AI & ML Masters Program

AI & ML Course - Overview

The AI & ML Masters Program at Yuva Sakthi Academy will empower delegates with advanced skills in artificial intelligence and machine learning, focusing on data analysis, predictive modeling, and algorithm development. The training covers essential topics such as supervised and unsupervised learning, deep learning, natural language processing, and data visualization. By the end of the course, learners will be adept at leveraging AI and ML techniques to analyze complex datasets and drive data-informed business decisions, utilizing real-world industry projects to reinforce their understanding.

Upon completing the AI & ML Masters Program, delegates will receive a prestigious course completion certificate from Yuva Sakthi Academy. This certificate is accredited and recognized by leading organizations worldwide, significantly enhancing the learner’s resume and improving their job prospects. The certification validates the learner's expertise in AI and ML, positioning them as highly sought-after candidates for various roles in the tech industry.

Yuva Sakthi Academy also offers extensive career support through a dedicated HR team. This team assists delegates with skill development, interview preparation, and effective communication strategies, ensuring they are fully equipped for job interviews. The program includes training on technical aptitude, mock interviews, and HR interview techniques, helping delegates secure positions in top MNCs and tech companies such as Infosys, Wipro, IBM, and more. We provide 100% placement assistance to ensure our delegates successfully launch their careers in AI and machine learning.

1. Introduction to AI & ML

• Definition and History of AI and ML
• AI vs Machine Learning vs Deep Learning
• Applications and Use Cases of AI in Real World
• Understanding Data Science

2. Basic Mathematics for Machine Learning

• Linear Algebra: Vectors, Matrices, and Tensors
• Statistics: Mean, Median, Mode, Variance, and Probability
• Probability Theory and Random Variables
• Basics of Calculus: Derivatives and Integrals

3. Introduction to Python for AI & ML

• Python Basics: Data Types, Variables, Loops, Functions
• Python Libraries: NumPy, Pandas, Matplotlib
• Working with DataFrames and CSV Files
• Data Cleaning and Preprocessing with Pandas

4. Exploratory Data Analysis (EDA)

• Understanding the Dataset
• Descriptive Statistics and Visualization
• Feature Engineering and Selection
• Handling Missing Data and Outliers
• Data Normalization and Standardization

5. Supervised Learning

• Linear Regression: Simple and Multiple
• Logistic Regression for Classification
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score

6. Unsupervised Learning

• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• Dimensionality Reduction Techniques
• Anomaly Detection

7. Ensemble Learning

• Bagging and Boosting Techniques
• AdaBoost and Gradient Boosting
• XGBoost: Extreme Gradient Boosting
• Voting and Stacking Models
• Hyperparameter Tuning with Grid Search and Random Search

8. Introduction to Deep Learning

• What is Neural Networks?
• Understanding Perceptrons and Activation Functions
• Forward and Backpropagation
• Gradient Descent and Optimization
• Introduction to Deep Neural Networks

9. Convolutional Neural Networks (CNN)

• Understanding Convolution Operations
• Building a CNN Model from Scratch
• Pooling and Flattening Layers
• Transfer Learning with Pre-trained Models
• Applications of CNN: Image Classification and Object Detection

10. Recurrent Neural Networks (RNN)

• Introduction to Sequential Data
• Understanding RNN Architecture
• Long Short-Term Memory (LSTM) Networks
• Time Series Forecasting and Text Generation
• Applications of RNN: Sentiment Analysis, Speech Recognition

11. Natural Language Processing (NLP)

• Text Preprocessing: Tokenization, Lemmatization, Stemming
• Bag of Words and TF-IDF
• Named Entity Recognition (NER) and POS Tagging
• Word Embeddings: Word2Vec, GloVe
• Transformers and BERT for NLP

12. Reinforcement Learning

• Introduction to Reinforcement Learning
• Markov Decision Process (MDP)
• Exploration vs Exploitation
• Q-Learning and SARSA
• Deep Q Networks (DQN)
• Applications: Game Playing, Robotics

13. Model Deployment and Serving

• Saving and Loading Trained Models
• Building APIs using Flask/Django to Serve Models
• Model Deployment on Cloud: AWS, GCP, Azure
• Using Docker for Model Deployment
• Monitoring and Maintaining AI Models

14. AI Ethics and Future Trends

• Ethics and Bias in AI Models
• Explainable AI and Interpretability
• AI for Social Good
• Future Trends in AI: Quantum Computing, AI in Healthcare

Tools & Libraries

• NumPy, Pandas, Matplotlib for Data Manipulation and Visualization
• Scikit-learn for Machine Learning Models
• TensorFlow, Keras, and PyTorch for Deep Learning
• OpenCV for Image Processing
• NLTK and SpaCy for NLP
• Google Colab for Model Training