Spearhead Your Career in Machine Learning Engineer from CloudNet India, Kolkata

Module 1: Python Ecosystem for AI & ML

• Python environment setup for AI development
• Virtual environments (venv, Conda)
• Jupyter Notebook & Google Colab for ML workflows
• Writing efficient, reusable Python code for ML projects

Module 2: Data Handling & Numerical Computing

• NumPy arrays, broadcasting, vectorization
• Linear algebra basics for ML (matrices, vectors)
• Random number generation & sampling
• Performance optimization using NumPy

Module 3: Data Analysis & Manipulation

• Pandas Series & DataFrames
• Data cleaning & preprocessing techniques
• Handling missing values & outliers
• Feature selection & transformation
• Time-series data handling
  

Module 4: Data Visualization for ML

• Data visualization using Matplotlib & Seaborn
• Statistical plots for data understanding
• Visualizing distributions, correlations & trends
• Visualization for model insights

Module 5: Statistics & Probability for Machine Learning

• Descriptive statistics
• Probability distributions
• Sampling techniques
• Hypothesis testing concepts
• Correlation & covariance

Module 6: Feature Engineering & Data Preparation

• Encoding categorical variables
• Feature scaling & normalization
• Dimensionality reduction concepts
• Data pipelines for ML workflows

Module 7: Python for Machine Learning Workflows

• Introduction to Scikit-learn
• Building ML pipelines
• Train-test split & cross-validation
• Model evaluation metrics
• Saving & loading models

Module 8: Python for Deep Learning Foundations

• Introduction to TensorFlow & PyTorch
• Tensors and computational graphs
• Data loaders & batching
• Training loops & optimization basics

Module 9: Python for Generative AI & LLMs

• Working with APIs (OpenAI / Hugging Face)
• Prompt engineering using Python
• Text generation & embeddings
• Image generation workflows
• Tokenization & vector databases basics
  

Module 10: Automation & AI Pipelines

• Automating ML workflows using Python
• Model inference pipelines
• Working with JSON, REST APIs
• Logging & monitoring basics

Module 11: Mini Projects (Hands-On)

• Data analysis & visualization project
• ML preprocessing pipeline project
• LLM-based text generation application
• Generative AI automation script

• Module 1: Introduction to Machine Learning

  • Definition and types of ML: Supervised, Unsupervised, Semi-supervised, Reinforcement Learning
  • Role of ML in AI and Generative AI
  • ML workflow: Data → Features → Model → Evaluation → Deployment
  • Applications of ML across industries (Healthcare, Finance, NLP, Vision)

  Module 2: Supervised Learning

  • Regression Algorithms
    • Linear Regression, Polynomial Regression
    • Regularization: Ridge, Lasso, ElasticNet
    • Evaluation metrics: MSE, RMSE, R², MAE
      
      
  • Classification Algorithms
    • Logistic Regression, K-Nearest Neighbors (KNN)
    • Decision Trees, Random Forests, Gradient Boosting (XGBoost, LightGBM)
    • Support Vector Machines (SVM)
    • Evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC
      
      
  • Practical Hands-On
    • Predicting housing prices (regression)
    • Classifying emails as spam/non-spam (classification)
      
      
  
  
  

  Module 3: Unsupervised Learning

  • Clustering Techniques
    • K-Means, Hierarchical Clustering, DBSCAN
    • Silhouette score & elbow method
      
      
  • Dimensionality Reduction
    • Principal Component Analysis (PCA)
    • t-SNE, UMAP for visualization
      
      
  • Anomaly Detection
    • Isolation Forest, One-Class SVM
      
      
  • Hands-On Projects
    • Customer segmentation using clustering
    • Fraud detection using anomaly detection
      
      

  Module 4: Feature Engineering & Data Preprocessing

  • Handling missing data and outliers
  • Encoding categorical variables
  • Feature scaling: Standardization, Normalization
  • Feature selection techniques
  • Creating ML pipelines for repeatable workflows

