Python for Machine Learning: The Complete Beginner's Course

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1 Year Access

Intermediate

3 Students

3 hours, 10 minutes

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Python for Machine Learning: The Complete Beginner’s Course Overview

Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no further; you’ve come to the right place!

Our easy-to-follow Python for Machine Learning: The Complete Beginner’s Course will provide you with all the deep knowledge and insight you need to know about this topic. This comprehensive course has been broken down into several manageable modules, which we believe will assist you to easily grasp each concept – from the fundamental to the more advanced aspects of the course.

Learn the most in-demand skills from the safety & comfort of your home. Enjoy the freedom to learn at your own comfortable pace and prepare yourself for the market of the future.

Learning Outcomes

Whether you are an aspiring professional or a complete beginner, this course will improve your expertise and boost your CV with key skills and an accredited certification attesting to your knowledge.

Entry Requirements

This course is available to all learners of all academic backgrounds.
Learners should be aged 16 or over to undertake the qualification.
Some basic understanding of the English language and numeracy.

Why Choose Us

Affordable, engaging & high-quality e-learning study materials;
Tutorial videos/materials from the industry-leading experts;
Study in a user-friendly, advanced online learning platform;
Efficient exam systems for the assessment and instant result;
The UK & internationally recognised accredited
Access to course content on mobile, tablet or desktop from anywhere, anytime;
The benefit of career advancement opportunities;
24/7 student support via email.

Career Path

Develop Your Career Plan is a useful qualification to possess and would be beneficial for any related profession or industry.

Course Curriculum

Section 01: Introduction to Machine Learning
	What is Machine Learning?	00:02:00
	Applications of Machine Learning	00:02:00
	Machine learning Methods	00:01:00
	What is Supervised learning?	00:01:00
	What is Unsupervised learning?	00:01:00
	Supervised learning vs Unsupervised learning	00:04:00
Section 02: Setting Up Python & ML Algorithms Implementation
	Introduction	00:03:00
	Python Libraries for Machine Learning	00:02:00
	Setting up Python	00:02:00
	What is Jupyter?	00:05:00
	Anaconda Installation Windows Mac and Ubuntu	00:06:00
	Implementing Python in Jupyter	00:01:00
	Managing Directories in Jupyter Notebook	00:05:00
Section 03: Simple Linear Regression
	Introduction to regression	00:02:00
	How Does Linear Regression Work?	00:02:00
	Line representation	00:01:00
	Implementation in Python: Importing libraries & datasets	00:05:00
	Implementation in Python: Distribution of the data	00:02:00
	Implementation in Python: Creating a linear regression object	00:05:00
Section 04: Multiple Linear Regression
	Understanding Multiple linear regression	00:03:00
	Implementation in Python: Exploring the dataset	00:00:00
	Implementation in Python: Encoding Categorical Data	00:06:00
	Implementation in Python: Splitting data into Train and Test Sets	00:04:00
	Implementation in Python: Training the model on the Training set	00:04:00
	Implementation in Python: Predicting the Test Set results	00:03:00
	Evaluating the performance of the regression model	00:01:00
	Root Mean Squared Error in Python	00:03:00
Section 05: Classification Algorithms: K-Nearest Neighbors
	Introduction to classification	00:01:00
	K-Nearest Neighbors algorithm	00:01:00
	Example of KNN	00:03:00
	K-Nearest Neighbours (KNN) using python	00:01:00
	Implementation in Python: Importing required libraries	00:01:00
	Implementation in Python: Importing the dataset	00:02:00
	Implementation in Python: Splitting data into Train and Test Sets	00:03:00
	Implementation in Python: Feature Scaling	00:03:00
	Implementation in Python: Importing the KNN classifier	00:02:00
	Implementation in Python: Results prediction & Confusion matrix	00:02:00
Section 06: Classification Algorithms: Decision Tree
	Introduction to decision trees	00:01:00
	What is Entropy?	00:02:00
	Exploring the dataset	00:01:00
	Decision tree structure	00:01:00
	Implementation in Python: Importing libraries & datasets	00:01:00
	Implementation in Python: Encoding Categorical Data	00:05:00
	Implementation in Python: Splitting data into Train and Test Sets	00:01:00
	Implementation in Python: Results Prediction & Accuracy	00:03:00
Section 07: Classification Algorithms: Logistic regression
	Introduction	00:01:00
	Implementation steps	00:01:00
	Implementation in Python: Importing libraries & datasets	00:02:00
	Implementation in Python: Splitting data into Train and Test Sets	00:01:00
	Implementation in Python: Pre-processing	00:02:00
	Implementation in Python: Training the model	00:01:00
	Implementation in Python: Results prediction & Confusion matrix	00:02:00
	Logistic Regression vs Linear Regression	00:02:00
Section 08: Clustering
	Introduction to clustering	00:01:00
	Use cases	00:01:00
	K-Means Clustering Algorithm	00:01:00
	Elbow method	00:04:00
	Steps of the Elbow method	00:01:00
	Implementation in python	00:07:00
	Hierarchical clustering	00:02:00
	Density-based clustering	00:03:00
	Implementation of k-means clustering in Python	00:03:00
	Importing the dataset	00:04:00
	Visualizing the dataset	00:03:00
	Defining the classifier	00:02:00
	3D Visualization of the clusters	00:04:00
	Number of predicted clusters	00:02:00
Section 09: Recommender System
	Introduction	00:02:00
	Collaborative Filtering in Recommender Systems	00:01:00
	Content-based Recommender System	00:02:00
	Implementation in Python: Importing libraries & datasets	00:05:00
	Merging datasets into one dataframe	00:00:00
	Sorting by title and rating	00:04:00
	Histogram showing number of ratings	00:01:00
	Frequency distribution	00:03:00
	Jointplot of the ratings and number of ratings	00:02:00
	Data pre-processing	00:02:00
	Sorting the most-rated movies	00:01:00
	Grabbing the ratings for two movies	00:01:00
	Correlation between the most-rated movies	00:00:00
	Sorting the data by correlation	00:01:00
	Filtering out movies	00:01:00
	Sorting values	00:01:00
	Repeating the process for another movie	00:02:00
Section 10: Conclusion
	Conclusion	00:01:00