Machine Learning Academy - ( in English)

Step into the realm of Python, the language that breathes life into AI. This lesson introduces you to the syntax, structure, and essential components of Python, empowering you with the coding prowess needed for the AI adventure ahead.

Dive deep into the numerical world with Numpy. This lesson equips you with the prowess to handle large, multi-dimensional arrays and matrices – a crucial skill in AI. Unleash the power of numerical computing to manipulate and analyze data with finesse.

S1-Data Exploration

Embark on a fascinating journey into the realm of Computer Vision. This lesson introduces you to a myriad of techniques employed in computer vision, providing a holistic understanding of how machines interpret and make sense of visual data.

Immerse yourself in a practical Colab session dedicated to implementing computer vision techniques. Through hands-on exercises, solidify your understanding of image processing, object detection, and other fundamental computer vision tasks.

Unravel the essence of features extraction in computer vision. This lesson delves into the methodologies for identifying and extracting significant features from visual data. Understand the importance of features in characterizing objects and scenes, laying the foundation for advanced computer vision applications.

Embark on a visual journey with Convolutional Neural Networks (CNNs). This lesson introduces the fundamental concepts behind CNNs, with a focus on the convolutional layer. Uncover the power of CNNs in extracting hierarchical features from visual data, making them a cornerstone in image analysis and beyond.

Explore the intricacies of padding and stride in the convolutional process. This lesson provides insights into how padding influences the spatial dimensions of feature maps, and how stride affects the movement of the convolutional kernel. Grasp the role of these parameters in shaping the output of the convolutional layer.

Delve deeper into the convolutional layer, unraveling its complexities and capabilities. This lesson extends your understanding of how different components within the convolutional layer contribute to feature extraction. Gain mastery in configuring and optimizing this critical layer for diverse applications.

Enter the realm of pooling layers, a crucial element in downsampling feature maps. This lesson demystifies pooling operations, such as max pooling and average pooling, illustrating their role in reducing spatial dimensions while retaining essential features. Understand how pooling enhances the robustness and efficiency of CNNs in image processing.

Elevate your understanding of image datasets with the art of data augmentation. This lesson explores techniques to diversify and expand your dataset, enhancing model robustness and generalization. Uncover how transformations like rotation, flipping, and scaling contribute to a more comprehensive training set.

Enter the world of renowned neural network architectures and the power of transfer learning. This lesson introduces you to popular architectures like VGG, ResNet, and Inception, while exploring how pre-trained models can be leveraged for new tasks. Discover the efficiency and effectiveness of utilizing knowledge from one domain to improve performance in another.

Unveil the complexities of object detection in images. This lesson provides a foundational understanding of how computer vision models identify and locate objects within an image. Explore techniques such as anchor boxes, non-maximum suppression, and region-based convolutional neural networks (R-CNNs) that contribute to accurate object detection.

Embark on a linguistic journey with tokenization, a fundamental natural language processing (NLP) technique. This lesson breaks down the process of tokenizing text into meaningful units, whether words, subwords, or characters. Understand how tokenization lays the groundwork for text analysis and language understanding.

Explore the transformation of words into numerical representations. This lesson introduces the concept of word vectors, illustrating how words are embedded in a continuous vector space. Uncover the significance of word representation in capturing semantic relationships between words and enhancing the understanding of textual data.

Delve into the world of embeddings, a cornerstone in NLP. This lesson expands on the concept of word vectors, exploring techniques like Word2Vec, GloVe, and FastText. Understand how embeddings capture semantic meaning, enabling machines to grasp context and relationships within language, leading to more nuanced language processing.

Embark on a journey into the temporal dimension of data with sequential data processing. This lesson introduces the concept of processing data over time, providing the foundation for understanding how sequential models can unravel patterns in time-series and sequential data.

Dive into the world of Recurrent Neural Networks (RNNs), designed to capture dependencies and patterns in sequential data. This lesson explores the architecture and mechanisms of RNNs, allowing you to model and predict sequential patterns effectively.

Delve deeper into the intricacies of sequential modeling with LSTM networks. This lesson uncovers the architecture of LSTMs, emphasizing their ability to capture long-term dependencies in data. Understand how LSTMs address the vanishing gradient problem, enhancing their efficacy in processing sequential data.

Explore the fusion of convolutional power with sequential data. This lesson introduces Convolutional Neural Networks (CNNs) adapted for sequential processing, showcasing their ability to extract features and patterns from sequences. Understand how CNNs contribute to efficient and effective sequential data analysis.

Embark on a journey through the dimension of time with time series data. This lesson introduces the unique characteristics and challenges posed by time-dependent datasets. Understand the essence of time series analysis and its applications in forecasting and trend identification.

Uncover the metrics tailored for evaluating time series data. This lesson explores metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and others specifically designed to gauge the performance of models in the context of time series forecasting.

Navigate the intricacies of preparing time series data for machine learning algorithms. This lesson covers techniques for handling temporal dependencies, dealing with missing values, and creating lag features. Gain insights into structuring time series data for effective model training.

Immerse yourself in a practical Colab session dedicated to time series analysis. This hands-on experience in Google Colab ensures a seamless transition from theory to implementation, allowing you to apply time series concepts to real-world scenarios and solidify your understanding of time-based data analysis.

Embark on your journey into JAX, a powerful library for high-performance numerical computing. This lesson introduces the fundamental concepts and syntax of JAX, paving the way for efficient and scalable computations. Grasp the basics of array handling, functions, and transformations that form the bedrock of JAX capabilities.

Deepen your understanding of JAX with a second installment of basics. This lesson builds upon the foundation laid in the first part, covering more advanced features and functionalities. Dive into the intricacies of JAX's autograd system, custom differentiation, and other advanced concepts that empower you to harness the full potential of JAX in numerical computing.

Embark on an exploration of the expansive JAX ecosystem. This lesson delves into the advanced features and extensions that complement JAX, providing a holistic view of its capabilities. From neural network libraries to optimization tools, this installment covers the ecosystem components that enhance the versatility of JAX.

Continue your journey through the rich JAX ecosystem with the second part of advanced features. This lesson goes beyond the basics, covering specialized libraries, frameworks, and utilities that seamlessly integrate with JAX. Explore how these ecosystem components synergize with JAX to address diverse computational challenges.

Embark on the intersection of JAX and Natural Language Processing (NLP). This lesson introduces you to the synergy between JAX's powerful numerical computing capabilities and the intricacies of processing and understanding human language. Explore the fundamental concepts and applications that bridge JAX and NLP.

Deepen your exploration of JAX's role in Natural Language Processing with the second installment. This lesson delves into advanced applications, models, and techniques that leverage JAX for processing and analyzing textual data. Gain insights into the transformative impact of JAX in enhancing NLP tasks and capabilities.

Embark on an exploration of the fusion between JAX and Computer Vision (CV). This lesson introduces the powerful synergy between JAX's numerical computing prowess and the intricate world of visual data processing. Dive into the foundational concepts and applications that unite JAX with the challenges and opportunities of computer vision.

Continue your journey into the integration of JAX with Computer Vision with the second installment. This lesson delves into advanced applications, models, and techniques that leverage JAX for processing and understanding visual information. Explore how JAX elevates the field of Computer Vision, enabling efficient and scalable solutions to complex visual tasks.