Grow Data Skills

Module 1 - Python for Data Science

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

Module 2 - Statistics for Machine Learning

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

Module 3 - Mathematics for Machine Learning

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

Module 4 - Introduction to Machine Learning

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

Module 5 - Pre-ML Essentials

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

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

Module 6 - Machine Learning Algorithms

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

Module 7 - Natural Language Processing

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

Module 8 - Industry Projects

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

Bonus Module - GenAI

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

Course Duration:

30 Sessions

Mode of the Course:

Recorded

Class Duration:

2 Hours Recoded Sessions

Class Recording Provided:

Yes

Course Validity:

1 year

Programming Language Used:

Python

Prerequisite:

Check Our Complete Python Course

⚠️ 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.

Complete Data Science Bootcamp Course (Self Paced)

LanguageEnglish

Sessions 30

Validity 1 Year

Mode Recorded

Tech stack you'll learn

Course Content

Course Schedule

Instructor

Shubhankit Sirvaiya

Related Courses

Complete Data Science Course (Self Paced)

Namaste 🙏