Machine Learning
Machine Learning Training In Bhubaneswar
Advance your career with Machine Learning training at Logic Wave in Bhubaneswar. Our program is meticulously designed to equip you with a deep understanding of Machine Learning, a critical technology driving innovation across various sectors.
Throughout the course, you’ll explore core topics including supervised and unsupervised learning, deep learning, and model evaluation. Our curriculum integrates theoretical knowledge with hands-on experience, allowing you to work on real-world projects and develop practical skills. You will learn to implement algorithms, analyze data, and build predictive models using popular tools and frameworks.
Our training is led by experienced instructors who bring industry insights and practical expertise to each session. The supportive learning environment at Logic Wave ensures that you receive personalized attention and guidance throughout your training.
Whether you’re new to the field or looking to enhance your existing skills, our Machine Learning training program is tailored to meet your needs. Join Logic Wave and take advantage of our modern facilities and expert instruction to unlock new career opportunities in the rapidly evolving world of Machine Learning.
Course Duration
3 Months
Course Fees
₹ 10,000
Includes
ML
Course Days
Weekly 3 Days
Course Duration
5 Months
Course Fees
₹ 30,000
Includes
ML, Live Project Training
Course Days
Monday To Friday
Course Overview and Modules
- Overview of Artificial Intelligence (AI) and its significance
- History and evolution of AI
- Key concepts and types of AI (Narrow AI, General AI, Superintelligent AI)
- Applications of AI across various industries
- Setting up the Python environment for AI development
- Essential Python libraries for AI (NumPy, pandas, SciPy)
- Working with data using Python
- Introduction to machine learning concepts
- Supervised vs. unsupervised learning
- Key algorithms (linear regression, k-nearest neighbors, clustering)
- Model evaluation metrics (accuracy, precision, recall, F1 score)
- Understanding neural networks and their components
- Introduction to deep learning frameworks (TensorFlow, Keras, PyTorch)
- Building and training basic neural networks
- Understanding activation functions and loss functions
- Convolutional Neural Networks (CNNs) for image processing
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequential data
- Generative Adversarial Networks (GANs) and their applications
- Transfer learning and pre-trained models
- Basics of NLP and its applications
- Text preprocessing techniques (tokenization, stemming, lemmatization)
- Building and training language models (word embeddings, transformers)
- Sentiment analysis, named entity recognition, and text generation
- Introduction to reinforcement learning concepts
- Key components: agents, environments, rewards
- Exploration vs. exploitation dilemma
- Implementing algorithms (Q-learning, policy gradients)
- Image processing techniques and algorithms
- Object detection and recognition
- Image segmentation and classification
- Applications of computer vision (face recognition, autonomous vehicles)
- Basics of robotics and AI integration
- Robot perception and control systems
- Path planning and autonomous navigation
- Applications of AI in robotics (manufacturing, healthcare)
- Understanding ethical considerations in AI
- Addressing biases and fairness in AI systems
- Ensuring transparency and accountability
- Privacy and data protection issues
- Model deployment strategies (cloud services, on-premises)
- Building scalable AI applications
- Monitoring and maintaining AI systems
- Handling production challenges and performance tuning
- Overview of AI development tools and platforms (Google AI, Microsoft Azure AI, AWS AI)
- Using pre-built AI services and APIs