Embark on a transformative journey into the realms of Machine Learning, Data Science, and Generative AI with Python. This comprehensive course "Machine Learning, Data Science and Generative AI with Python" equips you with the tools and knowledge needed to navigate the dynamic landscape of modern technology. Whether you're delving into the intricacies of Deep Learning or exploring the cutting-edge advancements in Generative AI, each module is meticulously crafted to demystify complex concepts and empower practical application.
From understanding the fundamental principles of Python to delving into the intricacies of TensorFlow and Keras, this course "Machine Learning, Data Science and Generative AI with Python" caters to both beginners and seasoned programmers alike. Dive deep into Deep Learning techniques such as MLPs, CNNs, and RNNs, and explore real-world applications through practical exercises. With a focus on industry-relevant skills and accessible learning, each module is designed to ensure a seamless transition from theory to practice, enabling you to tackle real-world data challenges with confidence.
Experience the future of technology firsthand as you delve into the latest advancements in Generative AI, including transformers, GPT, and the OpenAI API. Through hands-on projects and real-life scenarios, you'll gain invaluable insights into the mechanics of modern AI, preparing you to excel in the competitive tech industry. Whether you're aiming to enhance your career prospects or simply driven by a passion for innovation, this course "Machine Learning, Data Science and Generative AI with Python" is your gateway to unlocking the full potential of Machine Learning and AI with Python.
Machine Learning, Data Science and Generative AI with Python Table of Contents:
- Create artificial neural networks efficiently with TensorFlow and Keras.
- Deploy machine learning at scale with Apache Spark's MLLib.
- Utilize deep learning to classify images, data, and sentiments effectively.
- Make accurate predictions using various regression techniques such as linear, polynomial, and multivariate regression.
- Visualize data using Matplotlib and Seaborn for insightful analysis.
- Explore reinforcement learning principles and build practical applications like a Pac-Man bot.
- Implement classification algorithms including K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA.
- Employ train/test and K-Fold cross-validation methods to optimize and fine-tune your models.
- Develop a movie recommender system using collaborative filtering techniques like item-based and user-based filtering.
- Ensure data cleanliness by effectively handling outliers.
- Design and evaluate A/B tests using T-tests and P-values for robust statistical analysis.
Who is this course for?
- Software developers or programmers aiming for a data science or machine learning career will benefit greatly.
- Technologists curious about deep learning's mechanics will find valuable insights.
- Data analysts from non-tech fields transitioning into tech can learn data analysis through coding, with some coding or scripting experience required.
- If you lack coding or scripting experience, start with an introductory Python course before considering this one.
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