MACHINE LEARNING
Join our comprehensive Machine Learning course to learn data-driven decision-making, algorithm development, and deployment using Python, TensorFlow, and scikit-learn.
Total Courses
8
Level
Beginner
Duration
20 Days
Schedule
Flexible
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Key Features Of This Course
SKILLS COVERED
Course Details
1. Machine learning and its significance
2. Types of machine learning
1. Matplotlib
2. Seaborn
3. Plotly
1. Linear Regression
2. Logistic Regression
3. Gradient Boosting
4. Decision Tree
5. Time Series
1. CNN
2. LSTM
3. RNN
4. Reinforcement Learning
1. Regression
2. Logistic Regression
3. Decision Trees
4. Support Vector Machines
1. Basics of Artificial Neural Networks (ANNs)
2. Deep Learning Architectures: Convolutional Neural Networks (CNNs)
3. Recurrent Neural Networks (RNNs)
4. Deep Learning Frameworks
1. Text Preprocessing and Tokenization
2. Sentiment Analysis
1. Strategies for deploying Machine Learning Models in production
2. Containerization and Cloud Deployment
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Frequently Asked Questions
Machine Learning can be broadly categorized into three types:
1. Supervised Learning: Involves training a model on labeled data, where the model learns to map input data to the desired output.
2. Unsupervised Learning: Involves training a model on unlabeled data, where the model learns patterns and structures from the data without specific output labels.
3. Reinforcement Learning: Involves training a model to make sequences of decisions. The model learns by receiving feedback from its actions and adjusting its strategy to maximize rewards.
These types of Machine Learning techniques cater to different tasks and data scenarios, enabling diverse applications in various fields.
Semi-supervised learning is a type of machine learning where the model is trained on a combination of labeled and unlabeled data. The labeled data has both input features and corresponding output labels, while the unlabeled data only has input features. The model learns from the labeled data to generalize and make predictions on the unlabeled data, thereby improving performance by leveraging a larger pool of unlabeled data alongside limited labeled data.
Bias and variance are terms used to describe the performance of a machine learning model:
1. Bias: Bias measures how well the model approximates the true underlying relationship between inputs and outputs. High bias indicates that the model is too simplistic and may underfit the data, failing to capture the patterns.
2. Variance: Variance measures the model’s sensitivity to small fluctuations in the training data. High variance suggests that the model is overly complex and may overfit the training data, capturing noise rather than the underlying patterns.
Balancing bias and variance is crucial in machine learning to achieve a model that generalizes well to unseen data.