1:1 Technical Mentorship

Here’s a high-quality AI/ML concept list for demonstration purposes:

1. Introduction to Machine Learning: Types of ML (Supervised, Unsupervised, Reinforcement Learning).
2. Linear Regression and Gradient Descent.
3. Logistic Regression and Sigmoid Function.
4. Decision Trees and Random Forests.
5. Support Vector Machines (SVMs): Hyperplane and Kernel Trick.
6. K-Nearest Neighbors (KNN): Distance Metrics.
7. K-Means Clustering and Elbow Method.
8. Principal Component Analysis (PCA): Dimensionality Reduction.
9. Neural Networks: Feedforward Networks and Backpropagation.
10. Convolutional Neural Networks (CNNs): Image Recognition Basics.
11. Recurrent Neural Networks (RNNs): Sequence Prediction.
12. Natural Language Processing (NLP): Tokenization, Stemming, Lemmatization.
13. Transformers: Attention Mechanism and BERT Overview.
14. Autoencoders: Dimensionality Reduction and Denoising.
15. Generative Adversarial Networks (GANs): Generator and Discriminator Concepts.
16. Time Series Forecasting: ARIMA and LSTM Models.
17. Hyperparameter Tuning: Grid Search and Random Search.
18. Evaluation Metrics: Precision, Recall, F1 Score, ROC-AUC.
19. Bias and Variance Tradeoff.
20. Model Deployment Basics: APIs and Cloud Services Integration.