Machine Learning A-Z From Foundations to Deployment
What you'll learn
- Know which Machine Learning model to choose for each type of problem
- Make powerful analysis
- Have a great intuition of many Machine Learning models
- Master Machine Learning on Python & R
Requirements
- Just some high school mathematics level.
Description
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries simply.
Over 900,000 students worldwide trust this course.
We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course can be completed by either doing either the Python tutorials, R tutorials, or both - Python & R. Pick the programming language that you need for your career.
This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured in the following way:
Part 1 - Data Preprocessing
Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Part 4 - Clustering: K-Means, Hierarchical Clustering
Part 5 - Association Rule Learning: Apriori, Eclat
Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.
Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your models.
This course includes both Python and R code templates which you can download and use on your projects.
Who this course is for:
- Anyone interested in Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
Instructor
- 4.0 Instructor Rating
- 1,142 Reviews
- 72,373 Students
- 45 Courses
Hello, I'm Akhil Vydyula — Lead Data Engineer at Publicis Sapient, and Former Senior Data Scientist at PwC.
With over 5 years of rich industry experience and a strong focus on the BFSI sector, I’ve led and delivered end-to-end data and analytics solutions that power strategic decisions and transform business outcomes.
At Publicis Sapient, I currently lead complex data engineering initiatives, leveraging my deep expertise in cloud-native platforms like AWS to architect robust, scalable data pipelines. My work spans across developing and optimizing ETL workflows using PySpark and Spark SQL, orchestrating data flows via EMR, Step Functions, and EventBridge, and driving real-time and batch data processing into PostgreSQL (RDS/Redshift) environments. I've also implemented AWS Glue and DMS to seamlessly replicate and transform large-scale on-premise data into cloud-native formats.
Previously, at PwC, I specialized in advanced analytics and machine learning within the Advisory Consulting practice. I’ve built and deployed predictive models using statistical analysis, regression, classification, clustering, and text mining—particularly for risk identification and decision modeling. My passion lies in transforming raw data into actionable insights through effective data storytelling and visualization.
In parallel to my corporate career, I bring over 5 years of teaching experience, mentoring hundreds of aspiring data professionals. I’m deeply committed to helping students break into the data industry by translating real-world challenges into practical learning experiences.
Whether it's building data pipelines, uncovering business insights, or shaping the next generation of data talent, I thrive at the intersection of technology, strategy, and impact.
Let’s connect if you're passionate about data, eager to learn, or looking to collaborate on meaningful, data-driven initiatives.