Food Hygiene
Health & Safety
Safeguarding
First Aid
Business Skills
Personal Development
GET THIS COURSE AND 2500+ OTHERS FOR ONLY £49 FIND OUT MORE
Python for Machine Learning: The Complete Beginner’s Course teaches you everything on the topic thoroughly from scratch so you can achieve a professional certificate for free to showcase your achievement in professional life. This Python for Machine Learning: The Complete Beginner’s Course is a comprehensive, instructor-guided course, designed to provide a detailed understanding of the nature of the related sector and your key roles within it.
To become successful in your profession, you must have a specific set of skills to succeed in today’s competitive world. In this in-depth training course, you will develop the most in-demand skills to kickstart your career, as well as upgrade your existing knowledge & skills.
The training materials of this course are available online for you to learn at your own pace and fast-track your career with ease.
- Accredited by CPD
- Instant e-certificate and hard copy dispatch by next working day
- Fully online, interactive course with audio voiceover
- Self-paced learning and laptop, tablet, smartphone-friendly
- 24/7 Learning Assistance
- Discounts on bulk purchases
Sneak Peek
Who should take the course
Anyone with a knack for learning new skills can take this Python for Machine Learning: The Complete Beginner’s Course. While this comprehensive training is popular for preparing people for job opportunities in the relevant fields, it also helps to advance your career for promotions.
Certification
Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.
Accreditation
All of our courses, including this Python for Machine Learning: The Complete Beginner’s Course, are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.
Course Curriculum
The detailed curriculum outline of our Python for Machine Learning: The Complete Beginner’s Course is as follows:
- What is Machine Learning?
- Applications of Machine Learning
- Machine learning Methods
- What is Supervised learning?
- What is Unsupervised learning?
- Supervised learning vs Unsupervised learning
- Introduction
- Python Libraries for Machine Learning
- Setting up Python
- What is Jupyter?
- Anaconda Installation Windows Mac and Ubuntu
- Implementing Python in Jupyter
- Managing Directories in Jupyter Notebook
- Introduction to regression
- How Does Linear Regression Work?
- Line representation
- Implementation in Python: Importing libraries & datasets
- Implementation in Python: Distribution of the data
- Implementation in Python: Creating a linear regression object
- Understanding Multiple linear regression
- Implementation in Python: Exploring the dataset
- Implementation in Python: Encoding Categorical Data
- Implementation in Python: Splitting data into Train and Test Sets
- Implementation in Python: Training the model on the Training set
- Implementation in Python: Predicting the Test Set results
- Evaluating the performance of the regression model
- Root Mean Squared Error in Python
- Introduction to classification
- K-Nearest Neighbors algorithm
- Example of KNN
- K-Nearest Neighbours (KNN) using python
- Implementation in Python: Importing required libraries
- Implementation in Python: Importing the dataset
- Implementation in Python: Splitting data into Train and Test Sets
- Implementation in Python: Feature Scaling
- Implementation in Python: Importing the KNN classifier
- Implementation in Python: Results prediction & Confusion matrix
- Introduction to decision trees
- What is Entropy?
- Exploring the dataset
- Decision tree structure
- Implementation in Python: Importing libraries & datasets
- Implementation in Python: Encoding Categorical Data
- Implementation in Python: Splitting data into Train and Test Sets
- Implementation in Python: Results Prediction & Accuracy
- Introduction
- Implementation steps
- Implementation in Python: Importing libraries & datasets
- Implementation in Python: Splitting data into Train and Test Sets
- Implementation in Python: Pre-processing
- Implementation in Python: Training the model
- Implementation in Python: Results prediction & Confusion matrix
- Logistic Regression vs Linear Regression
- Introduction to clustering
- Use cases
- K-Means Clustering Algorithm
- Elbow method
- Steps of the Elbow method
- Implementation in python
- Hierarchical clustering
- Density-based clustering
- Implementation of k-means clustering in Python
- Importing the dataset
- Visualizing the dataset
- Defining the classifier
- 3D Visualization of the clusters
- Number of predicted clusters
- Introduction
- Collaborative Filtering in Recommender Systems
- Content-based Recommender System
- Implementation in Python: Importing libraries & datasets
- Merging datasets into one dataframe
- Sorting by title and rating
- Histogram showing number of ratings
- Frequency distribution
- Jointplot of the ratings and number of ratings
- Data pre-processing
- Sorting the most-rated movies
- Grabbing the ratings for two movies
- Correlation between the most-rated movies
- Sorting the data by correlation
- Filtering out movies
- Sorting values
- Repeating the process for another movie
- Conclusion
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 | 00:02:00 | ||
| 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 | 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 | 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 | ||
£20
ex Vat
TAKE THIS COURSE
SAVE 84% - OFFER ENDS SOON
- Original price was: £125.00.£20.00Current price is: £20.00. ex Vat
- 1 year
- Beginner
- Course Certificate
- 2 hours, 30 minutes Gift this course
Buying more than one of the same courses?
- 50% discount for orders of 10+ courses
- 60% discount for orders of 50+ courses
- 70% discount for orders of 100+ courses
Looking for a more personalised package?
contact us nowInstructors
8 STUDENTS ENROLLED
