihood
  • Feature Scaling
  • Confusion Matrix
  • Accuracy
  • Clustering
  • K-Means Clustering
  • The Elbow Method
  • K-Means++
  • Build Machine Learning models in Python
  • Make Predictions

In this course you will master the foundations of Machine Learning and practice building ML models with real-world case studies. We will start from scratch and explain:

What Machine Learning is

  • The Machine Learning Process of how to build a ML model

Regression: Predict a continuous number

  • Simple Linear Regression
  • Ordinary Least Squares
  • Multiple Linear Regression
  • R-Squared
  • Adjusted R-Squared

Classification: Predict a Category / Class

  • Logistic Regression
  • Maximum Likelihood
  • Feature Scaling
  • Confusion Matrix
  • Accuracy

Clustering: Predict / Identify a Pattern

  • K-Means Clustering
  • The Elbow Method

We will also do the following the three following practical activities:

  • Real-World Case Study: Build a Multiple Linear Regression model
  • Real-World Case Study: Build a Logistic Regression model
  • Real-World Case Study: Build a K-Means Clustering model

The Course Objectives are the following:

- Get the right basics of how machine learning works and how models are built.
- Understand what is regression.
- Understand the theory behind the linear regression model.
- Know how to build, train and evaluate a linear regression model for a real-world case study.
- Understand what is classification.
- Understand the theory behind the logistic regression model.
- Understand and apply feature scaling including both normalization and standardization.
- Know how to build, train and evaluate a logistic regression model for a real-world case study.
- Understand what is clustering.
- Understand the theory behind the k-means clustering model.
- Know how to build, train and evaluate the k-means clustering model for a real-world case study.
Online Course CoupoNED based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.