Machine Learning with Python: A Practical Introduction

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What Will You Learn?

The difference between the two main types of machine learning methods: supervised and unsupervised
Supervised learning algorithms, including classification and regression
Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
How statistical modeling relates to machine learning and how to compare them
Real-life examples of the different ways machine learning affects society

About This Course

Provider: Edx

Format: Online

Duration: 30 hours to complete [Approx]

Target Audience: Beginners

Learning Objectives: Upon completion this free course, you will be proficient in the fundamentals of machine learning using Python, distinguishing between supervised and unsupervised learning, understanding the relationship between statistical modeling and machine learning.

Course Prerequisites: NA

Assessment and Certification: Earn a Certificate upon completion from the relevant Provider

Instructor: IBM

Key Topics: Algorithms, Machine Learning, Python (Programming Language), Unsupervised Learning, Random Forest Algorithm, Statistical Modeling

Topic Covered:

- Introduction to Machine Learning
- Applications of Machine Learning
- Supervised vs Unsupervised Learning
- Python libraries suitable for Machine Learning
- Regression
- Linear Regression
- Non-linear Regression
- Model evaluation methods
- Classification
- K-Nearest Neighbour
- Decision Trees
- Logistic Regression
- Support Vector Machines
- Model Evaluation
- Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Density-Based Clustering
- Recommender Systems
- Content-based recommender systems
- Collaborative Filtering

Course Details