Python for Machine Learning: The Complete Beginner's Course
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Python for Machine Learning: The Complete Beginner's Course
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Overview
Empower your career journey with our in-demand course: Python for Machine Learning: The Complete Beginner’s Course
Boost your proficiency and propel your career forward with our meticulously crafted course, designed to be your ultimate guide to professional development. Our super-accessible modules break down complex topics into bite-sized, easy-to-understand lessons, filling your knowledge gaps and equipping you with real-world, practical skills.
Seeking career advancement and application of your skills? You’ve found the right place. This Python for Machine Learning: The Complete Beginner’s Course is your exclusive passport to unlocking your full potential.
Enroll today and enjoy:
- 100% online flexibility: Learn at your own pace, from anywhere in the world.
- Expert support: We're by your side throughout your journey, providing guidance and assistance.
- Comprehensive curriculum: Dive deep into relevant topics aligned with industry standards.
- Practical focus: Apply your learnings to real-world scenarios and situations.
This sought-after course is your key to a successful and lucrative career. Don’t miss out on this transformative opportunity. Enroll now and take your professional life to the next level!
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
Exam & Retakes
It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.
Certification
Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).
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 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 | 00:05:00 | ||
| 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 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 | 00:01:00 | ||
| 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 S9 | 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 | ||
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