Enroll in the "Deep Learning MasterClass" and embark on an exciting journey into the realm of deep learning. Whether you're a seasoned data scientist or a novice eager to dive into the world of artificial intelligence, this course equips you with the essential knowledge and skills to excel in the ever-evolving domain of deep learning.
What Will You Learn?
- Gain a deep understanding of the core concepts of deep learning.
- Explore data manipulation and analysis using Numpy and Pandas, essential tools for deep learning.
- Learn data visualization techniques with Matplotlib and Seaborn to communicate insights effectively.
- Master the fundamentals of machine learning, setting the stage for deep learning.
- Dive into the world of Artificial Neural Networks (ANN) and understand how they simulate human learning.
- Discover Convolutional Neural Networks (CNN) for image recognition and processing.
- Explore Recurrent Neural Networks (RNN) and their applications in sequential data analysis.
Who Should Take The Course?
- Data scientists and machine learning enthusiasts eager to dive into the realm of deep learning.
- Developers interested in incorporating deep learning into their applications and projects.
- Researchers and academics looking to expand their knowledge of artificial neural networks.
- Anyone curious about the cutting-edge technology transforming industries like healthcare, finance, and autonomous systems.
Requirements
- A basic understanding of machine learning concepts is helpful but not mandatory.
- Access to a computer for hands-on exercises and practical application.
- Python programming knowledge is beneficial as the course heavily relies on Python for deep learning.
- Enthusiasm to explore and excel in the field of deep learning and artificial neural networks.
Course Curriculum
-
- Introduction 00:06:00
-
- Introduction to Numpy 00:06:00
- Creating Arrays 00:10:00
- Shape and Reshape 00:11:00
- Indexing 00:08:00
- Introduction to Pandas 00:04:00
- Pandas Series 00:04:00
- DataFrame 00:06:00
- ReadCSV 00:03:00
- Analyze DataFrames 00:06:00
- Machine Learning Introduction 00:06:00
- Supervised Machine Learning 00:04:00
- Unsupervised Machine Learning 00:03:00
- Train Test Split 00:03:00
- Machine Learning LifeCycle 00:05:00
- Working with Missing Values 00:09:00
- Feature Scaling 00:07:00
- Feature Encoding 00:11:00
- Model Evaluation Metrics 00:07:00
- CNN Introduction 00:09:00
- Implementation of CNN using Keras and Tensorflow 00:13:00
- Order Certificate 00:05:00
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