Discover the best AI course in Hyderabad with Syntax Minds

 




Course Duration:

Full Course: 3–6 months (customizable based on pace and depth)
Weekly Schedule: 4-5 sessions (each lasting 1.5–2 hours)
Capstone Project: 2 weeks

Prerequisites:
Basic understanding of Python programming.
Familiarity with high school-level mathematics (algebra, probability, and statistics).

Course Modules:
1. Introduction to Data Science and AI
Overview of Data Science and Artificial Intelligence.
Key Applications and Use Cases (Healthcare, Finance, Marketing, etc.).
Tools & Technologies (Python, Jupyter Notebooks, TensorFlow, etc.).
2. Python for Data Science and AI
Python Basics: Data Types, Control Structures, Functions, and Libraries.
Key Libraries: NumPy, Pandas, Matplotlib, Seaborn.
Data Manipulation, Cleaning, and Visualization.
3. Statistics and Probability for AI and Data Science
Descriptive Statistics: Mean, Median, Mode, Variance.
Inferential Statistics: Hypothesis Testing, Confidence Intervals.
Probability Concepts and Distributions.
4. Machine Learning Fundamentals
Supervised Learning: Linear Regression, Logistic Regression, Decision Trees.
Unsupervised Learning: Clustering, Dimensionality Reduction.
Evaluation Metrics: Accuracy, Precision, Recall, F1 Score.
5. Deep Learning Essentials
Introduction to Neural Networks.
Activation Functions, Loss Functions, and Optimizers.
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
6. Data Science Workflow
Data Collection and Exploration.
Feature Engineering and Selection.
Data Preprocessing Techniques (Handling Missing Data, Encoding, Scaling).
7. Natural Language Processing (NLP)
Text Preprocessing: Tokenization, Lemmatization, Stemming.
Sentiment Analysis and Text Classification.
Word Embeddings: Word2Vec, GloVe.
8. AI Applications
Computer Vision: Image Recognition and Object Detection.
NLP Applications: Chatbots, Summarization, Machine Translation.
Generative AI: Introduction to GANs, AI for Creativity.
9. Advanced Topics in AI and Data Science
Reinforcement Learning Basics.
Explainable AI (XAI).
AI Ethics and Bias in Models.
10. Capstone Project
End-to-end implementation of a Data Science/AI project:
Define the problem statement.
Data collection, preprocessing, and analysis.
Building and training AI/ML models.
Evaluation and deployment.

Key Deliverables:
Hands-on Practice: Real-world datasets for projects and assignments.
Certification: Upon successful completion of the course and project.
Portfolio: Create a portfolio to showcase your AI/Data Science skills.

Tools & Platforms Used:
Python (Jupyter Notebooks, Anaconda).
Libraries: TensorFlow, PyTorch, scikit-learn, Pandas, NumPy.
Visualization Tools: Matplotlib, Seaborn, Plotly.

Who Should Enroll:
Students and professionals aspiring to become Data Scientists or AI Engineers.
Anyone interested in transitioning to AI or Data Science roles.
Would you like this structured further into a brochure, or need pricing details and schedules?

🔗 For More Details & Enrollment: Syntax Minds - Generative AI Course








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