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AI/ML TRAINING
AI/ML TRAINING
Curriculum
10 Sections
30 Lessons
40 Hours
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Module 1: Introduction to AI & Machine Learning
3
1.1
Overview of AI, Machine Learning, and Deep Learning
1.2
Real-world applications of AI/ML
1.3
Setting up the AI/ML environment (Python, Jupyter Notebook, Anaconda)
Module 2: Python for AI & ML
3
2.1
Python basics (variables, loops, functions)
2.2
Data structures (lists, tuples, dictionaries)
2.3
Working with NumPy and Pandas for data manipulation
Module 3: Mathematics & Statistics for ML
3
3.1
Linear algebra (vectors, matrices, eigenvalues)
3.2
Probability and statistics basics
3.3
Hypothesis testing and probability distributions
Module 4: Data Preprocessing & Feature Engineering
3
4.1
Handling missing data and outliers
4.2
Data normalization and standardization
4.3
Feature selection and dimensionality reduction
Module 5: Supervised Learning Algorithms
3
5.1
Linear Regression and Logistic Regression
5.2
Decision Trees and Random Forests
5.3
Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN)
Module 6: Unsupervised Learning Algorithms
3
6.1
Clustering techniques (K-Means, DBSCAN, Hierarchical Clustering)
6.2
Principal Component Analysis (PCA) for dimensionality reduction
6.3
Association Rule Learning (Apriori, FP-Growth)
Module 7: Deep Learning with Neural Networks
3
7.1
Introduction to Neural Networks and Perceptrons
7.2
Building Deep Learning models using TensorFlow & PyTorch
7.3
Convolutional Neural Networks (CNNs) for image recognition
Module 8: Natural Language Processing (NLP)
3
8.1
Text preprocessing and vectorization techniques
8.2
Sentiment analysis and chatbot development
8.3
Working with transformers and pre-trained models (BERT, GPT)
Module 9: Model Evaluation & Optimization
3
9.1
Bias-Variance Tradeoff and Overfitting
9.2
Cross-validation and Hyperparameter Tuning
9.3
Model Performance Metrics (Precision, Recall, F1-score, ROC-AUC)
Module 10: AI Model Deployment & Final Project
3
10.1
Deploying ML models using Flask and FastAPI
10.2
Using cloud platforms (AWS, GCP, or Azure) for AI applications
10.3
Capstone Project: Building and deploying a real-world AI/ML model
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Setting up the AI/ML environment (Python, Jupyter Notebook, Anaconda)
Data structures (lists, tuples, dictionaries)
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