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Educational Background:
Programming Skills:
Mathematics and Statistics:
Additional Skills:
Experience with data analysis, data manipulation, and data visualization techniques.
Understanding of fundamental ML/AI concepts, algorithms, and techniques.
Problem-solving skills and the ability to think critically and creatively.
Data Preprocessing: This module involves cleaning, transforming, and preparing raw data for analysis. It includes tasks such as data cleaning, data normalization, handling missing values, and feature scaling.
Supervised Learning: In supervised learning, the algorithm learns from labeled data, where each example is paired with the correct output. This module includes algorithms like linear regression, logistic regression, decision trees, support vector machines, and neural networks.
Unsupervised Learning: Unsupervised learning involves learning patterns from unlabeled data. This module includes algorithms such as clustering (e.g., K-means clustering, hierarchical clustering), dimensionality reduction (e.g., principal component analysis), and association rule learning.
Deep Learning: Deep learning is a subset of ML that focuses on neural networks with multiple layers. This module includes topics such as convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for sequence data, and generative adversarial networks (GANs) for generating new data samples.
Reinforcement Learning: Reinforcement learning involves training agents to make sequential decisions by interacting with an environment. This module includes algorithms such as Q-learning, deep Q-networks (DQN), and policy gradient methods.
Natural Language Processing (NLP): NLP is a subfield of AI focused on enabling computers to understand, interpret, and generate human language. This module includes tasks such as text classification, sentiment analysis, named entity recognition, and machine translation.
Computer Vision: Computer vision involves enabling computers to interpret and understand visual information from the real world. This module includes tasks such as object detection, image classification, image segmentation, and image generation.
Model Evaluation and Hyperparameter Tuning: This module focuses on techniques for evaluating ML models' performance and fine-tuning model parameters to improve performance. It includes techniques such as cross-validation, grid search, and randomized search.
Instructor Of Economy
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jan 17, 2022 at 10:43 am
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