Learn Artificial Intelligence/Machine Learning with Innovista
Description
This comprehensive course is designed to equip you with the knowledge and practical skills necessary to excel in the field of Data Science and Machine Learning. Starting with the fundamentals of Python, NumPy, and Pandas, you'll build a strong foundation in data manipulation and analysis.
We'll delve into data preprocessing techniques, including data cleaning, transformation, and feature engineering, and explore essential statistical concepts. You'll learn how to perform exploratory data analysis (EDA) to gain insights from your data, using descriptive statistics and visualization techniques. A real-world case study will provide hands-on experience in applying these concepts to solve practical problems.
Moving into the realm of Machine Learning, you'll gain an overview of core concepts and tools like Colab and Kaggle. We'll cover classification and ensemble methods, from Support Vector Machines (SVM) to XGBoost, and provide hands-on exercises to solidify your understanding. You'll also explore Neural Networks as classifiers, mastering the PyTorch framework through practical implementation.
The course will then venture into Unsupervised Learning techniques, including anomaly detection and recommendation systems, with hands-on projects to reinforce your learning. You'll get a theoretical understanding of Reinforcement Learning, complemented by practical exercises.
Next, we'll transition to Natural Language Processing (NLP), covering text preprocessing techniques and exploring CNN classifiers, sequence models, and transformers. You'll learn how to fine-tune large language models and work with tools like LangChain for Retrieval Augmented Generation. We'll also explore the creation of LLM agents, and discuss the risks and challenges associated with LLMs.
The course culminates with an introduction to Computer Vision (CV), covering image classification, CV architectures, and their applications. You'll learn about object tracking and recognition and delve into advanced topics in CV.
Throughout the course, you'll gain hands-on experience through practical exercises, case studies, and projects, enabling you to apply your knowledge to real-world scenarios. By the end of this course, you will be well-equipped to tackle a wide range of data science and machine learning challenges.
Key Takeaways:
You will master Python fundamentals for data science.
You will learn data preprocessing, EDA, and statistical analysis techniques.
You will gain expertise in classification, ensemble methods, and neural networks.
You will explore unsupervised learning, anomaly detection, and recommendation systems.
You will understand reinforcement learning concepts and applications.
You will learn NLP techniques and work with large language models.
You will be introduced to computer vision and its applications.
We'll delve into data preprocessing techniques, including data cleaning, transformation, and feature engineering, and explore essential statistical concepts. You'll learn how to perform exploratory data analysis (EDA) to gain insights from your data, using descriptive statistics and visualization techniques. A real-world case study will provide hands-on experience in applying these concepts to solve practical problems.
Moving into the realm of Machine Learning, you'll gain an overview of core concepts and tools like Colab and Kaggle. We'll cover classification and ensemble methods, from Support Vector Machines (SVM) to XGBoost, and provide hands-on exercises to solidify your understanding. You'll also explore Neural Networks as classifiers, mastering the PyTorch framework through practical implementation.
The course will then venture into Unsupervised Learning techniques, including anomaly detection and recommendation systems, with hands-on projects to reinforce your learning. You'll get a theoretical understanding of Reinforcement Learning, complemented by practical exercises.
Next, we'll transition to Natural Language Processing (NLP), covering text preprocessing techniques and exploring CNN classifiers, sequence models, and transformers. You'll learn how to fine-tune large language models and work with tools like LangChain for Retrieval Augmented Generation. We'll also explore the creation of LLM agents, and discuss the risks and challenges associated with LLMs.
The course culminates with an introduction to Computer Vision (CV), covering image classification, CV architectures, and their applications. You'll learn about object tracking and recognition and delve into advanced topics in CV.
Throughout the course, you'll gain hands-on experience through practical exercises, case studies, and projects, enabling you to apply your knowledge to real-world scenarios. By the end of this course, you will be well-equipped to tackle a wide range of data science and machine learning challenges.
Key Takeaways:
You will master Python fundamentals for data science.
You will learn data preprocessing, EDA, and statistical analysis techniques.
You will gain expertise in classification, ensemble methods, and neural networks.
You will explore unsupervised learning, anomaly detection, and recommendation systems.
You will understand reinforcement learning concepts and applications.
You will learn NLP techniques and work with large language models.
You will be introduced to computer vision and its applications.
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