Python for Machine Learning: The Complete Beginner's Course

Home
Course
IT & Software
Python for Machine Learning: The Complete Beginner's Course

Python for Machine Learning: The Complete Beginner's Course

Level: Intermediate
Modules: 84 Modules
Duration: 2 hours, 29 minutes
Students: 2

Unlock the doors to the future of technology with Python for Machine Learning: The Complete Beginner’s Course. This comprehensive course is designed to take you from a novice to a proficient coder, ready to tackle the exciting world of machine learning. Starting with an introduction to machine learning concepts, this course gradually guides you through the essentials of setting up Python and implementing various ML algorithms. Dive deep into the fascinating realms of linear regression, classification algorithms, and clustering, all while mastering the techniques of a recommender system. Python for Machine Learning: The Complete Beginner’s Course is meticulously crafted to provide you with a robust theoretical foundation, making complex topics accessible and engaging. Whether you’re looking to pivot into a new career or simply expand your knowledge, Python for Machine Learning: The Complete Beginner’s Course will equip you with the necessary skills to excel in this rapidly evolving field.

Learning Outcomes

Understand the fundamentals of machine learning.
Set up Python environment for machine learning tasks.
Implement simple and multiple linear regression models.
Explore various classification algorithms.
Learn about clustering techniques.
Gain insights into building recommender systems.

Why buy this Python for Machine Learning: The Complete Beginner’s Course?

Unlimited access to the course for forever
Digital Certificate, Transcript, student ID all included in the price
Absolutely no hidden fees
Directly receive CPD accredited qualifications after course completion
Receive one to one assistance on every weekday from professionals
Immediately receive the PDF certificate after passing
Receive the original copies of your certificate and transcript on the next working day
Easily learn the skills and knowledge from the comfort of your home

Certification

After studying the course materials of the Python for Machine Learning: The Complete Beginner’s Course there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99. Original Hard Copy certificates need to be ordered at an additional cost of £8.

Who is this Python for Machine Learning: The Complete Beginner’s Course for?

Beginners with no prior machine learning experience.
Python programmers looking to expand their knowledge into machine learning.
Data enthusiasts eager to understand theoretical aspects of machine learning.
Students and professionals interested in the foundations of machine learning algorithms.
Individuals aiming to enhance their analytical and technical skills.
Lifelong learners passionate about entering the field of machine learning.

Prerequisites

This Python for Machine Learning: The Complete Beginner’s Course does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Python for Machine Learning: The Complete Beginner’s Course was made by professionals, and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.

Career path

Data Scientist: £45,000 to £65,000 per year
Machine Learning Engineer: £50,000 to £70,000 per year
AI Research Scientist: £50,000 to £80,000 per year
Data Analyst: £30,000 to £45,000 per year
Software Developer (with a focus on ML): £35,000 to £55,000 per year
Business Intelligence Developer: £40,000 to £60,000 per year

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 S2	00:01:00
	Python Libraries for Machine Learning	00:02:00
	Setting up Python	00:02:00
	What is Jupyter?	00:02:00
	Anaconda Installation Windows Mac and Ubuntu	00:04:00
	Implementing Python in Jupyter	00:01:00
	Managing Directories in Jupyter Notebook5	00:03:00
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	00:02:00
	Implementation in Python: Distribution of the data	00:02:00
	Implementation in Python: Creating a linear regression object	00:03:00
Section 04: Multiple Linear Regression
	Understanding Multiple linear regression	00:02:00
	Implementation in Python: Exploring the dataset	00:04:00
	Implementation in Python: Encoding Categorical Data	00:05:00
	Implementation in Python: Splitting data into Train and Test Sets	00:02:00
	Implementation in Python: Training the model on the Training set	00:01:00
	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	00:01:00
	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	00:01:00
	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?	00:01:00
	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	00:03:00
	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 S7	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	00:02:00
	Steps of the Elbow method	00:01:00
	Implementation in python	00:04:00
	Hierarchical clustering	00:01:00
	Density-based clustering	00:02:00
	Implementation of k-means clustering in Python	00:01:00
	Importing the dataset	00:03:00
	Visualizing the dataset	00:02:00
	Defining the classifier	00:02:00
	3D Visualization of the clusters	00:03:00
	Number of predicted clusters	00:02:00
Section 09: Recommender System
	Introduction S9	00:01:00
	Content-based Recommender System	00:01:00
	Implementation in Python: Importing libraries & datasets	00:03:00
	Merging datasets into one dataframe	00:01:00
	Sorting by title and rating	00:04:00
	Histogram showing number of ratings	00:01:00
	Frequency distribution	00:01:00
	Jointplot of the ratings and number of ratings	00:01:00
	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	00:02:00
	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