This comprehensive course covers the full data science lifecycle — from data collection and cleaning to analysis, visualization, and machine learning. Students will gain hands-on experience with tools like Python, Pandas, NumPy, SQL, and Scikit-learn. Designed for beginners and professionals alike, the course equips learners with practical skills to solve real-world problems and prepare for roles like Data Analyst, Data Scientist, or ML Engineer.
After Deep Learning, learners typically explore areas like Natural Language Processing (NLP), Computer Vision, Reinforcement Learning, Big Data tools, and MLOps.
A solid understanding of Deep Learning fundamentals is helpful, especially with neural network architectures used in NLP and CV tasks.
Frameworks like TensorFlow, PyTorch, Apache Spark, and cloud platforms (AWS, GCP, Azure) are commonly used in advanced applications.
MLOps (Machine Learning Operations) involves deploying, monitoring, and maintaining ML models in production. It ensures scalable and reliable AI systems.
Explainable AI helps understand how models make decisions, ensuring transparency, trust, and ethical compliance in AI systems.
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