Artificial Intelligence Training Institute in Hyderabad

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Artificial Intelligence Training Institute in Hyderabad

Artificial Intelligence (AI) with Python is an exciting and rewarding endeavor! Below is a roadmap to guide you step-by-step, from beginner to advanced levels. This roadmap assumes you have basic programming knowledge (preferably in Python).

Module-1. Foundations of Python for AI
- Variables
- Data Types
- loops
- conditional control statement
- Iterative control statement
- Functions, and File handling.
- Object-Oriented Programming (OOPs) concepts.

Module-2: Key Python Libraries:
- NumPy: For numerical computations and array manipulation.
- Pandas: For data manipulation and analysis.
- Matplotlib and Seaborn: For data visualization.
- Scipy: For scientific computing.
- Module-3. Mathematics for AI
  AI relies heavily on mathematical concepts.Focus on the following areas:
  - Vectors
  - matrices
  - matrix multiplication
  - eigenvalues
  - eigenvectors
  - Derivatives
  - gradients,
  - optimization.
  - Probability distributions
  - Bayes' theorem,
  - mean
  - variance
  - hypothesis testing.
  - Differential Equations
  - Discrete Mathematics
  - Optimization Technologies
- Module-4: Introduction to Artificial Intelligence
  Understand the basics of AI, including key concepts and types of AI.
  - What is Artificial Intelligence?
  - Narrow AI and Superintelligent AI.
  - General AI
  - Superintelligent AI.
  - Agents
  - environments
  - rationality
  - learning
  - Key milestones and breakthroughs in AI.
- Module-5 : Basics of AI/ML
  - Introductions to Supervised
  - Unsupervised Learning in Machine Learning
  - Introductions to Neural Networks
  - Introductions to Deep Learning, and
  - Introductions to Reinforcement Learning

Module-6: Data Skills for AI/ML
- Data collection
- Data cleaning and processing
- Feature Engineering
- Data visualization Technologies
- Pandas
- Numpy
- Matplotlib
- Seaborn.

Module-7: Machine Learning
- Machine Learning is a subset of AI.
- Focus on algorithms where the model learns from data
- Regression
- Classification.
- Clustering
- Dimensionality Reduction.
- Accuracy.
- Precision.
- Recall.
- ROC-AUC.

Module-8. Deep Learning
Dive into neural networks and deep learning frameworks.
- Perceptrons
- Activation functions
- Backpropagation.
- TensorFlow and Keras: For building and training neural networks.
- PyTorch: Another popular deep learning framework.
- Convolutional Neural Networks (CNNs) for image data.
- Recurrent Neural Networks (RNNs) for sequential data.

Module-9: Natural Language Processing (NLP)
Learn how to process and analyze human language data.
- Tokenization,
- stemming
- lemmatization.
- Word2Vec
- GloVe
- FastText
- BERT
- GPT
- T5
- Bag of Words, TF-IDF LSTMs, GRU

Module-10:. Computer Vision
Learn how to process and analyze visual data.
- OpenCV, PIL
- Image classification, object detection Algorithms
- Deep Learning for Computer Vision (PyImageSearch)
- Object Detection Algorithms
- Image Segmentation
- Facial Recognition Techniques

Module-11: Reinforcement Learning
- Bellman Equation
- Markov Decision Processes (MDPs),
- Q-Learning
- Policy Gradients.
- OpenAI Gym
- Stable-Baselines.

Module-12. Advanced Topics
Explore advanced AI techniques and applications.

Generative Models:
Variational Autoencoders (VAEs)
GANs

AI Ethics and Fairness:

Bias in AI.
fairness
transparency.

AI in Healthcare, Finance, and Robotics:

Domain-specific applications.
Module-13: Tools and Libraries
- scipy
- scikit-Learn
- keras
- TesnsorFlow
- PyTorch
- opencv
- Matplotlib,seaborn,plotly
- seaborn
- plotly
- pandas,numpy
- numpy
- Jupyter Notebook
- Jupyter Lab