Machine Learning & Deep Learning Course – Master AI-Powered Solutions
Step into the future with Code Codence’s Machine Learning & Deep Learning Course! Designed for aspiring AI and data science professionals, this course will equip you with the skills to develop intelligent models that can learn from data and make predictions. From foundational concepts in machine learning to advanced deep learning techniques, this hands-on course offers real-world applications, empowering you to build AI solutions for diverse industries.
What You’ll Learn:
1. Introduction to Machine Learning (ML):
- Understanding supervised, unsupervised, and reinforcement learning.
- Key ML concepts: training, testing, overfitting, and model evaluation.
- Popular algorithms: Linear Regression, Logistic Regression, Decision Trees, and Random Forest.
2. Data Preprocessing and Feature Engineering:
- Handling missing data, scaling, normalization, and encoding categorical data.
- Feature selection and extraction techniques to improve model performance.
3. Supervised Learning Algorithms:
- Regression models: Linear, Polynomial, and Ridge/Lasso regression.
- Classification models: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Naive Bayes.
4. Unsupervised Learning Algorithms:
- Clustering methods: K-Means, Hierarchical Clustering, and DBSCAN.
- Dimensionality reduction techniques: PCA (Principal Component Analysis) and t-SNE.
5. Introduction to Deep Learning (DL):
- Fundamentals of neural networks: perceptrons, activation functions, and backpropagation.
- Building feedforward neural networks using frameworks like TensorFlow and Keras.
6. Convolutional Neural Networks (CNNs):
- Understanding image data and building CNNs for image classification.
- Advanced techniques: data augmentation, transfer learning, and pre-trained models.
7. Recurrent Neural Networks (RNNs) and LSTMs:
- Sequence modeling for time-series data and natural language processing (NLP).
- Building RNNs and LSTMs for text generation, sentiment analysis, and forecasting.
8. Advanced Deep Learning Topics:
- Generative Adversarial Networks (GANs) for creating synthetic data.
- Transformers and attention mechanisms for NLP tasks like translation and summarization.
9. Model Deployment and Optimization:
- Hyperparameter tuning, model optimization, and performance evaluation.
- Deploying models using Flask, FastAPI, or cloud services.
10. Capstone Project:
- Build and deploy an end-to-end AI project such as image recognition, NLP application, or recommendation system.
Course Highlights:
- Duration: Flexible (tailored to individual pace)
- Format: Online or Hybrid (live sessions, recorded lectures, and hands-on labs)
- Tools Covered: Python, TensorFlow, Keras, PyTorch, Scikit-Learn, OpenCV
- Hands-on Projects: Real-world projects to enhance your portfolio
- Mentor Support: One-on-one guidance from AI experts
- Certification: Earn a professional Machine Learning & Deep Learning certification
Join Code Codence’s Machine Learning & Deep Learning Course to build intelligent models, solve complex problems, and unlock career opportunities in AI and data science!
For more details about the Machine Learnig & Deep Learning course, contact us here or chat with us directly on WhatsApp — we’re here to help you get started!
