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Python for Machine Learning: The Complete Beginner's Course
1 Students (5 Reviews)
Register on the today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study …
GET THIS COURSE AND 3000+ OTHERS FOR ONLY £49 PER YEAR. FIND OUT MORE
Register on the Python for Machine Learning: The Complete Beginner’s Course today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.
The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.
Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a certificate as proof of your course completion.
The Python for Machine Learning: The Complete Beginner’s Course course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablets, and smartphones.
The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!
What You Get With The Python for Machine Learning: The Complete Beginner’s Course course
- Receive a digital certificate upon successful completion of the course
- Get taught by experienced, professional instructors
- Study at a time and pace that suits your learning style
- Get instant feedback on assessments
- 24/7 help and advice via email or live chat
- Get full tutor support on weekdays (Monday to Friday)
Course Design
The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.
You are taught through a combination of
- Video lessons
- Online study materials
Certification
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post.
Who Is This Course For:
The course is ideal for those who already work in this sector or are aspiring professionals. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.
Requirements:
The online training is open to all students and has no formal entry requirements. To study the Python for Machine Learning: The Complete Beginner’s Course course, all you need is a passion for learning, A good understanding of English, numeracy, and IT skills. You must also be over the age of 16.
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 | 00:01:00 | ||
| 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 | 00:01:00 | ||
| 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 | 00:01:00 | ||
| 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 | 00:03:00 | ||
| 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 | 00:01:00 | ||
| Implementation steps | 00:01:00 | ||
| Implementation in Python: Importing libraries & datasets | 00:03:00 | ||
| 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 | 00:00:00 | ||
| 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 | 00:01:00 | ||
| Collaborative Filtering in Recommender Systems | 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 | ||
| Order your Certificates & Transcripts | |||
| Order your Certificates & Transcripts | 00:00:00 | ||
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Frequently asked questions
Can’t find the anwser you’re looking for ? Reach out to customer support team.
There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course.
You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience.
For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime.
Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks.
We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase.
Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course.
If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.