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Deep Dive into Machine Learning & Deep Learning with AI - Course Details
Machine Learning

Deep Dive into Machine Learning & Deep Learning with AI

Embark on a comprehensive journey into the world of Machine Learning and Deep Learning. Master algorithms, neural networks, and deploy real-world AI solutions using Python and leading frameworks.

4.9
55 Lessons
35 Hours
₹199 ₹6,999 97% OFF
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Deep Dive into Machine Learning & Deep Learning with AI

Course Highlights

What You'll Achieve

  • Understand the mathematical foundations of various ML algorithms
  • Implement supervised learning models (Regression, Classification) from scratch and with Scikit-learn
  • Explore unsupervised learning techniques (Clustering, Dimensionality Reduction)
  • Design and train deep neural networks using TensorFlow/Keras and PyTorch
  • Master Convolutional Neural Networks (CNNs) for image recognition and computer vision tasks
  • Implement Recurrent Neural Networks (RNNs) and LSTMs for sequence data (NLP)
  • Learn advanced techniques like transfer learning, fine-tuning, and generative models
  • Evaluate, debug, and optimize machine learning models for real-world performance
  • Deploy ML models into production environments

Key Features

  • Extensive Hands-on Coding Labs
  • Detailed Explanations of ML/DL Theory
  • Access to GPU-Accelerated Environments (Google Colab)
  • Advanced Model Deployment Strategies
  • Interview Preparation & Career Guidance

Tools & Technologies

  • Python 3
  • Jupyter Notebook
  • Scikit-learn
  • TensorFlow
  • Keras
  • PyTorch
  • NumPy
  • Pandas
  • Matplotlib

Target Audience

  • Aspiring Machine Learning Engineers
  • Data Scientists
  • AI Researchers
  • Software Developers transitioning to AI

Prerequisites

  • Strong understanding of Python programming
  • Basic knowledge of linear algebra and calculus

Detailed Curriculum

Module 1: Module 1: Machine Learning Fundamentals

  • Lesson 1: Revisiting Python for ML & Data Preprocessing
  • Lesson 2: Supervised Learning: Regression (Linear, Polynomial)
  • Lesson 3: Supervised Learning: Classification (Logistic Regression, SVM, Decision Trees)
  • Lesson 4: Model Evaluation Metrics and Cross-Validation
  • Lesson 5: Bias-Variance Trade-off and Regularization

Module 2: Module 2: Advanced Machine Learning

  • Lesson 1: Ensemble Methods: Random Forests, Gradient Boosting
  • Lesson 2: Unsupervised Learning: K-Means Clustering, Hierarchical Clustering
  • Lesson 3: Dimensionality Reduction: PCA, t-SNE
  • Lesson 4: Anomaly Detection Algorithms
  • Lesson 5: Introduction to Recommender Systems

Module 3: Module 3: Deep Learning with TensorFlow/Keras

  • Lesson 1: Introduction to Neural Networks and Perceptrons
  • Lesson 2: Activation Functions, Loss Functions, Optimizers
  • Lesson 3: Building Your First Neural Network
  • Lesson 4: Backpropagation and Gradient Descent Explained
  • Lesson 5: Regularization in Deep Learning

Module 4: Module 4: Convolutional Neural Networks (CNNs)

  • Lesson 1: Introduction to Computer Vision
  • Lesson 2: Convolutional Layers and Pooling Layers
  • Lesson 3: Building CNNs for Image Classification
  • Lesson 4: Advanced CNN Architectures (ResNet, VGG, Inception)
  • Lesson 5: Transfer Learning and Fine-tuning CNNs

Module 5: Module 5: Recurrent Neural Networks (RNNs) & NLP

  • Lesson 1: Introduction to Sequence Models
  • Lesson 2: RNNs, LSTMs, and GRUs for Time Series Data
  • Lesson 3: Natural Language Processing (NLP) Fundamentals
  • Lesson 4: Word Embeddings (Word2Vec, GloVe)
  • Lesson 5: Building Text Classification and Generation Models

Module 6: Module 6: Advanced Deep Learning & Deployment

  • Lesson 1: Generative Adversarial Networks (GANs) Basics
  • Lesson 2: Reinforcement Learning Concepts
  • Lesson 3: Model Deployment Strategies (Flask, Docker, Cloud Platforms)
  • Lesson 4: Monitoring and Maintaining ML Models
  • Lesson 5: Capstone Project: Develop an End-to-End AI Application

What Our Students Say

(5/5)

"An incredibly thorough and challenging course! The depth of the explanations and practical implementation with both TensorFlow and PyTorch was outstanding."

- Dr. Kavita Rao

(4.9/5)

"Coming from a software background, this course provided the perfect bridge into ML engineering. The deployment module was particularly helpful."

- Arjun Saini

Frequently Asked Questions

Is a strong math background required?

Basic understanding of linear algebra and calculus is highly recommended. The course will review key mathematical concepts as needed, but prior exposure helps.

What is the difference between TensorFlow and PyTorch covered?

The course provides an introduction to both frameworks, allowing you to understand their core concepts and choose the one best suited for your projects.

Will I build a complete AI project?

Yes, the capstone project involves designing, implementing, and deploying a full-fledged AI application, consolidating all learned concepts.

Earn Your Certification

Receive a "Certified Machine Learning & Deep Learning Expert" certificate, validating your advanced AI skills.

View Certification Details

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