Live Online TensorFlow Course for Data Analytics

Live Course Module: TensorFlow Course for Data Analytics

Total Duration: 24 Hours (4 Weeks)

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Week 1: Introduction to TensorFlow & Data Processing (6 Hours)

Session 1 (2 hrs): Introduction to TensorFlow and Setup

• What is TensorFlow and its role in data analytics

• Installing TensorFlow and exploring TensorFlow 2.x

• Tensors, constants, and operations

• TensorFlow vs. PyTorch overview

• Hands-on: Tensor operations and computations

Session 2 (2 hrs): TensorFlow Basics & Computation Graphs

• Eager execution and computational graphs

• Variables, placeholders, and automatic differentiation

• TensorFlow math, string, and array operations

• Hands-on: Linear regression from scratch using TensorFlow

Session 3 (2 hrs): Data Handling and Preprocessing

• Loading datasets with tf.data API

• Batch processing, shuffling, and pipeline optimization

• Working with CSV, image, and text datasets

• Hands-on: Building a preprocessing pipeline

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Week 2: Building and Training Models (6 Hours)

Session 4 (2 hrs): Introduction to Keras and Neural Networks

• TensorFlow with Keras high-level API

• Building Sequential and Functional models

• Activation functions and loss functions

• Hands-on: Simple feed-forward neural network

Session 5 (2 hrs): Model Training and Optimization

• Forward and backward propagation concepts

• Optimizers: SGD, Adam, RMSProp

• Model compilation, training, and evaluation

• Visualizing training with TensorBoard

• Hands-on: Predictive analytics with tabular data

Session 6 (2 hrs): Overfitting and Regularization Techniques

• Dropout, early stopping, L1/L2 regularization

• Batch normalization

• Learning rate scheduling

• Practical: Improving model generalization

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Week 3: Deep Learning Models for Data Analytics (6 Hours)

Session 7 (2 hrs): Classification and Regression in TensorFlow

• Binary and multiclass classification

• Softmax and categorical cross-entropy

• Regression tasks and loss functions

• Hands-on: Predicting housing prices using TensorFlow

Session 8 (2 hrs): Convolutional Neural Networks (CNNs) Overview

• CNN architecture: convolution, pooling, flatten, dense layers

• Using CNNs for image analytics

• Transfer learning using pre-trained models

• Practical: Image-based defect detection (introductory project)

Session 9 (2 hrs): Time Series and Sequential Data Modeling

• Introduction to RNNs, LSTMs, and GRUs

• Preparing sequential datasets

• Building a forecasting model using LSTM

• Hands-on: Predicting stock or sales trends

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Week 4: Model Evaluation, Deployment & Capstone Project (6 Hours)

Session 10 (2 hrs): Model Evaluation and Explainability

• Evaluation metrics for regression and classification

• Confusion matrix, precision, recall, F1-score, AUC

• Model explainability with TensorFlow and SHAP

• Hands-on: Model performance visualization

Session 11 (2 hrs): Saving, Loading, and Deploying Models

• Saving models in H5 and SavedModel format

• Loading and using trained models for predictions

• Model serving with TensorFlow Serving and Flask

• Practical: Creating an analytics API endpoint

Session 12 (2 hrs): Capstone Project & Presentation

• Capstone: End-to-end TensorFlow project

  • Data preprocessing → Model training → Evaluation → Deployment

• Example projects:

  • Predict customer churn

  • Sales forecasting

  • Sentiment classification

• Final presentation, review, and certification assessment

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🧠 Tools & Technologies Used

• TensorFlow 2.x

• Keras API

• NumPy, Pandas, Matplotlib, Seaborn

• Jupyter Notebook / Google Colab

• TensorBoard

• Flask (for deployment)

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🏁 Final Deliverables

• End-to-end TensorFlow project notebook

• Model files and documentation

• Performance report and visualization dashboard

• Certificate of completion

Learning Outcomes

By the end of this course, learners will be able to:
✅ Understand TensorFlow architecture and core components
✅ Build and train deep learning models using TensorFlow and Keras
✅ Apply TensorFlow for classification, regression, and time series forecasting
✅ Optimize, evaluate, and visualize model performance
✅ Deploy TensorFlow models in production or analytics environments