Machine Learning Mastery: From Basics to Real-World Applications

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About Course

This course is a hands-on, beginner-to-advanced journey into the world of Machine Learning (ML). You’ll start from scratch, learning the foundational math and Python tools, and then move on to key ML algorithms used in real-life applications like predictions, classifications, recommendations, and clustering.

You’ll implement everything practically using Python, NumPy, pandas, scikit-learn, and Matplotlib/Seaborn, with projects and case studies in healthcare, finance, e-commerce, and more. By the end of the course, you’ll not only understand how ML works but also know how to build, evaluate, and optimize real-world machine learning models.

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What Will You Learn?

  • Understand the fundamentals of Machine Learning and its categories
  • Perform data preprocessing, feature selection, and cleaning
  • Use Python libraries like NumPy, pandas, scikit-learn, and Matplotlib
  • Build and evaluate supervised learning models (Linear Regression, Decision Trees, etc.)
  • Apply unsupervised learning techniques like Clustering and PCA
  • Tune hyperparameters and avoid overfitting
  • Work on real-world datasets and industry-based ML projects
  • Build a neural network using TensorFlow/Keras
  • Develop a job-ready portfolio for internships, jobs, or freelancing

Course Content

Introduction to Machine Learning
This module provides a high-level understanding of what Machine Learning (ML) is and why it's important. Topics Covered: What is Machine Learning? Differences between ML, AI, and Deep Learning. Types of ML: Supervised Learning (with labeled data), Unsupervised Learning (without labels), Reinforcement Learning (based on reward systems). Applications: Image recognition, fraud detection, recommendation systems, predictive maintenance, etc. ML Workflow Overview: Data collection → preprocessing → model building → evaluation → deployment.

  • What is Machine Learning?
  • AI vs ML vs Deep Learning
  • Tools Required for ML
  • Your First ML Code

Python for Machine Learning
Python is the most commonly used language in ML. This module ensures you're ready with the required tools and libraries. Topics Covered: Python Basics – Variables, loops, functions, classes. NumPy – Efficient numerical computations (arrays, matrices). Pandas – Data manipulation and cleaning (DataFrames, filtering, merging). Matplotlib & Seaborn – Visualizing data. Scikit-learn – Python’s main ML library for model building and evaluation. Jupyter Notebooks – Writing, running, and documenting ML code interactively.

Data Preprocessing & Feature Engineering
Before feeding data into ML models, it must be clean and well-prepared. Topics Covered: Data Cleaning: Handling missing values, duplicates, outliers. Data Transformation: Normalization, standardization, encoding categorical variables (Label Encoding, One-Hot Encoding). Feature Engineering: Creating new features from existing ones (e.g., from a "Date" column extract "Month"). Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) to reduce feature space while retaining information.

Supervised Learning Algorithms
You learn how to train models using labeled data. Topics Covered: Regression Algorithms: Linear Regression, Polynomial Regression. Classification Algorithms: Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forest, Support Vector Machines (SVM), Naive Bayes. Use Cases: Predicting house prices (regression), classifying emails as spam/ham (classification).

Model Evaluation & Tuning
This module focuses on how to assess the performance of models and improve them. Topics Covered: Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, ROC Curve, AUC. Confusion Matrix – Understanding true/false positives/negatives. Cross-Validation – Ensuring model performance is reliable on unseen data. Hyperparameter Tuning: Grid Search, Random Search, using GridSearchCV and RandomizedSearchCV.

Unsupervised Learning Algorithms
You explore how to find hidden patterns in unlabeled data. Topics Covered: Clustering Algorithms: K-Means, Hierarchical Clustering, DBSCAN. Dimensionality Reduction: PCA, t-SNE for visualization of high-dimensional data. Use Cases: Customer segmentation, anomaly detection, document classification.

Real-World Projects in ML
This module allows you to apply what you've learned to real datasets. Projects May Include: Loan Default Prediction – Using classification to predict if a loan applicant will default. Stock Price Forecasting – Using regression for future price predictions. Customer Churn Prediction – Identifying customers likely to stop using a service. Movie Recommendation System – Collaborative filtering using user preferences. Skills Practiced: End-to-end pipeline development. EDA (Exploratory Data Analysis), modeling, evaluation, and presentation.

Introduction to Deep Learning & Beyond
You get a glimpse into more advanced ML topics and neural networks. Topics Covered: What is Deep Learning? Introduction to Neural Networks and how they differ from traditional ML. ANN (Artificial Neural Networks) – Basic architecture and training. Frameworks – Introduction to TensorFlow and Keras. Beyond Deep Learning (overview only): CNNs (Computer Vision), RNNs (Time Series & NLP), GANs (Image generation), Transformers (like ChatGPT).

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About us

Invigo Infotech Provide Industrial Training Courses Online For All Branches, Offline for ECE, Computer Science and by collaboration other Company on their related field, like civil, mechanical, ECE, Ee etc.