Course Description
Take advantage of our comprehensive Machine Learning with Python course on the power of machines to learn and make predictions.
This course delves into the fundamentals of machine learning, giving you the skills to build intelligent systems that can analyze data, identify patterns, and make data-driven decisions.
With manual Python programming, you can develop important machine learning algorithms such as linear regression, decision trees, and k-nearest neighbors. Learn data preprocessing techniques, model evaluation methods, and explore advanced topics such as natural language processing and deep learning.
This Machine Learning with Python course is designed for both beginners and those with some programming experience. No prior Machine Learning skills are necessary, we will go through Python and configure the necessary libraries using ML algorithms.
Learning Outcome
- Develop the ability to choose appropriate ML algorithms for different scenarios
- Gain hands-on experience in solving real-world machine learning problems
- Learn data preprocessing, model evaluation, and selection strategies
- Master various regression, classification, and clustering techniques
- Develop practical skills in implementing ML algorithms using Python and key libraries
- Understand fundamental machine learning concepts and algorithms
Why should you choose this course
- Study at your own pace
- Full Tutor support on weekdays (Monday – Friday)
- CPD Qualification Standards and accredited
- Efficient exam system, assessment, and instant results
- Gain professional skills and better earning potential
- No hidden fees or exam charges
- Our customer support team is always ready to help you
Accredited Certification
After successfully completing the Machine Learning with Python course, you will be able to get the and internationally accepted certificate to share your achievement with potential employers or include it in your CV.
Career path
- Business Intelligence Analyst
- AI Research Scientist
- Data Scientist
- Machine Learning Engineer
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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 00:02:00
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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 S7 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 00:01:00
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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 S9 00:02:00
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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 00:02:00
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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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This course includes:
01 Year of full Course access
Endorsed Certificate of Compleiton
83 Units
0 Quiz
2 hours, 27 minutes of total Unit + Quiz duration
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