2. L E A R N
Step 1
What You have To DO ?
Internship Roadmap
A P P L Y
Step 2
E N G A G E
Step 3
S H A R E
Step 4
3. Topperworld is a project-based learning organization that aims to
build a strong tech future for all developers.
We at Topperworld strongly believe that practical knowledge can
make a student more successful in their career.
Our aim is to help others gain personal and professional skills for
their careers.
Topperworld is primarily for students who want to start a career in a
technical field but have lack of basic knowledge.
We are a officialy MSME registered e-learning startup company.
4. To receive fast updates about internships, interns must follow us on
social media.
Selected interns are need to join WhatsApp group as well as
Telegram group for Task Updates .
All chosen interns must complete three task in order to be eligible
for a Certificate of Completion.
To be eligible for a Letter of Recommendation (LOR), complete all
of the assigned tasks.
If we discover that your code contains plagiarism, we will fire you by
way of the internship.
5. Maintain a unique GitHub repository (for instance, TW
Internship).
Add all the task codes and projects to the GitHub repository.
Upload the task videos with explanations to LinkedIn and tag
@Topperworld.
All interns must update their LinkedIn profiles.
Change the title of your LinkedIn profile to reflect your position,
such as "Data Science intern at Topperworld."
Update your LinkedIn Experience with Topperworld.
6. TASK 1
Detection Of Road
Lane Line
TASK 2 TASK 3
1 2 3
Movie Recommend
System
Fake News
Detection
7. Dataset: Collect a diverse and labeled dataset of road images or videos with annotated
lane lines.
Computer Vision Libraries: Utilize appropriate libraries or frameworks (e.g., OpenCV,
TensorFlow, PyTorch) for image processing and deep learning.
Model Architecture: Choose or design a suitable deep learning model for lane line
detection.
Detection Of Road Lane Line
The objective of the road lane line detection project in data science is to develop a
computer vision system that can accurately identify and localize lane lines on the road
from input images or video streams. This system is crucial for various applications, such
as autonomous vehicles, advanced driver-assistance systems (ADAS), and road safety
analysis.
8. Detection Of Road Lane Line
Training Hardware: Access to GPUs (Graphics Processing Units) to accelerate the
training process for deep learning models, if applicable.
Evaluation Metrics: Define relevant metrics to evaluate the performance of the lane
line detection system (e.g., accuracy, precision, recall).
Load and preprocess the dataset, including resizing, normalization, and data
augmentation techniques to improve model generalization.
Choose an appropriate lane line detection model, such as a convolutional neural
network (CNN) or a combination of CNN and recurrent neural network (RNN) for
sequence learning.
Implement and train the chosen model on the preprocessed dataset, optimizing
hyperparameters and loss functions.
1. Data Preprocessing:
2. Model Selection and Development:
9. Detection Of Road Lane Line
Split the dataset into training and validation sets to assess the model's
performance.
Evaluate the model using defined metrics to fine-tune and optimize its
performance.
Apply post-processing techniques (e.g., smoothing, filtering) to refine the detected
lane lines and reduce noise.
If the application requires real-time lane line detection, optimize the model for
efficient inference and deploy it on appropriate hardware (e.g., GPUs, FPGAs).
3. Validation and Performance Evaluation:
4. Post-processing:
5. Real-time Inference (Optional):
10. The objective of the movie recommendation system project in data science is to
build a personalized and accurate recommendation system that suggests movies to
users based on their preferences, viewing history, and behavior. The system aims to
enhance user engagement, satisfaction, and retention on a movie streaming
platform.
Movie Recommend System
Movie Dataset: Obtain a comprehensive dataset of movies, including attributes like
genre, actors, directors, release year, and user ratings.
User Interaction Data: Gather data on user interactions, such as movie ratings,
watch history, likes, and dislikes.
11. Movie Recommend System
Collaborative Filtering or Content-Based Algorithms: Implement recommendation
algorithms like collaborative filtering (user-based or item-based) or content-based
filtering to generate movie recommendations.
Data Preprocessing: Clean and preprocess the movie and user data, handling missing
values and ensuring data consistency.
Evaluation Metrics: Define appropriate metrics to evaluate the performance of the
recommendation system (e.g., accuracy, precision, recall, F1-score).
Gather movie data from various sources and combine it into a structured dataset.
Collect user interaction data, ensuring user privacy and consent.
Preprocess the data to handle missing values, remove duplicates, and encode
categorical features.
1.Data Collection and Preprocessing:
12. Movie Recommend System
Perform data exploration and visualization to gain insights into movie distributions,
user behaviors, and correlations between features.
Implement collaborative filtering algorithms like user-based or item-based filtering,
or content-based filtering to generate movie recommendations.
Alternatively, consider hybrid approaches that combine multiple recommendation
techniques for better accuracy.
2. Exploratory Data Analysis (EDA):
3. Recommendation Algorithms:
Split the data into training and validation sets to train the recommendation models.
Evaluate the performance of the models using predefined evaluation metrics and
fine-tune the algorithms if necessary.
Develop user profiles based on their movie preferences and interactions to
personalize the recommendations for each user.
4. Model Training and Evaluation:
5. Personalization and User Profiling:
13. The objective of the fake news detection project in data science is to develop a robust
and accurate system that can automatically identify and classify fake or misleading
news articles from genuine and reliable ones. The system aims to combat the spread
of misinformation and enhance media trustworthiness.
Fake News Dataset: Gather a labeled dataset consisting of both fake and genuine
news articles for training and evaluation purposes.
Text Preprocessing: Preprocess the news articles, including tasks such as
tokenization, stop-word removal, stemming, and lowercasing, to prepare the text
data for modeling.
Fake News Detection
14. Fake News Detection
Natural Language Processing (NLP) Libraries: Utilize NLP libraries or frameworks
(e.g., NLTK, spaCy) for text analysis and feature extraction.
Machine Learning Models: Implement machine learning models like logistic
regression, support vector machines (SVM), or deep learning models (e.g., LSTM,
BERT) for classification.
Evaluation Metrics: Define appropriate evaluation metrics such as accuracy,
precision, recall, and F1-score to assess the performance of the fake news detection
system.
Collect a diverse dataset of labeled news articles, ensuring a balance
between fake and genuine samples.
Preprocess the text data to remove noise, handle missing values, and
convert the text into a suitable format for modeling.
1. Data Collection and Preprocessing:
15. Fake News Detection
Extract relevant features from the preprocessed text data, such as TF-IDF (Term
Frequency-Inverse Document Frequency) vectors or word embeddings, to represent
the articles numerically.
Choose appropriate machine learning algorithms or deep learning architectures for
classification.
Split the dataset into training and validation sets and train the selected models on
the training data.
Choose appropriate machine learning algorithms or deep learning architectures for
classification.
Split the dataset into training and validation sets and train the selected models on
the training data.
2. Feature Extraction:
3. Model Selection and Training:
4. Model Selection and Training:
16. If you have any doubts and queries feel free to
contact us !
topperworldinternship@gmail.com
Topperworld.in
www.topperworld.in
Topperworld
Topperworld
