Data Science Course — Python, ML & Azure

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Complete Data Science - Basic To Advance

By Shubhankit In Grow Data Skills

Self Paced
English
Completion Certificate

Demo Class Video

1 Year

Validity (From the date of Enrollment)

65 Hours

Duration

30

Sessions

2

Projects

INR 5400

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This course includes

Content Duration65 Hours
Total Video Sessions30
Total Industry Projects2
Azure Cloud
Quality Assignments & quizzes after each module
Interview preparation guide
Dedicated placement assistance
Resume & Linkedin profile making
Doubt support on private discord community
Certificate of completion

Tech stack you'll learn

Course Content

EDA with Python - Revising Python Libraries
- 📍 Data Mining, Data Cleaning , Data Tranformation and Data Visulaization

EDA with Python - Art of Doing EDA
- 📍 End to End Data Analysis using Python

Descriptive Statistics
- 📍 Measures of Central Tendency, Dispersion
- 📍 Skewness, Kurtosis

Probability & Distributions
- 📍 Probability Theory, Bayes Theorem
- 📍 Probability Distributions, Sampling

Inferential Statistics
- 📍 Confidence Intervals, Hypothesis Testing Basics
- 📍 t-test, ANOVA, Chi-square Tests

Hypothesis Testing and Regression Analysis
- 📍 Linear Regression Assumptions, t-test, F-test, Regression Diagnostics

Getting all stats together plus Practical Use cases

Linear Algebra Fundamentals
- 📍 Vectors, Matrices, Matrix Operations
- 📍 Eigenvalues, Eigenvectors

Calculus for ML
- 📍 Derivatives, Integrals, Partial Derivatives
- 📍 Gradient, Chain Rule

Optimization Techniques
- 📍 Gradient Descent, Stochastic Gradient Descent
- 📍 Convergence, Learning Rate Tuning

ML Overview & Data Preprocessing
- 📍 Data Cleaning, Feature Scaling, Feature Engineering,What is Machine Learning.

Model Evaluation Metrics
- 📍 Accuracy, Precision, Recall, F1 Score, ROC-AUC, Confusion Matrix.

Overfitting & Underfitting
- 📍 Regularization Techniques, Bias-Variance Tradeoff, Model Complexity, Cross-Validation

Linear Regression
- 📍 Assumptions, Ordinary Least Squares, Multicollinearity, Residual Analysis

Logistic Regression
- 📍 Binary Classification, Sigmoid Function, Log-Likelihood, Odds Ratio, Multiclass Classification

Decision Trees
- 📍 Tree Construction, Gini Index, Entropy, Pruning, Decision Tree Limitations

Random Forests
- 📍 Ensemble Learning, Bagging, Feature Importance, Random Forests vs Decision Trees

Xgboost
- 📍 Boosting Techniques, Building Xgboost Models.

Support Vector Machines (SVM)
- 📍 Kernel Trick, Margin Maximization, Soft Margin, SVM for Regression

K-Means Clustering
- 📍 Elbow Method, Cluster Initialization, Distance Metrics, Clustering Evaluation

Hierarchical Clustering
- 📍 Dendrograms, Agglomerative Method, Distance Measures, Cluster Number Determination

Introduction to Neural Networks
- 📍 Activation Functions, Network Architectures, Backpropagation, Loss Functions

NLP Fundamentals
- 📍 Intro to NLP , Machine Understanding Words, Vectorizers , Simple NLP models, Sentiment Classification Project

SmartGuard: End-to-End Predictive Maintenance for Industrial Automation
- 📍 Task 1: Data Engineering - The Foundation of Predictive Insights
- 📍 Task 2: Exploratory Analysis & Feature Engineering - Uncovering Hidden Signals
- 📍 Task 3: Predictive Modelling - Crafting the Crystal Ball
- 📍 Task 4: Deployment & Monitoring Using Azure - Bringing the Model to Life

Feedback Fusion: Enhancing Business Intelligence through Customer Sentiment Analysis
- 📍 Task 1: Data Collection & Integration - Building the Feedback Repository
- 📍 Task 2: Text Preprocessing & Basic NLP - Preparing the Data
- 📍 Task 3: Sentiment Analysis & Insight Extraction - Understanding Customer Sentiments
- 📍 Task 4: Dashboard Development & Deployment Using Azure - Bringing Insights to Action

Attention Seeking Resume Preparation and Interview Strategies
Strategies To Crack Tech Interviews
Linkedin Profile Making
How To Expand Your Professional Network On Linkedin
How To Use Various Job Portals
How To Approach For Referrals

Intro to GenAI and OpenAI
- 📍 Understanding GenAI, Connecting with OpenAI , Building Natural Language to SQL using Gen AI

Project Title: End-to-End Food Delivery Time Prediction Using Supervised Learning and Ensemble Methods
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.

Project Title: Airline Passenger Referral Program Development with Classification Techniques
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.

Project Title: Advanced Zomato Restaurant Clustering and Sentiment Analysis
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.

Project Title: Creating an Intelligent Chatbot with Deep Learning and Python
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.

Course Schedule

Mode of the Course:

Recorded

Course Duration:

65 Hours

Total Sessions:

Total Projects:

Validity:

1 Year (From the date of enrollment)

Programming Language Used:

Python

Prerequisite:

Basic Python , Basic SQL

⚠️ 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.

Complete Data Science - Basic To Advance (Self Paced)

INR 5400

Buy Now

Complete Data Science - Basic To Advance

1 Year

65 Hours

30

2

Tech stack you'll learn

Course Content

Module 1 - Python for Data Science

Module 2 - Statistics for Machine Learning

Module 3 - Mathematics for Machine Learning

Module 4 - Introduction to Machine Learning

Module 5 - Pre-ML Essentials

Module 6 - Machine Learning Algorithms

Module 7 - Natural Language Processing

Module 8 - Industry Projects

Module 9 - Resume, LinkedIn & Interview Strategies

Bonus Module - GenAI

Bouns: Machine Learning Project - 1

Bouns: Machine Learning Project - 2

Bouns: Machine Learning Project - 3

Bouns: Machine Learning Project - 4

Course Schedule

Instructor

Shubhankit Sirvaiya

Complete Data Science - Basic To Advance (Self Paced)

Namaste 🙏