Machine Learning with Python Course
Machine Learning with Python Course
- Interactive video lectures by industry experts
- Developed by qualified first aid professionals
- Instant e-certificate and hard copy dispatch by next working day
- Fully online, interactive course with audio voiceover
- Self paced learning and laptop, tablet, smartphone friendly
- 24/7 Learning Assistance
- Discounts on bulk purchases
Main Course Features:
- Thorough coverage of machine learning concepts, algorithms, and techniques
- Hands-on projects and coding exercises to reinforce learning
- Exploration of popular machine learning libraries such as scikit-learn and TensorFlow
- Implementation of supervised and unsupervised learning algorithms for classification, regression, and clustering tasks
- Guidance on data preprocessing, feature engineering, and model evaluation
- Real-world case studies and examples to illustrate machine learning applications
- Access to resources and tools for building, testing, and deploying machine learning models
- Supportive online community for collaboration and assistance throughout the course
Learning Outcomes
- Understand fundamental machine learning concepts and techniques
- Implement machine learning algorithms and models using Python
- Perform data preprocessing, feature engineering, and model evaluation
- Develop predictive models for classification and regression tasks
- Apply unsupervised learning algorithms for clustering and dimensionality reduction
- Deploy machine learning models in real-world applications
- Debug and optimize machine learning models for improved performance
- Stay updated with the latest advancements and trends in machine learning with Python.
Who Should Take This Course
- Aspiring data scientists and machine learning enthusiasts looking to start their journey in machine learning with Python
- Programmers and developers interested in expanding their skill set to include machine learning for data analysis and prediction
- Students and professionals seeking to enhance their career prospects with machine learning expertise
Certification
Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post. All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.
Assessment
At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.
Course Curriculum
Introduction to Course 
00:06:00 What is Machine Learning 00:05:00 Life Cycle 00:05:00
Introduction to Numpy Library 00:07:00 Creating Arrays from Scratch 00:06:00 Creating Arrays from Scratch Continued 00:05:00 Array Indexing and Slicing 00:10:00 Numpy Array Functions and Shape Modification 00:09:00 Mathematical Operations on Numpy Arrays 00:07:00 Introduction to Pandas Library 00:10:00 Working with Pandas DataFrames 00:07:00 Slicing and Indexing with Pandas 00:07:00 Create DataFrame and Explore Dataset 00:08:00 Data Analysis with Pandas DataFrame 00:12:00 Other Useful Methods in Pandas Library 00:04:00
Introduction to Matplotlib 00:06:00 Customizing Line Plots 00:08:00 Create Plot Using DataFrame 00:09:00 Standard Scaler to Scale the Data 00:06:00 Encoding Categorical Data 00:11:00 Sklearn Pipeline and Column Transformer 00:12:00 Evaluation Metrics in Sklearn 00:07:00 Linear Regression 00:12:00 Evaluation of Linear Regression Model 00:10:00
Polynomial Regression 00:13:00 Polynomial Regression Continued 00:13:00 Sklearn Pipeline Polynomial Regression 00:11:00 Decision Tree Classifier 00:13:00 Decision Tree Evaluation 00:07:00 Random Forest 00:06:00 Support Vector Machines 00:09:00 K-means Clustering 00:04:00 K-Means Clustering – Hands On 00:12:00 Data Loading and Analysis 00:06:00 Dimensionality Reduction with PCA 00:09:00 Hyper Parameter Tuning 00:09:00 Summary 00:02:00
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