Skip to content
Home
About
▼
👥
Founders Desk
🎓
Tutors
🖼️
Gallery
STEM Robotics
▼
📘
Board Curriculum
🤖
StepIT Lab
🛠️
Deployment & Assistance
Our Services
▼
📚
Online Courses
🎯
Talent Box
⚖️
NyayMithr
💻
Software Services
Contact
Log In
Sign Up
P
Profile
👤 My Profile
📚 My Courses
🚪 Log Out
☰
Home
About
Founders Desk
Tutors
Gallery
STEM Robotics
STEM Robotics
Board Curriculum
StepIT Lab
Deployment & Assistance
Our Services
Online Courses
Talent Box
NyayMithr
Software Services
Contact
Log In
Sign Up
Home
All Courses
Technology
Artificial Intelligence & Machine Learning
Curriculum
16 Sections
51 Lessons
18 Weeks
Expand all sections
Collapse all sections
Week 1: Introduction to AI & ML
4
1.1
What is AI, ML, and Deep Learning?
5 Minutes
1.2
Supervised, Unsupervised, and Reinforcement Learning
1.3
Applications of AI in various industries
1.4
Setting up the environment: Python, Jupyter Notebook, Anaconda
Week 2: Python for AI & ML
3
2.1
Python Basics (Lists, Tuples, Dictionaries, Loops, Functions)
2.2
Numpy & Pandas for data manipulation
2.3
Matplotlib & Seaborn for data visualization
Week 3: Statistics & Probability for ML
4
3.1
Descriptive Statistics (Mean, Median, Mode, Variance)
3.2
Probability Distributions (Normal, Binomial, Poisson)
3.3
Hypothesis Testing & p-values
3.4
Correlation & Covariance
Week 4: Data Preprocessing & Feature Engineering
5
4.1
Handling missing values
4.2
Data scaling & normalization
4.3
Encoding categorical variables
4.4
Feature selection & dimensionality reduction (PCA, LDA)
4.5
Project: Data Cleaning & Visualization on Real-World Dataset
Week 5: Supervised Learning – Regression
3
5.1
Linear Regression, Polynomial Regression
5.2
Ridge & Lasso Regression
5.3
Evaluation Metrics (RMSE, R², MAE)
Week 6: Supervised Learning – Classification
4
6.1
Logistic Regression, Decision Trees, Random Forest
6.2
Support Vector Machines (SVM), K-Nearest Neighbors (KNN)
6.3
Performance Metrics (Accuracy, Precision, Recall, F1-score, ROC)
6.4
📌 Project: Predicting House Prices (Regression) / Spam Email Classification
Week 7: Unsupervised Learning
3
7.1
K-Means & Hierarchical Clustering
7.2
Principal Component Analysis (PCA)
7.3
Anomaly Detection
Week 8: Feature Selection & Model Optimization
4
8.1
Hyperparameter tuning (GridSearchCV, RandomizedSearchCV)
8.2
Cross-validation techniques
8.3
Ensemble learning (Bagging, Boosting, Stacking)
8.4
Project: Customer Segmentation (K-Means Clustering)
Week 9: Neural Networks Fundamentals
3
9.1
Perceptron & Multi-layer Perceptron (MLP)
9.2
Activation functions (ReLU, Sigmoid, Softmax)
9.3
Backpropagation & Gradient Descent
Week 10: Deep Learning with TensorFlow & Keras
3
10.1
Building Neural Networks with Keras
10.2
Optimizers (Adam, RMSprop, SGD)
10.3
Dropout & Batch Normalization
Week 11: Convolutional Neural Networks (CNN)
3
11.1
Image Processing & Convolution
11.2
Pooling, Padding, Transfer Learning (ResNet, VGG)
11.3
Object Detection & Image Classification
Week 12: Recurrent Neural Networks (RNN) & NLP
4
12.1
Understanding Time Series & Sequential Data
12.2
LSTMs & GRUs for Text Processing
12.3
Sentiment Analysis, Text Generation
12.4
Project: Sentiment Analysis on Tweets
Week 13: Reinforcement Learning
2
13.1
Markov Decision Process (MDP)
13.2
Q-learning & Deep Q-Networks (DQN)
Week 14: AI in Real-World Applications
2
14.1
AI in Healthcare, Finance, and Robotics
14.2
Generative AI (GANs & Transformers)
Week 15: Model Deployment & MLOps
2
15.1
Deploying models with Flask & FastAPI
15.2
CI/CD pipelines in ML (MLflow, Docker, Kubernetes)
Week 16: Capstone Project & Resume Preparation
2
16.1
Full end-to-end AI project
16.2
Resume & Interview Preparation for AI/ML Jobs
This content is protected, please
login
and
enroll
in the course to view this content!
Modal title
Main Content
