Python is recognized as one of the most widely used programming languages in machine learning (ML). For this reason, it is a required skill in many ML job listings.

This course is suitable for machine learning practitioners looking to acquire essential Python skills that will help them stand out to employers. It focuses on core ML concepts and the iterative nature of model development. Using Python libraries such as Scikit-learn, you will be able to gain hands-on experience with tools used for real-world applications. You will also be able to build a strong foundation in statistical methods such as linear and logistic regression.

With its extensive libraries, such as TensorFlow and Pandas, you'll discover supervised learning techniques. Its classification methods, such as decision trees, KNN, and SVM, and key concepts, such as the bias-variance tradeoff, will enable you to explore its power and versatility. Additionally, the course includes unsupervised learning, such as clustering and dimensionality reduction.

Guidance on model evaluation, tuning techniques, and practical projects in Jupyter Notebooks will equip you with strong Python skills to power your ML journey. ENROLL TODAY to enhance your resume with in-demand expertise!

Learning Objectives

Job-ready foundational machine learning skills in Python in just 6 weeks, including how to use Scikit-learn to implement machine learning models.
How to apply data preparation techniques and manage bias-variance tradeoffs to optimize model performance.
How to build supervised learning models and implement core machine learning algorithms for classification and regression tasks, including linear regression, decision trees, and SVM.
How to build unsupervised learning models and implement clustering models and dimension reduction algorithms.

Course Syllabus

Module 1: Introduction to Machine Learning

Welcome to the Course

Video: Course Introduction and Welcome
Reading: Course Overview
Learning Objectives and Syllabus
Plugin/Reading: Helpful Tips for Course Completion
Grading Scheme

Machine Learning In Action

An Overview of Machine Learning
Machine Learning Model Lifecycle
A Day in the Life of a Machine Learning Engineer
Data Scientist vs AI Engineer
Tools for Machine Learning
Scikit-learn Machine Learning Ecosystem
Practice Quiz: Intro to Machine Learning
Module Summary and Highlights
Graded Quiz: Intro to Machine Learning

Module 2: Linear and Logistic Regression

Introduction to Regression
Simple Linear Regression
Lab: Simple Linear Regression
Multiple Linear Regression
Lab: Multiple Linear Regression
Non-Linear Polynomial Regression
Practice Quiz: Linear Regression
Introduction to Logistic Regression
Logistic Regression Training
Lab: Logistic Regression
Practice Quiz: Regression
Module Summary
Cheatsheet
Graded Quiz: Regression

Module 3: Building Supervised learning models

Classification
Lab: Multiclass Prediction
Decision Trees
Lab: Decision Trees
Regression Trees
Lab: Regression Trees
Practice Quiz
Supervised Learning with SVMs
Lab: Credit Card Fraud Detection with Decision Trees and SVM
Supervised Learning with KNN
Lab: KNN Classification
Bias, Variance, and Ensemble Models
Lab: Random Forests and XGBoost
Practice Quiz: Other Supervised Learning Models
Module Summary
Cheatsheet
Graded Quiz

Module 4: Building Unsupervised Learning Models

Clustering Strategies and Real-World Applications
k-means and More on k-means
Lab: K-Means
DBSCAN and HDBSCAN
Lab: Comparing DBSCAN and HDBSCAN
Practice Quiz
Clustering, Dimension Reduction & Feature Engineering
Dimension reduction Algorithms
PCA Principle Component Analysis
t-SNE and UMAP
Practice Quiz: Advance Clustering Models
Module Summary
Cheatsheet
Graded Quiz

Module 5: Evaluating and Validating Machine Learning Models

Classification Metrics and Evaluation Techniques
Lab: Evaluating Classification Models
Regression Metrics and Evaluation Techniques
Lab: Evaluating random forest performance
Evaluating Unsupervised Learning Models: Heuristics and Techniques
Lab:Evaluating k-means clustering
Practice Quiz: Evaluating ML Models
Cross-Validation and Advanced Model Validation Techniques
Regularization in Regression and Classification
Lab: Regularization in Linear Regression
Data Leakage and other Pitfalls
Lab: Machine Learning Pipelines and GridSearchCV
Practice Quiz
Module Summary
Cheatsheet
Graded Quiz

Module 6: Course Summary and Final Project

Course Wrap up
Final Exam
Final Project Scenario
Final Project: Building a Rainfall Prediction Classifier
Final Project Submission and Evaluation
Congratulations and Next Steps
Thanks from the Course Team

Recommended Skills Prior to Taking this Course

To get the most out of this course, you should be comfortable with the following topics and technologies:
This course requires a working knowledge of Python and Python libraries, such as Pandas, NumPy, and more, to perform data preparation and data analysis.

Machine Learning with Python: A Practical Introduction

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