# Skill Path: Machine Learning Introduction with Python

Machine learning is an exciting and in-demand aspect of the artificial intelligence world. It enables systems to learn and improve without direct instructions from users. In this path we cover the essential programming and statistics skills required for getting started in machine learning. From there we learn common machine learning techniques, including k-nearest neighbors, k-means clustering, and decision trees.

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**Learn Machine Learning with Python**

###### Here's what you'll learn to do.

Dataquest Skill Paths teach you job-ready skills that can be immediately applied to your current or future data roles and projects.

- How to program in Python, including how to clean and visualize data
- How to make predictions using statistics and machine learning
- The basics of building a machine learning project from start to finish
- Common machine learning techniques, including k-nearest neighbors, k-means clustering, and decision trees

## Course Structure:

## Machine Learning Intro with Python

### Python for Data Science: Fundamentals

Learn the basics of Python programming and data science.

### Python for Data Science: Intermediate

Learn important tools for your Python data science toolbox.

### Pandas and NumPy Fundamentals

Learn how to analyze data using the pandas and NumPy libraries.

### Data Visualization Fundamentals

Learn the fundamentals of data visualization in Python by striking a good balance between graph interpretation (statistics) and tooling (Matplotlib and Seaborn).

### Data Cleaning and Analysis

Learn data cleaning and analysis with pandas, how to combine data sets, and how to clean string and handle missing data.

### Statistics Fundamentals

Learn about sampling, variables and distributions.

### Statistics Intermediate: Averages and Variability

Learn how to summarize distributions using the mean, the median, and the mode. Learn to measure variability using variance or standard deviation, and how to locate and compare values using z-scores.

### Probability: Fundamentals

Learn the fundamentals of probability theory using Python.

### Machine Learning Fundamentals

Learn the basics of machine learning and explore how to avoid common pitfalls in machine learning.

### Conditional Probability

Learn about conditional probability, Bayes' theorem, and Naive Bayes.

### Calculus For Machine Learning

Explore the key ideas from calculus for understanding how mathematical functions behave and prepare for intermediate machine learning techniques.

### Linear Algebra For Machine Learning

Explore the key ideas from linear algebra for understanding linear systems and prepare for intermediate machine learning techniques.

### Deep Learning Fundamentals

Learn how neural networks are represented, how neural networks capture nonlinearity in the data, and how adding hidden layers can provide improved model performance.

### Machine Learning Project

Walk through a machine learning project start to finish.

### Kaggle Fundamentals

Build a simple machine learning model and make your first Kaggle submission and create new features and select the best-performing features to improve your score.