The Machine Learning Training and Internship Program offers a structured learning path, covering fundamental to expert-level skills:
15 Days (Introduction to Machine Learning):
Gain a basic understanding of machine learning, including types of ML, data preprocessing, and implementation of linear regression and classification models. The program also includes hands-on projects like predicting house prices and classifying the Iris dataset.
30 Days (Beginner-Level Machine Learning):
Dive deeper into Python libraries for ML, explore supervised and unsupervised learning models like decision trees, SVM, and K-means clustering, and learn dimensionality reduction techniques like PCA. You’ll work on customer segmentation projects to apply these concepts.
45 Days (Intermediate-Level Machine Learning):
Enhance your skills with advanced regression techniques such as polynomial, ridge, and lasso regression, and explore advanced classification techniques like KNN and Naive Bayes. Learn about model evaluation and hyperparameter tuning, applying these skills in practical projects like fraud detection.
60 Days (Advanced ML Development):
Explore neural networks and deep learning concepts, including basic neural networks with TensorFlow/Keras. Learn about time series forecasting using ARIMA and LSTMs and develop a sentiment analysis model using NLP techniques. The program also includes advanced projects such as stock price prediction and sentiment analysis.
90 Days (Comprehensive ML Development):
Master deep learning with Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for sequence modeling. Learn model deployment with Flask or FastAPI, and work on projects like an image classification system and chatbot development.
180 Days (Expert-Level ML Development):
Achieve expertise in advanced machine learning and deep learning techniques, including ensemble learning, transfer learning, and Generative Adversarial Networks (GANs). The final month focuses on advanced NLP with transformers and building large-scale applications such as recommendation systems and end-to-end cloud deployment.
Throughout the program, you will engage in hands-on practice with coding assignments, weekly assessments, and capstone projects to develop real-world skills.
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