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Overview
Are you ready to enhance your career and job prospects in the exciting field of Machine Learning? Our “Machine Learning with Python Course” is your gateway to mastering this transformative technology. Dive into the world of Machine Learning and gain valuable insights into its foundational concepts through a structured curriculum. From understanding the basics of Machine Learning to harnessing the power of Numpy and Matplotlib libraries, this course equips you with the knowledge you need to excel in this dynamic field. Explore the intricacies of Polynomial Regression and unlock the potential of data-driven decision-making. With Machine Learning at the forefront, elevate your career and stay ahead in the competitive job market.
Learning Outcomes:
By the end of this course, you will:
- Gain a deep understanding of Machine Learning principles and techniques.
- Master the Numpy library for efficient data manipulation and analysis.
- Create insightful data visualizations using Matplotlib.
- Develop expertise in Polynomial Regression for predictive modeling.
- Apply Machine Learning concepts to real-world problems.
- Be prepared to pursue advanced Machine Learning specializations.
Description
Our “Machine Learning with Python Course” offers a comprehensive journey into the realm of Machine Learning. With a focus on theoretical foundations, this course provides you with a strong knowledge base to tackle complex problems in data analysis and prediction. You’ll delve into essential topics such as Numpy and Matplotlib, gaining the skills necessary for data manipulation and visualization. The course culminates in an exploration of Polynomial Regression, a vital tool in predictive modeling. Whether you’re a beginner or looking to reinforce your understanding of Machine Learning, this course is designed to empower you with the knowledge you need.
Why Choose Us?
- This course is accredited by the CPD Quality Standards.
- Lifetime access to the whole collection of the learning materials.
- Online test with immediate results.
- Enroling in the course has no additional cost.
- You can study and complete the course at your own pace.
- Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.
Certificate of Achievement
Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £9.99.
Who Is This Course For?
- Aspiring data scientists and analysts.
- Professionals seeking to transition into Machine Learning roles.
- Students and enthusiasts passionate about data-driven solutions.
- Anyone interested in understanding the foundations of Machine Learning.
Requirements
The Machine Learning with Python Course course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of Machine Learning with Python Course, without any fuss.
Career Path
- Machine Learning Engineer
- Data Scientist
- AI Researcher
- Data Analyst
- Business Intelligence Analyst
- Software Developer
- Statistician
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Course Curriculum
| Section 01: Introduction | |||
| Introduction to Course | 00:06:00 | ||
| What is Machine Learning | 00:05:00 | ||
| Life Cycle | 00:05:00 | ||
| Section 02: Numpy Library | |||
| 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 | ||
| Section 03: Matplotlib | |||
| 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 | ||
| Section 04: Polynomial Regression | |||
| 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 | ||
| KMeans 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 | ||
| Order Your Certificate | |||
| Order Your Certificate | 00:00:00 | ||
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