Next cohort begins on 1st September 2025 | Submit your application today | Top 40 applications will be accepted for next cohort | Best 3 candidates in each Cohort gets 100% sponsored mentorship.
Machine Learning
Transform your career in just 10 weeks with Machine Learning
Limited Batch
Live Data
Real Client
On Job Experience
Who Can Benfit From The Program?
Business Intelligence Skills Using Power BI Can Benefit A Wide Range Of Individuals And Organizations Including:
Students: Aiming a career in Data Science
Working Professional: Wants to transform over to Data Science Domain.
Freelancer: Looking for opportunities to upskill and network in Analytics Domain
Human Resource Professionals: Who want to analyze employee data to identify trends and opportunities for improvement.
Finance Professionals: Who want to track and analyze financial data to make better business decisions.
Business Managers: For intelligent decision making in their operations.
Data Analysts: Who are upskilling their business analytical skills.
Enrepreneurs & C-Level Executives: Wanting to scale their business using BI strategies.
Sales Professionals: Who want to analyze sales data to identify trends and opportunities.
Marketing Professionals: Who want to track and analyze marketing data to improve campaign performance.
Introduction to Machine Learning
Overview: Key concepts, definitions, and types of machine learning.
Role of Python: Why Python is popular for building machine learning models.
Data Preparation and Pre-processing
Data Cleaning: Handling missing values, outliers, and duplicates.
Feature Scaling: Normalization and standardization techniques.
Supervised Learning: Regression Models
Linear Regression: Model building, assumptions, and evaluation metrics.
Polynomial Regression: Extending linear models for nonlinear data.
Supervised Learning: Classification Models
Logistic Regression: Binary classification and probability predictions.
Support Vector Machines (SVM): Understanding the hyperplane and kernel trick.
Decision Trees and Ensemble Methods
Decision Trees: Model interpretation and overfitting control with pruning.
Random Forest and Gradient Boosting: Bagging and boosting techniques for improved accuracy.
Unsupervised Learning: Clustering
KMeans Clustering: Partitioning data into K distinct clusters.
Hierarchical Clustering: Building a tree of clusters for hierarchical relationships.
Unsupervised Learning: Dimensionality Reduction
Principal Component Analysis (PCA): Reducing data dimensions while retaining variance.
tSNE and UMAP: Advanced techniques for data visualization in lower dimensions.
Model Evaluation and Selection
CrossValidation: Ensuring model generalization using kfold and other techniques.
Evaluation Metrics: Precision, recall, F1score, and ROCAUC for classification; RMSE and MAE for regression.
Hyper parameter Tuning and Model Optimization
Grid Search and Random Search: Techniques for finding the best model parameters.
Automated Hyper parameter Tuning: Using libraries like Hyperopt and Optuna.
Deployment of Machine Learning Models
Model Exporting: Saving models with `pickle` or `joblib`.
Deployment: Integrating models into applications using Flask or FastAPI.
Program Certificate
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Program Certificate
Meet Our Experts
VINAY BORHADE
Founder | Chief Data Scientist
MOHIT JAIN
PM | Data Scientist
AKASH POL
AI Mentor
How It Works?
1.Application Process
Apply for Program via dedicated link to show your interest
