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Introduction to Machine Learning

This course offers an in-depth overview of Machine Learning (ML) principles, algorithms, and their practical uses. 

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. This course offers an in-depth overview of Machine Learning (ML) principles, algorithms, and their practical uses.

  2. Tailored for both novices and experienced individuals, it addresses the core aspects of machine learning, encompassing supervised, unsupervised, and reinforcement learning methods.

  3. Participants will engage in practical exercises with actual datasets, utilizing widely-used tools and libraries like Python and Scikit-learn.

  4. Upon completion, you will possess the skills to construct, assess, and implement fundamental machine learning models to address real-world challenges.

CERTIFICATION:

  1. Upon finishing the course, participants will be awarded a Certificate of Completion.

  2. This certificate signifies your grasp of machine learning fundamentals, algorithms, and practical application skills.

  3. You can include this certificate in your resume, LinkedIn profile, or portfolio to highlight your machine learning expertise to prospective employers.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1.  Participants will gain a foundational understanding of machine learning, including its definition and practical applications.

  2. They will be able to distinguish among supervised, unsupervised, and reinforcement learning methodologies.

  3. Participants will also learn to set up a Python environment for machine learning using tools like Jupyter Notebook and Anaconda, and utilize essential libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn.

  4. Additionally, learners will acquire skills in data preparation, including the collection, cleaning, and preprocessing of datasets, as well as techniques for managing missing values and scaling categorical data.

  5. They will build and train machine learning models by applying regression algorithms like Linear and Logistic Regression, implementing classification models such as Decision Trees, KNN, and Naive Bayes, and understanding clustering methods including K-Means and Hierarchical Clustering.

  6. Furthermore, participants will evaluate the performance of machine learning models through various metrics, including accuracy, precision, recall, F1 score, and confusion matrix, while employing techniques like Train-Test splits and Cross-Validation.

  7. The course will also introduce advanced topics such as Neural Networks, Deep Learning, Reinforcement Learning, and Natural Language Processing (NLP).

  8. Finally, learners will have the opportunity to construct and deploy basic machine learning models to address real-world challenges, such as predicting housing prices, classifying images, or analyzing customer churn.

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Course Curriculum

Foundations of Machine Learning
  1. Overview of Machine Learning (ML)
  2. Types of ML: Supervised, Unsupervised, and Reinforcement Learning
  3. Applications and real-world use cases of ML
Mathematics for Machine Learning
  1. Linear Algebra: Vectors, matrices, and operations
  2. Probability and Statistics: Basics of distributions, Bayes theorem, and hypothesis testing
  3. Calculus: Gradients and optimization
Data Preprocessing
  1. Data cleaning and handling missing values
  2. Feature scaling: Normalization and standardization
  3. Feature selection and dimensionality reduction
Supervised Learning
  1. Regression models: Linear and Logistic Regression
  2. Classification algorithms: Decision Trees, Random Forests, and Support Vector Machines (SVMs)
  3. Evaluation metrics: Accuracy, Precision, Recall, F1 Score
Unsupervised Learning
  1. Clustering techniques: K-means, Hierarchical clustering
  2. Dimensionality reduction: Principal Component Analysis (PCA)
  3. Anomaly detection
Neural Networks and Deep Learning
  1. Basics of neural networks: Perceptron, activation functions, and forward/backward propagation
  2. Introduction to deep learning frameworks (e.g., TensorFlow, PyTorch)
  3. Applications of neural networks: Image and text processing
Model Evaluation and Optimization
  1. Train-test split and cross-validation
  2. Overfitting and underfitting: Detection and mitigation
  3. Hyperparameter tuning: Grid search and Random search
Practical Applications
  1. Building ML models with Python (libraries like Scikit-learn, Pandas, and NumPy)
  2. End-to-end ML projects: From data preprocessing to model deployment
  3. Case studies: ML in healthcare, finance, and e-commerce
  4.  

Training Features

Hands-On Practice

Practical exercises and real-world projects to apply ML concepts using Python and popular ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch).

Interactive Learning Modules

Engaging lessons with quizzes, code challenges, and step-by-step tutorials to solidify understanding.

Access to Datasets

Preloaded datasets for experimentation and model building, including case studies from domains like healthcare, finance, and e-commerce.

Expert Mentorship

Guidance from industry professionals to clarify doubts, review projects, and provide feedback on progress.

Comprehensive Resources

Downloadable cheat sheets, formula summaries, and implementation guides to support independent learning.

Certification of Completion

Recognized certificate upon finishing the course, demonstrating proficiency in Machine Learning fundamentals and practical skills.

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