The Machine Learning course at Eddoc Technologies is designed to equip learners with the knowledge and practical skills necessary to develop intelligent systems and predictive models. As businesses increasingly rely on data-driven decision-making, machine learning has become a key skill across industries. This comprehensive course is ideal for aspiring data scientists, AI enthusiasts, and software developers looking to expand their expertise in machine learning and artificial intelligence.

Machine Learning Training Course

Key Features of ML

50+ live sessions spread around seven months

Hands-On Projects with Real-World Datasets

Expert Instructors with Industry Experience

Practical Experience with Popular ML Tools and Frameworks

Resume Preparation and LinkedIn Profile Review

Career Guidance and Placement Assistance

COURSE CURRICULUM

Module 1: Introduction to Machine Learning

➤ What is Machine Learning?

• Definition and basic concepts

• Applications of machine learning

• Differences between AI, ML, and Deep Learning

➤ Types of Machine Learning

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

➤ Key Concepts

• Features and labels

• Training, testing, and validation datasets

• Overfitting and underfitting

Module2:Mathematical Foundations

➤ Linear Algebra for ML

• Matrices, vectors, and operations

• Eigenvalues and eigenvectors

➤ Probability and Statistics

• Bayes’ Theorem

• Random variables and probability distributions

➤ Optimization Basics

• Cost functions

• Gradient Descent

Module 3: Supervised Learning

➤ Regression

• Linear Regression

• Polynomial Regression

• Regularization (Lasso, Ridge)

➤ Classification

• Logistic Regression

• k-Nearest Neighbors (k-NN)

• Support Vector Machines (SVMs)

➤ Model Evaluation

• Metrics: Accuracy, Precision, Recall, F1 Score

• Confusion Matrix and ROC-AUC

Module 4: Unsupervised Learning

➤ Clustering

• k-Means Clustering

• Hierarchical Clustering

• DBSCAN

➤ Dimensionality Reduction

• Principal Component Analysis (PCA)

• t-SNE

➤ Anomaly Detection

• Applications in fraud detection

• Isolation Forest

Module 5: Ensemble Learning

➤ Basics of Ensemble Methods

• Why use ensemble methods?

• Bagging vs Boosting

➤ Popular Algorithms

• Random Forest

• Gradient Boosting Machines (GBM)

• XGBoost, LightGBM, CatBoost

➤ Stacking and Blending

• Combining multiple models for better accuracy

Module 6: Neural Networks

➤ Introduction to Neural Networks

• Structure of a neuron

• Feedforward and backpropagation

➤ Deep Learning Basics

• Introduction to TensorFlow and PyTorch

• Activation Functions (ReLU, Sigmoid, Tanh)

➤ Specialized Architectures

• Convolutional Neural Networks (CNNs) for image data

• Recurrent Neural Networks (RNNs) for sequential data

Module 7: Model Deployment

➤ Preprocessing and Feature Engineering

• Handling missing data

• Feature scaling and encoding

➤ Model Deployment Techniques

• Flask and FastAPI for model serving

• Using Streamlit for building ML dashboards

➤ Cloud Deployment

• Deploying on AWS, Azure, and Google Cloud

Module 8: Special Topics and Trends

➤ Natural Language Processing (NLP)

• Text preprocessing (Tokenization, Stemming, Lemmatization)

• Sentiment analysis and text classification

➤ Computer Vision

• Image classification

• Object detection

➤ Ethics and Bias in ML

• Avoiding bias in datasets

• Explainable AI (XAI)

Become a Machnie Leanring Professional in Just 1 Month

Shape the Future of Connectivity with ML Certification

Machine Learning - Frequently Asked Questions (FAQs)

1. What is Machine Learning (ML)?

➤ Machine Learning is a subset of Artificial Intelligence (AI) that allows computers to learn and make decisions or predictions without being explicitly programmed. It uses data, algorithms, and statistical techniques to improve performance over time.

2. What are the types of Machine Learning?

➤ There are three main types of Machine Learning:

• Supervised Learning: Training models with labeled data (e.g., Classification, Regression).

• Unsupervised Learning: Working with unlabeled data to identify patterns (e.g., Clustering, Dimensionality Reduction).

• Reinforcement Learning: Training models through rewards and penalties based on actions taken.

3. What are some real-world applications of Machine Learning?

➤ Machine Learning is used in various fields, such as:

• Healthcare: Disease prediction, medical image analysis.

• Finance: Fraud detection, stock price prediction.

• E-commerce: Recommendation systems (e.g., Amazon, Netflix).

• Automotive: Self-driving cars.

• Customer Service: Chatbots and virtual assistants.

• Social Media: Content recommendations and sentiment analysis.

4. What are the prerequisites for learning Machine Learning?

➤ Basic knowledge of the following topics is helpful:

• Programming (e.g., Python, R)

• Mathematics (Linear Algebra, Calculus)

• Statistics and Probability

• Basic Data Analysis

5. What programming languages are used in Machine Learning?

➤ The most popular programming languages for Machine Learning are:

• Python: Libraries like NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch.

• R: Statistical analysis and visualization.

• Java/Scala: Used for production systems and big data tools (e.g., Apache Spark).

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