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Machine Learning with Python

Acquire expertise in Machine Learning (ML) with Python through this practical course

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Acquire expertise in Machine Learning (ML) with Python through this practical course. 

  2. Discover how to construct, train, and assess ML models for practical use cases, utilizing well-known Python libraries such as Scikit-learn, TensorFlow, and Pandas. 

  3. Suitable for both novices and future data scientists, this course covers essential machine learning principles and methods. 

  4. Gain the skills needed to derive insights and make informed, data-driven choices. 

  5. Engage in hands-on learning to effectively apply machine learning techniques in real-world scenarios.

CERTIFICATION:

  1. Earn a Certified Machine Learning with Python Practitioner credential, showcasing your expertise in applying machine learning techniques using Python.

LEARNING OUTCOMES:

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

  1. Grasp fundamental concepts and algorithms in machine learning, focusing on supervised, unsupervised, and reinforcement learning. 

  2. Utilize Python libraries like Pandas and NumPy for data preprocessing and cleaning. 

  3. Develop and train machine learning models with Scikit-learn. 

  4. Assess model effectiveness using metrics including accuracy, precision, recall, and F1-score.

  5. Apply advanced techniques such as ensemble learning, decision trees, and random forests, while exploring neural networks with TensorFlow and Keras for deep learning applications.

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Course Curriculum

Introduction to Machine Learning
  1. What is Machine Learning?
  2. Types of Machine Learning:
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  3. Real-world applications of Machine Learning.
  4. The Machine Learning workflow.
Python for Machine Learning
  1. Python basics for Machine Learning: Variables, data types, and control structures.
  2. Introduction to essential Python libraries:
    • NumPy: Array operations and linear algebra.
    • pandas: Data manipulation and cleaning.
    • Matplotlib and Seaborn: Data visualization.
Data Preprocessing and Exploration
  1. Data collection and cleaning: Handling missing values, duplicates, and outliers.
  2. Feature scaling and transformation: Normalization and standardization.
  3. Exploratory Data Analysis (EDA):
    • Visualizing data distributions.
    • Correlation analysis.
    • Insights generation.
  4.  
Supervised Learning
  1. Regression:
    • Linear Regression.
    • Polynomial Regression.
  2. Classification:
    • Logistic Regression.
    • Decision Trees.
    • Random Forests.
    • Support Vector Machines (SVM).
    • k-Nearest Neighbors (k-NN).
Unsupervised Learning
  1. Clustering:
    • K-Means Clustering.
    • Hierarchical Clustering.
    • DBSCAN.
  2. Dimensionality Reduction:
    • Principal Component Analysis (PCA).
Advanced Topics
  1. Ensemble Methods:
    • Bagging: Random Forests.
    • Boosting: Gradient Boosting, AdaBoost, and XGBoost.
  2. Feature Engineering: Creating and selecting the best features for the model.
  3. Introduction to Natural Language Processing (NLP): Tokenization and text classification.
Deployment of ML Models
  1. Saving and loading ML models using joblib or pickle.
  2. Introduction to Flask/Django for deploying Machine Learning models.
  3. Integrating ML models with web applications.
Capstone Project
  1. Complete an end-to-end Machine Learning project:
    • Example topics:
      • Predicting house prices.
      • Customer segmentation for marketing.
      • Credit card fraud detection.
      • Building a spam email classifier.
    • Steps:
      • Data preprocessing.
      • Model training and testing.
      • Model evaluation and optimization.
      • Deployment of the model.

Training Features

Hands-On Learning

Extensive practice with coding exercises and real-world datasets.

Comprehensive Coverage

Covers core Machine Learning concepts and Python libraries.

Project-Based Approach

Gain practical experience by working on projects in diverse domains like finance, healthcare, and marketing.

Career-Ready Skills

Prepare for roles such as Data Scientist, Machine Learning Engineer, and AI Specialist.

Interactive Learning Tools

Leverage Jupyter Notebooks, Python IDEs, and cloud-based platforms for a seamless learning experience.

Certification

A globally recognized certificate upon completing the course.

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