Python for Machine Learning: The Complete Beginner’s Course

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5 Students

Last updated: October 18, 2024
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Language: English
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Flexible Schedule

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

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

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

  1. Unlimited access to the course for forever
  2. Digital Certificate, Transcript, student ID all included in the price
  3. Absolutely no hidden fees
  4. Directly receive CPD accredited qualifications after course completion
  5. Receive one to one assistance on every weekday from professionals
  6. Immediately receive the PDF certificate after passing
  7. Receive the original copies of your certificate and transcript on the next working day
  8. 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?

  1. Beginners with no prior machine learning experience.
  2. Python programmers looking to expand their knowledge into machine learning.
  3. Data enthusiasts eager to understand theoretical aspects of machine learning.
  4. Students and professionals interested in the foundations of machine learning algorithms.
  5. Individuals aiming to enhance their analytical and technical skills.
  6. 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
Python for Machine Learning: The Complete Beginner’s Course
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This course includes:

  • level Skill Level
  • course_duration Duration
    2 hours, 29 minutes
  • studentsStudents
    5 Students