Top Free Machine Learning Using Python complete course certification - ( Free Course )

This is a free course on Diploma in Machine Learning from beginner to advanced level.

Before we see what this course entails, download our course brochure to help you exprore the recommended learning path for this course.

Download Course Brochure

Embark on a transformative learning journey with our comprehensive FREE Machine Learning Using Python course certification.

Dive into the fundamentals of Python programming and unlock the power of machine learning algorithms.

Gain hands-on experience through real-world projects, equipping yourself with the skills to analyse data, build predictive models, and make data-driven decisions.

Elevate your expertise in one of the most sought-after fields with this immersive and practical FREE certification program.

Understanding Machine Learning.

Machine learning is a subset of artificial intelligence enabling computers to learn from data without explicit programming.

It’s used in various fields, enhancing efficiency and decision-making. Taking a machine learning course is valuable as it:

Equips individuals to analyse vast datasets for accurate predictions.
Meets the growing demand for machine learning professionals across industries.
Provides a competitive edge in a technology-driven job market, fostering innovation and problem-solving skills.

About Course

This free online course introduces fundamental concepts of artificial intelligence and machine learning, catering to absolute beginners with no prerequisites.

It demystifies the ML black box, making these complex fields accessible and inviting for those without prior background in mathematics or programming.

Start the course now with no specific requirements.

What Will You Learn?

Outline the connection between artificial intelligence, machine learning and deep learning
Describe the concept of supervised and unsupervised learning
Recognise supervised learning building blocks of features and labels and provide examples
Explain the process of training a model in supervised learning
Recognise the challenges of under fitting and overfitting
Identify classification and regression tasks
Outline clustering and dimension reduction
Describe reinforcement learning

Course Modules and Lessons

Machine Learning Path/ Roadmap

Module 1: Introduction to Machine Learning level 1

Module 2: Machine Learning background

Module 3. Machine Learning terminologies, models and Features.

Module 4. Classifications of Machine learning systems

Module 5. Machine Learning Using Python and Pandas level 2.

Module 6. Pandas library in Machine Learning

Module 7. Data visualization with python level 3

Module 8. Advanced Machine Learning

Module 9. Diploma in Machine Learning level 4

Module 10. Diploma in building high accuracy model with core Machine Learning

Module 11. Machine Learning projects

Click here to register free

Material available for this course Includes

Full Access to 16000+ online courses: Gain comprehensive knowledge with Full Access to over 16,000 online courses covering diverse subjects and disciplines.
Play & Pause Course Viewing: Enjoy the flexibility of Play & Pause Course Viewing, allowing you to learn at your own pace and convenience.
HD Recorded Lectures: Immerse yourself in the educational experience with HD Recorded Lectures, ensuring clarity and detail in your learning materials.
Access on Mobile/PC/Tablet: Seamlessly access courses on various devices, including Mobile, PC, and Tablet, for a truly versatile and on-the-go learning experience.
Access on Mobile/PC/Tablet: Test your understanding through engaging Quizzes and Real Projects, reinforcing your learning and practical application of acquired skills.
Certificate of Completion: Showcase your achievements with a Certificate of Completion, validating your commitment and expertise in the completed courses.

Requirements For This course

These are general requirements; however, it’s crucial to know that everything will be covered in this course.

Mathematics Foundation:

Linear Algebra
Calculus
Probability and Statistics

Programming Skills:

Proficiency in Python is commonly required.

Computer Science Concepts:

Basic understanding of algorithms and data structures.

Access to a smartphone, laptop and PC

A Laptop or a computer with a least 2GB RAM. If you possess a smartphone you can take the course and practice later.

A willing mind.

Be willing to learn, correct mistakes and develop from them.

Who is this course for ( Audience )

Anyone Willing to learn how to Deploy machine Learning Model
Beginner Machine Learning or Data Science Professional Willing to Enhance their Skills
Intermediate Machine Learning or Data Science Professional Willing to Enhance their Skills
Advance Machine Learning or Data Science Professional Willing to Enhance their Skills

Career of a Machine Learning expert

A machine learning expert can pursue careers in fields such as data science, artificial intelligence research, software development, and machine learning engineering. They may work in industries like finance, healthcare, technology, or academia.

Data Scientist: Analyzing and interpreting complex data sets to inform business decision-making. Developing algorithms and models to extract insights.
Machine Learning Engineer: Designing and implementing machine learning models, algorithms, and systems. Collaborating with software engineers to deploy and integrate solutions.
Artificial Intelligence Researcher: Conducting research to advance the field of AI, exploring new algorithms, techniques, and models to solve challenging problems.
Software Developer (with ML focus): Integrating machine learning components into software applications, working on projects that leverage AI for various functionalities.
Natural Language Processing (NLP) Engineer: Specializing in the development of algorithms and models for understanding, interpreting, and generating human language.
Computer Vision Engineer: Focusing on developing algorithms and systems for image and video analysis, enabling machines to interpret and understand visual information.
AI Consultant: Advising businesses on how to implement AI and machine learning solutions to address specific challenges and improve processes.
Data Engineer: Building and maintaining the architecture for collecting, storing, and processing large volumes of data, crucial for machine learning applications.
Robotics Engineer: Integrating machine learning algorithms into robotic systems, enabling them to perceive their environment and make autonomous decisions.
Quantitative Analyst (Quant): Applying machine learning and statistical models to analyze financial data, make predictions, and inform investment strategies in the finance industry.
Healthcare Data Scientist: Utilizing machine learning techniques to analyze medical data, identify patterns, and contribute to advancements in diagnosis and treatment.
Academic Researcher in ML/AI: Conducting research in academia to advance the understanding and application of machine learning and artificial intelligence.