Practical experience with Data Science Projects
Interested to know the best way to get practical experience in Data Science for beginners?
Indulge in simple projects to master the key insights of Data Science:
1. Predictive modeling with linear regression: A fundamental technique in data science involving problem definition, Data Collection & pre-processing, Exploratory Data Analysis, Feature Selection, Model Building, Model Evaluation, Model Interpretation and Prediction
2. Sentiment analysis on social media data: A process of determining the emotional tone behind a series of words. It’s particularly useful for analyzing social media data to understand public sentiment towards a topic, brand, or event.
3. Recommendation system for movies or books: Recommendation systems are used to suggest items to users based on various factors like user preferences, past behavior, and item characteristics. Three types of recommendation systems are Collaborative Filtering, Content Based filtering and Hybrid Systems.
4. Image classification with convolutional neural networks: Image classification with convolutional neural networks (CNNs) involves training a neural network to recognize and categorize images into predefined classes.
5. Customer churn prediction model: Customer churn prediction involves creating a model to identify which customers are likely to stop using a company's products or services.
https://1stepgrow.com/advance-data-science-and-artificial-intelligence-course/
Indulge in simple projects to master the key insights of Data Science:
1. Predictive modeling with linear regression: A fundamental technique in data science involving problem definition, Data Collection & pre-processing, Exploratory Data Analysis, Feature Selection, Model Building, Model Evaluation, Model Interpretation and Prediction
2. Sentiment analysis on social media data: A process of determining the emotional tone behind a series of words. It’s particularly useful for analyzing social media data to understand public sentiment towards a topic, brand, or event.
3. Recommendation system for movies or books: Recommendation systems are used to suggest items to users based on various factors like user preferences, past behavior, and item characteristics. Three types of recommendation systems are Collaborative Filtering, Content Based filtering and Hybrid Systems.
4. Image classification with convolutional neural networks: Image classification with convolutional neural networks (CNNs) involves training a neural network to recognize and categorize images into predefined classes.
5. Customer churn prediction model: Customer churn prediction involves creating a model to identify which customers are likely to stop using a company's products or services.
https://1stepgrow.com/advance-data-science-and-artificial-intelligence-course/
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