Introduction to Python for Machine Learning
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1 Year
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Beginner
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Course Certificate
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86
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0
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2 hours, 29 minutes
Duration
Overview
Master the key skills and expertise required to use Python to develop machine learning models with our Introduction to Python for Machine Learning. Through this course, you will get the chance to develop a solid foundation in this area. You will get to elevate your skills and your career potential through this course.
This online training is divided into bite-sized modules for easy understanding. From this course, you will learn the main principles of machine learning. The informative modules will help you understand the basics of setting up Python & ML algorithms. Then, you will learn about simple linear regression and multiple linear regression. The course will also cover lessons on classification algorithms, clustering and recommender systems.
Once you complete the course, you will receive a certificate of achievement. This accredited certificate will enhance your employability in the relevant industry. Join now!
Course Preview
Learning Outcomes
- Familiarise yourself with the basic concepts of machine learning
- Learn the fundamentals of setting up Python & ML algorithms
- Receive detailed lessons on simple linear regression and multiple linear regression
- Enhance your knowledge of classification algorithms
- Improve your skills in clustering
- Explore the vital areas of the recommender system
Why Take This Course From John Academy?
- Affordable, well-structured and high-quality e-learning study materials
- Meticulously crafted, engaging and informative tutorial videos and materials
- Efficient exam systems for the assessment and instant result
- Earn UK & internationally recognised accredited qualification
- Easily access the course content on mobile, tablet, or desktop from anywhere, anytime
- Excellent career advancement opportunities
- Get 24/7 student support via email
What Skills Will You Learn from This Course?
Python for Machine Learning
Who Should Take This Introduction to Python for Machine Learning Course?
Whether you’re an existing practitioner or an aspiring professional, this course is an ideal training opportunity. It will elevate your expertise and boost your CV with key skills and a recognised qualification attesting to your knowledge.
Are There Any Entry Requirements?
This Introduction to Python for Machine Learning course is available to all learners of all academic backgrounds. But learners should be aged 16 or over to undertake the qualification. And a good understanding of the English language, numeracy, and ICT will be helpful.
Certificate of Achievement
After completing this course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates & Transcripts can be obtained either in Hardcopy at £14.99 or in PDF format at £11.99.
Career Path
This exclusive Introduction to Python for Machine Learning course will equip you with effective skills and abilities and help you explore career paths such as
- Data Scientist
- Machine Learning Engineer
- Artificial Intelligence Specialist
- Data Analyst
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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 00:02:00
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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 libraries & datasets 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 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 of k-means clustering in Python 00:01:00
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Importing the dataset 00:01:00
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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
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