EXTENDED OFFER ENDS INMachine Learning with Python Overview
Machine Learning with Python Course is yet another “Teacher’s Choice” course from Teachers Training for a complete understanding of the fundamental topics. You are also entitled to exclusive tutor support and a professional CPD-accredited certificate in addition to the special discounted price for a limited time. Just like all our courses, this Machine Learning with Python Course and its curriculum have also been designed by expert teachers so that teachers of tomorrow can learn from the best and equip themselves with all the necessary skills.
Consisting of several modules, the course teaches you everything you need to succeed in this profession.
The course can be studied part-time. You can become accredited within 05 Hours studying at your own pace. Your qualification will be recognised and can be checked for validity on our dedicated website.
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- This is a dedicated website for teaching
- 24/7 tutor support
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Entry Requirements
No formal entry requirements. You need to have:
- Passion for learning
- A good understanding of the English language
- Be motivated and hard-working
- Over the age of 16.
Certification
CPD Certification from The Teachers Training
Successfully completing the MCQ exam of this course qualifies you for a CPD-accredited certificate from The Teachers Training. You will be eligible for both PDF copy and hard copy of the certificate to showcase your achievement however you wish.
- You can get your digital certificate (PDF) for £4.99 only
- Hard copy certificates are also available, and you can get one for only £10.99
- You can get both PDF and Hard copy certificates for just £12.99!
The certificate will add significant weight to your CV and will give you a competitive advantage when applying for jobs.
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 | 00:02:00 | ||
| 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 | 00:03:00 | ||
| Implementation in Python: Splitting data into Train and Test Sets | 00:02:00 | ||
| Implementation in Python: Training the model on the Training set | 00:02:00 | ||
| 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 | ||
| 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 | 00:03:00 | ||
| Merging datasets into one dataframe | 00:01:00 | ||
| Sorting by title and rating | 00:01:00 | ||
| 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 | ||
COURSE REVIEWS
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