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Machine Learning and Deep Learning

The Machine Learning and Deep Learning course offers a comprehensive exploration of the fundamental concepts and algorithms associated with machine learning (ML) and deep learning (DL).

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. The Machine Learning and Deep Learning course offers a comprehensive exploration of the fundamental concepts and algorithms associated with machine learning (ML) and deep learning (DL).

  2. Participants will engage in practical applications of these methodologies to address intricate real-world challenges. The curriculum encompasses both theoretical insights and hands-on experiences in ML and DL, including supervised and unsupervised learning, neural networks, natural language processing (NLP), and computer vision.

  3. This course emphasizes the ability of machines to learn from data autonomously, with deep learning representing a specialized area of ML that utilizes multi-layered neural networks. Students will focus on constructing models through popular ML algorithms and advanced DL frameworks like TensorFlow, Keras, and PyTorch.

  4. Additionally, they will acquire skills in data preprocessing, model training, algorithm optimization, and the deployment of ML/DL models in operational environments.

CERTIFICATION:

  1. Upon finishing the Machine Learning and Deep Learning course, participants will be awarded a Certificate in Machine Learning and Deep Learning.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Participants will grasp the foundational concepts of machine learning, including its definition, subfields, and its connection to artificial intelligence.

  2. They will differentiate between supervised, unsupervised, and reinforcement learning, and learn to frame problems suitable for machine learning while selecting the right algorithms. In supervised learning, they will implement key algorithms such as Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees, and Random Forests, while also evaluating their performance through metrics like accuracy, precision, recall, F1-score, and confusion matrices.

  3. They will gain insights into overfitting and underfitting, learning to mitigate these issues with cross-validation and regularization techniques. In the realm of unsupervised learning, participants will implement clustering methods like K-means, hierarchical clustering, and DBSCAN, as well as dimensionality reduction techniques such as Principal Component Analysis and t-SNE, applying these methods to real-world scenarios like customer segmentation and anomaly detection.

  4. They will also delve into reinforcement learning, understanding the fundamentals of Markov Decision Processes, rewards, and actions, while exploring key algorithms like Q-learning and policy gradient methods, applying these concepts to simple environments such as game playing and robotic control tasks.

  5. Finally, participants will learn about artificial neural networks, including their structure, activation functions, and weights, and will implement a basic neural network using frameworks like TensorFlow or Keras for binary classification tasks, while comprehending the backpropagation algorithm for training neural networks.

  6. They will also explore the architecture of Convolutional Neural Networks and their applications in computer vision.

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Course Curriculum

Introduction to Machine Learning and Deep Learning
  1. What is Machine Learning (ML)?
    • Definition and key concepts.
    • Types of ML: Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning.
  2. What is Deep Learning (DL)?
    • Overview of neural networks and deep learning.
    • Comparison between ML and DL.
  3. Applications of ML and DL
    • Real-world use cases in healthcare, finance, retail, autonomous vehicles, etc.
Foundations of Machine Learning
  1. Mathematics for ML
    • Linear Algebra: Vectors, matrices, and transformations.
    • Probability and Statistics: Distributions, Bayes theorem.
    • Calculus: Gradients, optimization, and gradient descent.
  2. Programming for ML
    • Python basics for ML.
    • Libraries: NumPy, pandas, and scikit-learn.
Core Machine Learning Algorithms
  1. Supervised Learning
    • Regression: Linear Regression, Logistic Regression.
    • Classification: Support Vector Machines (SVM), Decision Trees, Random Forests, K-Nearest Neighbors.
  2. Unsupervised Learning
    • Clustering: K-Means, DBSCAN, Hierarchical Clustering.
    • Dimensionality Reduction: PCA, t-SNE.
  3. Ensemble Learning
    • Boosting (AdaBoost, Gradient Boosting, XGBoost).
    • Bagging (Random Forest, Bagging Regressor/Classifier).
  4. Model Evaluation
    • Metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC.
    • Cross-validation and hyperparameter tuning.
Neural Networks and Deep Learning Fundamentals
  1. Basics of Neural Networks
    • Structure: Input, hidden, and output layers.
    • Activation Functions: ReLU, sigmoid, tanh.
    • Backpropagation and optimization techniques.
  2. Deep Learning Frameworks
    • Introduction to TensorFlow and PyTorch.
  3. Training Deep Models
    • Data preprocessing, feature scaling, and initialization techniques.
Advanced Deep Learning Architectures
  1. Convolutional Neural Networks (CNNs)
    • Basics of CNNs for image processing.
    • Architectures: LeNet, AlexNet, VGG, ResNet.
  2. Recurrent Neural Networks (RNNs)
    • Understanding sequential data.
    • Variants: LSTM, GRU.
  3. Transformers
    • Attention mechanisms and self-attention.
    • Pretrained models: BERT, GPT, and T5.
  4. Generative Models
    • Variational Autoencoders (VAEs).
    • Generative Adversarial Networks (GANs).
Specialized Topics
  1. Natural Language Processing (NLP)
    • Tokenization, embeddings (Word2Vec, GloVe).
    • Sentiment analysis and machine translation.
  2. Computer Vision
    • Object detection and segmentation.
    • Image classification and style transfer.
  3. Reinforcement Learning
    • Markov Decision Processes (MDPs).
    • Q-Learning and Deep Q-Learning.
  4. Time Series Analysis
    • Forecasting using ARIMA, LSTMs, and Transformers.
  5. Transfer Learning
    • Fine-tuning pretrained models for specific tasks.
Deployment and Scaling
  1. Model Deployment
    • Using Flask/Django for API-based deployments.
    • Containerization with Docker.
  2. Cloud Integration
    • Deploying models on AWS, Google Cloud, or Azure.
  3. Scalability
    • Optimizing models for latency and throughput.
Capstone Project
  1. Image Classification
    • Build a CNN to classify images from datasets like CIFAR-10 or MNIST.
  2. Chatbot Development
    • Create a conversational AI using transformers or RNNs.
  3. Recommendation System
    • Implement collaborative and content-based filtering.
  4. Fraud Detection
    • Use supervised learning to detect fraudulent transactions.
  5. Time Series Forecasting
    • Predict future sales or stock prices using LSTMs.

Training Features

Hands-on Projects

Real-world projects in image recognition, NLP, and recommendation systems.

State-of-the-Art Techniques

Training with cutting-edge tools like TensorFlow, PyTorch, and Hugging Face Transformers.

Interactive Learning

Virtual labs, coding challenges, and live sessions.

Career Support

Guidance for ML/DL job roles, resume building, and interview preparation.

Ethics in AI

Understanding biases and ethical implications in ML/DL applications.

Globally Recognized Certification

A certificate of expertise in Machine Learning and Deep Learning.

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