  Module 5: Model Evaluation & Optimization

  • Train-Test split, K-Fold Cross Validation
  • Hyperparameter tuning: Grid Search, Random Search
  • Regularization techniques to prevent overfitting
  • Bias-Variance tradeoff and model selection
  • Model interpretation with SHAP and LIME

  Module 6: Ensemble Learning

  • Bagging & Boosting techniques
  • Random Forest, AdaBoost, Gradient Boosting
  • XGBoost, LightGBM, CatBoost practical applications
  • Voting and stacking ensemble methods
    
    
  
  
  
  
  

  Module 7: Introduction to Neural Networks

  • Perceptron and multilayer neural networks
  • Activation functions: Sigmoid, ReLU, Softmax
  • Forward propagation and backpropagation
  • Training neural networks using TensorFlow & PyTorch

  Module 8: Advanced Machine Learning Techniques

  • Support Vector Machines (SVM) in-depth
  • Kernel methods
  • Time-series forecasting using ARIMA & LSTM
  • Natural Language Processing (NLP) basics for AI
  • Reinforcement learning overview

  Module 9: Machine Learning for Generative AI

  • Role of ML in Generative AI
  • Dimensionality reduction for generative models
  • Feature representation for LLMs and GANs
  • Preparing datasets for text, image, and audio generation
  Introduction to prompt engineering and embeddings
  
  

Module 1: Introduction to Deep Learning

• What is Deep Learning and its importance in AI
• Difference between ML and DL
• Applications of Deep Learning: Computer Vision, NLP, Speech Recognition, Generative AI
• Overview of Deep Learning frameworks: TensorFlow & PyTorch
• GPU vs CPU for training

Module 2: Neural Network Fundamentals

• Perceptron and Multilayer Perceptrons (MLP)
• Activation functions: Sigmoid, ReLU, Tanh, Softmax
• Forward propagation & backpropagation
• Loss functions: Mean Squared Error, Cross-Entropy Loss
• Gradient descent and optimization basics
  

Module 3: Deep Learning Model Training

• Data preparation for Deep Learning
• Batch training, mini-batch gradient descent
• Learning rate, momentum, and optimizers (SGD, Adam, RMSProp)
• Overfitting and regularization: Dropout, L1/L2 Regularization
• Early stopping and checkpointing

Module 4: Convolutional Neural Networks (CNN)

• CNN architecture and concepts: Convolution, Pooling, Flattening
• Building CNNs for image classification
• Transfer learning: Pretrained models (VGG, ResNet, Inception)
• Data augmentation and image preprocessing
• Hands-on project: Image classification with CIFAR-10 / MNIST

Module 5: Recurrent Neural Networks (RNN)

• Sequence data and time-series basics
• RNN architecture and limitations
• LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) networks
• Applications in NLP, forecasting, and sequential prediction
• Hands-on project: Text sentiment analysis or stock price prediction

Module 6: Advanced Deep Learning Architectures

• Autoencoders for dimensionality reduction & anomaly detection
• Variational Autoencoders (VAE) for generative tasks
• Generative Adversarial Networks (GANs) basics
• Attention mechanism and introduction to Transformers

Module 7: Deep Learning for NLP

• Word embeddings: Word2Vec, GloVe, FastText
• Sequence-to-sequence models
• Transformer architecture: Attention, Encoder-Decoder
• Large Language Models (LLMs) overview
• Hands-on project: Text generation / summarization

Module 8: Deep Learning for Generative AI

• CNNs & GANs for image generation
• VAEs for latent space modeling
• Diffusion models basics
  
• Prompt engineering with LLMs for generative tasks
• Hands-on project: AI-generated text, images, or music

Module 9: Model Evaluation & Deployment

• Evaluating deep learning models (accuracy, F1, BLEU, ROUGE)
• Model saving & loading (HDF5, PyTorch formats)
• Exporting models for production (ONNX, TensorFlow Serving)
• Basic introduction to MLops for Deep Learning

Module 10: Hands-On Projects

• CNN Project: Image classification or object detection
• RNN/LSTM Project: Time-series forecasting or NLP task
• GAN Project: Image or audio generation
• Autoencoder/VAE Project: Anomaly detection / generative modeling