This comprehensive course introduces AI, ML, and DL concepts through a telecom lens. From foundational theory to applied projects, discover how these technologies solve real-world telecom challenges—like churn prediction, anomaly detection, and network optimization. Visit www.TelcoLearn.com for more details #AIinTelecom #MachineLearning #DeepLearning #TelecomUseCases #TelecomAI #DataScienceTelecom #AIMLTraining

1. Not Sure
Artificial Intelligence, Machine
Learning & Deep Learning
with Telecom use cases
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2. Learning Outcomes
1. Understanding AI, ML, and Deep Learning in Telecom: Gain foundational
knowledge of AI, ML, and Deep Learning, including their definitions,
differences, and applications in the telecom industry.
2. Hands-on with ML Algorithms: Learn to apply machine learning algorithms
(e.g., Linear Regression, Decision Trees, K-means Clustering) to telecom-
specific problems like churn prediction, network optimization, and call detail
analysis.
3. Data Preprocessing and EDA Skills: Develop expertise in telecom data
preprocessing, handling missing values, feature engineering, and conducting
Exploratory Data Analysis (EDA) to uncover patterns and insights in telecom
data.
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3. Learning Outcomes
4. Model Building and Evaluation: Build, evaluate, and tune machine learning
models (Logistic Regression, Random Forest, SVM) using various evaluation
metrics (accuracy, precision, recall, etc.) for telecom use cases such as predictive
maintenance and churn prediction.
5. Deep Learning Applications in Telecom: Understand deep learning
fundamentals and apply neural networks (CNNs, RNNs, LSTMs) for telecom-
specific challenges like anomaly detection, network fault prediction, and call data
record (CDR) analysis.
6. Capstone Project and Real-world Use Cases: Gain practical experience by
working on an end-to-end AI/ML solution for telecom use cases (e.g., churn
prediction, anomaly detection, or customer sentiment analysis) and presenting
your results.
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4. Day 1: Introduction to AI and ML in Telecom
• Module 1: Overview of AI, ML, and Deep Learning, explaining their
definitions, differences, and evolution in the telecom industry.
• Module 2: Introduction to different types of machine learning
(Supervised, Unsupervised, Reinforcement) and algorithms (e.g.,
Linear Regression, Decision Trees). Telecom use cases like customer
churn prediction and network optimization.
• Module 3: Setting up the environment with Python, Jupyter notebooks,
and libraries (NumPy, Pandas, Scikit-learn, TensorFlow, Keras). Hands-
on lab on basic Python exercises and data manipulation using telecom
data.
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5. Day 2: Data Preprocessing and EDA
• Module 1: Understanding telecom data (network, customer, device
data) and data sources (OSS/BSS, CDRs).
• Module 2: Techniques for data cleaning, handling missing values,
scaling, and feature engineering. Hands-on lab on preprocessing a
telecom dataset (e.g., customer churn dataset).
• Module 3: Exploratory Data Analysis (EDA) to identify patterns and
outliers. Visualizing data using Matplotlib and Seaborn. Hands-on lab to
explore customer behavior and network performance.
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6. Day 3: ML Model Building and Evaluation
• Module 1: Building machine learning models with Scikit-learn (Logistic
Regression, Random Forest, SVM), including model selection and
hyperparameter tuning. Hands-on lab on building and tuning a churn
prediction model.
• Module 2: Evaluating models using metrics like accuracy, precision,
recall, F1-score, and ROC curve. Cross-validation techniques for
validation. Hands-on lab to evaluate models for telecom use cases.
• Module 3: Introduction to predictive maintenance in telecom networks
and building a fault prediction model. Hands-on lab on predictive
maintenance with network data.
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7. Day 4: Deep Learning and Neural Networks
• Module 1: Fundamentals of deep learning, explaining neural networks
(CNN, RNN, LSTM) and their telecom applications.
• Module 2: Building deep learning models using Keras: creating layers,
applying activation functions, and training models. Hands-on lab on
building a basic neural network.
• Module 3: Anomaly detection in telecom data (fraud, intrusion). Hands-
on lab on anomaly detection using autoencoders.
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8. Day 5: Advanced Deep Learning and Telecom
Use Cases
• Module 1: Advanced deep learning techniques: CNNs for image data
and RNNs/LSTMs for sequence data in telecom. Hands-on lab on
building an LSTM model for CDR analysis.
• Module 2: Natural Language Processing (NLP) in telecom, including
chatbots and sentiment analysis. Hands-on lab to create a simple
chatbot using NLP.
• Module 3: Capstone project: Applying AI/ML to a telecom use case
(e.g., churn prediction, anomaly detection). Final presentation,
feedback, and course wrap-up.
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9. ThankYou
Email
info@TelcoLearn.com
Phone
+91-8810549800
Website
www.TelcoLearn.com
www.Telcolearn.com