Course Highlights
Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Python for Machine Learning: The Complete Beginner’s course has been specially designed to help learners gain a good command of Python for Machine Learning, providing them with a solid foundation of knowledge to become a qualified professional.
Through this Python for Machine Learning: The Complete Beginner’ course, you will gain both practical and theoretical understanding of Python for Machine Learning: that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.
Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.
Learning outcome
- Familiar yourself with the recent development and updates of the relevant industry
- Know how to use your theoretical knowledge to adapt in any working environment
- Get help from our expert tutors anytime you need
- Access to course contents that are designed and prepared by industry professionals
- Study at your convenient time and from wherever you want
Course media
Why should I take this course?
- Affordable premium-quality E-learning content, you can learn at your own pace.
- You will receive a completion certificate upon completing the course.
- Internationally recognized Accredited Qualification will boost up your resume.
- You will learn the researched and proven approach adopted by successful people to transform their careers.
- You will be able to incorporate various techniques successfully and understand your customers better.
Requirements
- No formal qualifications required, anyone from any academic background can take this course.
- Access to a computer or digital device with internet connectivity.
Course Curriculum
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What is Machine Learning?
00:02:00 -
Applications of Machine Learning
00:02:00
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Machine learning Methods
00:01:00
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What is Supervised learning?
00:01:00
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What is Unsupervised learning?
00:01:00
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Supervised learning vs Unsupervised learning
00:04:00
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Introduction S2
00:01:00
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Python Libraries for Machine Learning
00:02:00
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Setting up Python
00:02:00
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What is Jupyter?
00:02:00
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Anaconda Installation Windows Mac and Ubuntu
00:04:00
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Implementing Python in Jupyter
00:01:00
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Managing Directories in Jupyter Notebook
00:03:00
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Introduction to regression
00:02:00
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How Does Linear Regression Work?
00:02:00
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Line representation
00:01:00
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Implementation in Python: Importing libraries & datasets
00:03:00
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Implementation in Python: Distribution of the data
00:02:00
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Implementation in Python: Creating a linear regression object
00:03:00
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Understanding Multiple linear regression
00:02:00
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Implementation in Python: Exploring the dataset
00:04:00
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Implementation in Python: Encoding Categorical Data
00:03:00
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Implementation in Python: Splitting data into Train and Test Sets
00:01:00
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Implementation in Python: Training the model on the Training set
00:01:00
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Implementation in Python: Predicting the Test Set results
00:03:00
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Evaluating the performance of the regression model
00:01:00
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Root Mean Squared Error in Python
00:03:00
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Introduction to classification
00:01:00
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K-Nearest Neighbors algorithm
00:01:00
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Example of KNN
00:01:00
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K-Nearest Neighbours (KNN) using python
00:01:00
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Implementation in Python: Importing required libraries
00:01:00
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Implementation in Python: Importing the dataset
00:02:00
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Implementation in Python: Splitting data into Train and Test Sets
00:01:00
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Implementation in Python: Feature Scaling
00:01:00
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Implementation in Python: Importing the KNN classifier
00:02:00
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Implementation in Python: Results prediction & Confusion matrix
00:02:00
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Introduction to decision trees
00:01:00
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What is Entropy?
00:01:00
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Exploring the dataset
00:01:00
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Decision tree structure
00:01:00
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Implementation in Python: Importing libraries & datasets
00:03:00
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Implementation in Python: Encoding Categorical Data
00:03:00
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Implementation in Python: Splitting data into Train and Test Sets
00:01:00
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Implementation in Python: Results Prediction & Accuracy
00:03:00
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Introduction S7
00:01:00
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Implementation steps
00:01:00
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Implementation in Python: Importing libraries & datasets
00:03:00
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Implementation in Python: Splitting data into Train and Test Sets
00:01:00
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Implementation in Python: Pre-processing
00:02:00
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Implementation in Python: Training the model
00:01:00
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Implementation in Python: Results prediction & Confusion matrix
00:02:00
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Logistic Regression vs Linear Regression
00:02:00
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Introduction to clustering
00:01:00
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Use cases
00:01:00
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K-Means Clustering Algorithm
00:01:00
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Elbow method
00:02:00
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Steps of the Elbow method
00:01:00
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Implementation in python
00:04:00
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Hierarchical clustering
00:01:00
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Density-based clustering
00:02:00
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Implementation in python
00:04:00
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Implementation of k-means clustering in Python
00:01:00
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Implementation in Python: Importing the dataset
00:02:00
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Visualizing the dataset
00:02:00
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Defining the classifier
00:02:00
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3D Visualization of the clusters
00:03:00
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Number of predicted clusters
00:02:00
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Introduction S9
00:01:00
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Collaborative Filtering in Recommender Systems
00:01:00
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Content-based Recommender System
00:01:00
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Implementation in Python: Importing libraries & datasets
00:03:00
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Merging datasets into one dataframe
00:01:00
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Sorting by title and rating
00:04:00
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Histogram showing number of ratings
00:01:00
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Frequency distribution
00:01:00
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Jointplot of the ratings and number of ratings
00:01:00
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Data pre-processing
00:02:00
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Sorting the most-rated movies
00:01:00
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Grabbing the ratings for two movies
00:01:00
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Correlation between the most-rated movies
00:02:00
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Sorting the data by correlation
00:01:00
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Filtering out movies
00:01:00
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Sorting values
00:01:00
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Repeating the process for another movie
00:02:00
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Conclusion
00:01:00
£25
This course includes:
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Duration:2 hours, 32 minutes
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Access:1 Year
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Units:86
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