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About This Course
- Overview
Educational accomplishments and income are closely correlated. Education and skill with endorsed certificates from credible and renowned authorities typically lead to better jobs with attractive salaries. Educated and skilled workers also have lower rates of unemployment. Therefore, skills and endorsed certificates to showcase are essential for people early in their careers.
- Why Choose Imperial Academy?
Imperial Academy offers this premium Python for Machine Learning: The Complete Beginner’s Course course to ensure security in your career. In addition, this comprehensive Python for Machine Learning: The Complete Beginner’s Course course will assist you in building relevant skills that will help you find a job in the related sectors. Also, the Certificate you’ll get after completing the Python for Machine Learning: The Complete Beginner’s Course will put your head and shoulder above others in front of potential employers.
Become the person who would attract the results you seek. What you plant now, you will harvest later. So, grab this opportunity and start learning Python for Machine Learning: The Complete Beginner’s Course!
- What Imperial Academy Offers You
- QLS/ CPD/ CIQ Accredited
- 24/7 Assistance from our Support Team
- 100% Online
- Self-paced course
- Bite-sized Audio-visual Modules
- Rich Learning Materials
- Developed by Industry Specialists
- Career Guidance
- Course Design
Learn at your own pace from the comfort of your home, as the rich learning materials of this premium course is accessible from any place at any time. The advanced course curriculums are divided into tiny bite-sized audio-visual modules by industry specialists with years of experience behind them.
- Audio-visual Lessons
- Online Study Materials
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
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
00:02:00
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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
00:05:00
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Implementation in Python: Splitting data into Train and Test Sets
00:02:00
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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
00:03:00
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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
00:01:00
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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
00:01:00
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Implementation steps
00:01:00
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Implementation in Python: Importing libraries & datasets
00:02:00
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Implementation in Python: Splitting data into Train and Test Sets
00:02:00
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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
00:02:00
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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 of k-means clustering in Python
00:01:00
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Importing the dataset
00:03:00
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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
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
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