Grow Data Skills

Machine Learning Projects (Self Paced)

LanguageEnglish
Mode Recorded
Validity 1 Year
Duration 16+ Hours
Sessions 8
Projects 4

Demo Class Video

INR 1000

This course includes

16+ Hours content
4 industry level projects
Project will be covered from scratch
Tools – Jupyter Notebook, Google Colab, PyCharm, Spyder, Visual Studio Code
Technology – Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Streamlit, SMOTE, NLTK, TensorFlow, Keras, SpaCy, Azure
Certificate of completion

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Tech stack you'll learn

Course Content

Tech Stack: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Streamlit
Introduction to food delivery industry challenges and the importance of delivery time prediction with case studies.
Basics of supervised learning with a focus on predicting delivery times.
Understanding and handling categorical and continuous variables.
Fundamentals of linear regression, including assumptions and use cases in delivery prediction.
Overview of ensemble methods like bagging and boosting in regression.
Conducting exploratory data analysis (EDA) using Pandas, NumPy, Matplotlib, and Seaborn.
Implementing data transformation techniques including scaling and feature engineering.
Building regression models: linear, decision tree, random forest, and boosting models.
Evaluating models with metrics like MAE, MSE, RMSE, and cross-validation.
Deploying a predictive web app using Streamlit.

Tech Stack: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, SMOTE
Classification problems, differences from regression, applications in various industries.
Logistic regression concepts, assumptions, limitations, use cases in predicting passenger referrals.
Decision trees, splitting criteria, use cases in classification problems.
Boosting algorithms, including AdaBoost, Gradient Boosting, XGBoost, applications.
Efficient EDA using Pandas, NumPy, Matplotlib, Seaborn for dataset understanding and visualization.
Data cleaning, feature engineering, handling missing values, encoding categorical variables.
Target feature distribution analysis, class imbalance techniques like SMOTE.
Algorithm selection: logistic regression, decision trees, boosting, training, evaluation.
Feature importance analysis, summarizing findings, recommendations based on model results.
Importance of communicating findings to stakeholders, real-world impact, future.

Tech Stack: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, NLTK, SpaCy, TensorFlow, Keras
Analyze the food and restaurant industry, focusing on customer reviews and clustering objectives.
Collect data from Zomato, understand dataset features and structure.
Clean and preprocess data: handle missing values, normalize text, encode categorical variables.
Engineer features: extract useful features, calculate sentiment scores, create dummy variables.
Perform EDA using visualizations like histograms and scatter plots to identify data trends and patterns.
Understand clustering concepts, focusing on K-means and methods for determining the number of clusters.
Apply PCA for dimensionality reduction to improve clustering accuracy.
Conduct sentiment analysis using lexicon-based and machine learning methods.
Implement K-means clustering and analyze costs, ratings, and restaurant data using PCA.
Summarize insights from clustering and sentiment analysis, and provide recommendations and future enhancements.

Tech Stack: Python, TensorFlow, Keras, NLTK, SpaCy, Streamlit, Azure
Conversational chatbot using Seq2Seq LSTM models for text generation and machine translation.
Packages: numpy, tensorflow, pickle, keras; Chatterbot Kaggle English Dataset.
Parsing .yml files, tokenizing questions and answers, preparing encoder and decoder input data arrays.
Encoder-decoder model: embedding, LSTM, Dense layers for sequence generation.
LSTM components: memory blocks, cell state, input, forget, output gates.
Model training: 150 epochs, RMSprop optimizer, categorical_crossentropy loss, 96% accuracy.
Inference models: encoding questions, decoding answers using LSTM states.
Question tokenization, state value prediction, answer generation with decoder model.
Sequence generation by updating state values until the end tag.
Coherent, contextually appropriate responses, model effectiveness in conversational AI.

Attention Seeking Resume Preparation and Interview Strategies
Linkedin Profile Making

Course Schedule

Mode Of The Course:

Recorded

Course Duration:

16+ Hours

Total Sessions:

8

Total Project:

4

Class Recording Provided:

Yes

Course Validity:

1 year

Prerequisite:

Basic Python, Basic understanding of Data Science

⚠️ Important Notice :

The video may not work on Linux due to DRM restrictions. It is only accessible on Chrome when using Windows or macOS.

Workaround: To access the video on Linux, you can create a Windows virtual machine (VM) and watch the video through the VM. Alternatively, you can use our Android or iOS application to view the video on your mobile device.

Instructor

Shubhankit Sirvaiya

Shubhankit Sirvaiya is a Data Scientist At World Wide Technology | Ex- Accenture Growth & Strategy , mentored over 700+ students around Data Science Domain.

Machine Learning Projects (Self Paced)

INR 1000

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