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Machine Learning with Python Overview

Take advantage of our comprehensive Machine Learning with Python course on the power of machines to learn and make predictions.

This course delves into the fundamentals of machine learning, giving you the skills to build intelligent systems that can analyze data, identify patterns, and make data-driven decisions.

With manual Python programming, you can develop important machine learning algorithms such as linear regression, decision trees, and k-nearest neighbors. Learn data preprocessing techniques, model evaluation methods, and explore advanced topics such as natural language processing and deep learning.

This course is designed for both beginners and those with some programming experience. No prior Machine Learning skills are necessary, we will go through Python and configure the necessary libraries using ML algorithms.

Learning Outcomes

  • Grasp the fundamental principles of machine learning and its applications in various industries.
  • Master the Python programming language, essential for building machine learning models.
  • Implement core machine learning algorithms like linear regression, decision trees, and k-nearest neighbors.
  • Analyze data to uncover hidden patterns and build predictive models.
  • Design and develop intelligent applications using machine learning techniques.

Why You Should Choose Machine Learning with Python

  • Lifetime access to the course
  • No hidden fees or exam charges
  • CPD Accredited certification on successful completion
  • Full Tutor support on weekdays (Monday – Friday)
  • Efficient exam system, assessment and instant results
  • Download Printable PDF certificate immediately after completion
  • Obtain the original print copy of your certificate, dispatch the next working day for as little as £9.
  • Improve your chance of gaining professional skills and better earning potential.

Who is this Course for?

Machine Learning with Python is CPD certified and IAO accredited. This makes it perfect for anyone trying to learn potential professional skills.

As there is no experience and qualification required for this course, it is available for all students from any academic backgrounds.

Requirements

Our Machine Learning with Python is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.

Career Path

You will be ready to enter the relevant job market after completing this course. You will be able to gain necessary knowledge and skills required to succeed in this sector. All our Diplomas’ are CPD and IAO accredited so you will be able to stand out in the crowd by adding our qualifications to your CV and Resume.

Course Curriculum

Machine Learning with Python
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 Notebook 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 00:03:00
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 00:03:00
Implementation in Python: Splitting data into Train and Test Sets 00:02:00
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 00:02:00
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
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 00:02:00
Collaborative Filtering in Recommender Systems 00:01:00
Content-based Recommender System 00:01:00
Implementation in Python: Importing libraries & datasets 00:02:00
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

FAQs

This course is for anyone who's interested in this topic and wants to learn more about it. This course will also help you gain potential professional skills.

No prior qualifications are needed to take this course.

You can study this course from wherever and whenever you want. You can study at your own pace and from any device. Just log in to your account from any device and start learning!

Yes, there is a test at the end of the course. Once you’ve completed all the modules of the course, you will have to give a multiple-choice test. The questions will be based on the topics of the modules you studied. And of course, you can take the test at any time, from any device and from anywhere you want.

Don’t worry if you fail the test, you can retake it as many times as you want.

You don’t have to wait a minute after your payment has been received, you can begin immediately. You will create your login details during the checkout process and we will also send you an email confirming your login details.

We make the payment process easy for you. You can either use your Visa, MasterCard, American Express, Solo cards or PayPal account to pay for the online course. We use the latest SSL encryption for all transactions, so your order is safe and secure.

After you complete the course, you’ll immediately receive a free printable PDF certificate. Hard Copy certificate is also available, and you can get one for just £9! You may have to wait for 3 to 9 days to get the hard copy certificate.

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