Artificial Intelligence Learning Path

Artificial Intelligence (AI) Engineering: Interview-Oriented Training with Real-Time Projects

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Master the skills to build intelligent applications, machine learning models, and AI-driven solutions. This course prepares you to be industry-ready and interview-ready, with hands-on experience in Python, ML frameworks, deep learning, NLP, and computer vision.

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Course Highlights:

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1. AI & Machine Learning Fundamentals

Understand AI, machine learning, deep learning, and reinforcement learning concepts.

Explore supervised, unsupervised, and reinforcement learning algorithms.

Learn problem-solving approaches and real-world AI applications.

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2. Python for AI & Data Engineering

Master Python programming, libraries (NumPy, Pandas, Matplotlib, Seaborn) for AI workflows.

Projects: data preprocessing, feature engineering, and exploratory data analysis (EDA).

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3. Machine Learning Algorithms & Implementation

Implement linear regression, logistic regression, decision trees, random forests, SVMs, K-means, PCA.

Hands-on with scikit-learn, XGBoost, and LightGBM.

Projects: predictive modeling, classification, and recommendation systems.

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4. Deep Learning & Neural Networks

Learn artificial neural networks, CNNs, RNNs, LSTMs, GANs.

Hands-on with TensorFlow and PyTorch.

Projects: image classification, sentiment analysis, and generative models.

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5. Natural Language Processing (NLP)

Implement text preprocessing, tokenization, embeddings, and transformers.

Hands-on with NLTK, spaCy, and Hugging Face Transformers.

Projects: chatbots, sentiment analysis, and document classification.

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6. Computer Vision

Learn image processing, object detection, and image segmentation.

Hands-on with OpenCV, TensorFlow, and PyTorch for vision applications.

Projects: face recognition, real-time object detection, and image enhancement.

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7. AI in Cloud & Big Data

Deploy AI solutions using AWS SageMaker, Azure ML, and Databricks.

Learn scalable AI pipelines with Spark and big data processing.

Projects: cloud-based ML workflows and real-time AI applications.

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8. Real-Time Projects & Case Studies

Build end-to-end AI solutions from data ingestion to model deployment.

Work on projects that simulate real-world business problems.

Showcase interview-ready AI projects in your portfolio.

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9. Interview Preparation

Mock interviews covering machine learning concepts, deep learning, NLP, computer vision, AI pipelines, and problem-solving scenarios.

Solve practical coding challenges and scenario-based AI problems commonly asked in top tech companies.

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Why This Course?

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Hands-on training in Python, scikit-learn, TensorFlow, PyTorch, OpenCV, NLP, cloud AI services, Spark, and Databricks.

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Build real-time AI projects to demonstrate practical expertise.

Focused on interview readiness and problem-solving skills.

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Gain confidence to secure roles as AI Engineer, ML Engineer, Data Scientist, or NLP Engineer.