Building AI Projects Master Machine Learning & Deep Learning
What you'll learn
- Master Python, Machine Learning, Deep Learning, and Time Series techniques by implementing real-world projects.
- Gain practical experience through 25+ hours of video content and downloadable resources.
- Build 5 hands-on Data Science projects with Jupyter Notebooks for a comprehensive learning experience.
- Understand the theory and practical applications of ML and DL, setting you up for success in the industry.
Requirements
- No prior programming knowledge is required. This course is beginner-friendly.
- Basic knowledge of mathematics is helpful but not necessary. You’ll learn the core concepts as we progress.
- Ideal for those with an engineering, science, mathematics, or statistics background who want to delve into machine learning.
Description
Unlock the Power of AI: From Beginner to Advanced Machine Learning & Deep Learning Projects
Are you ready to dive into the world of Artificial Intelligence and master Machine Learning and Deep Learning? Whether you're just starting or want to expand your AI skills, this comprehensive course is designed to guide you through hands-on projects that you can use to showcase your abilities in the real world.
Key Highlights of the Course:
Hands-On, Project-Based Learning: This is not just a theory-heavy course. You’ll be actively building and deploying AI models that solve real-world problems. Each module introduces a new project, ensuring you gain practical experience while learning.
Perfect for Beginners to Experts: Start with the basics and move towards advanced concepts at your own pace. Whether you're new to AI or looking to deepen your knowledge, this course will meet you where you are and help you grow.
Practical AI Applications: Learn to apply AI in fields like image classification, natural language processing (NLP), recommendation systems, and more, giving you a diverse skillset that can be applied to various industries.
Master Deep Learning: Learn cutting-edge techniques like neural networks, CNNs (Convolutional Neural Networks), and RNNs (Recurrent Neural Networks) to handle complex tasks, opening up exciting opportunities in AI development.
Deployment & Scalability: Learn to take your models from development to deployment. Understand how to use cloud platforms and scaling strategies to make your AI solutions accessible and efficient.
Collaborative Learning: Engage with fellow learners, share your progress, and collaborate on projects, creating a supportive and dynamic learning environment.
Expert Mentorship: Get valuable insights and feedback from experienced instructors to improve your projects and enhance your learning experience.
Who This Course Is For:
Beginners in Python and AI: No prior experience needed! This course is perfect for those new to programming and AI.
Career Changers: If you're looking to switch into Data Science, Machine Learning, or AI from another field, this course will provide you with the foundational knowledge and practical experience needed to start your career.
Job Seekers & Freshers: Get a strong start in AI and Machine Learning with real-world projects that will enhance your resume and job prospects.
AI Enthusiasts & Developers: If you have some background in programming and want to deepen your understanding of AI through hands-on projects, this course will help you grow your portfolio.
What You’ll Achieve by the End of This Course:
Portfolio of AI Projects: Complete real-world projects that demonstrate your ability to build, deploy, and scale machine learning and deep learning models.
Job-Ready Skills: Whether you’re aiming for a career in AI, Data Science, or Machine Learning Engineering, you'll have the skills and confidence to succeed.
Practical Knowledge: Gain deep, hands-on knowledge of AI tools, techniques, and strategies to apply in professional settings.
Who this course is for:
- Beginner Python developers looking to explore Data Science and Machine Learning.
- Career changers from non-technical fields who want to break into Data Science.
- Freshers eager to start a career as a Machine Learning Engineer.
- Aspiring Data Scientists wanting a practical approach to ML and DL.
Instructor
- 4.1 Instructor Rating
- 989 Reviews
- 63,734 Students
- 48 Courses
Hello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.
My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.
I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.
Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.
Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.
I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!