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Easy Learning with Python for Data Science and Machine Learning
Development > Data Science
17.5 h
£44.99 Free for 0 days
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Sale Ends: 17 Mar

Master Data Science & Machine Learning with Python

What you will learn:

  • Python for Data Science
  • Pandas Data Analysis
  • Data Visualization with Matplotlib & Seaborn
  • Machine Learning with Scikit-learn
  • Deep Learning with TensorFlow
  • Natural Language Processing (NLP)
  • Computer Vision
  • Model Deployment
  • NumPy for Numerical Computing
  • SQL Database Integration with Python

Description

Launch your data science career with this in-depth Python course!

Unlock the power of Python to analyze data, build stunning visualizations, and master machine learning algorithms. This comprehensive course, a cost-effective alternative to expensive bootcamps, equips you with the skills to tackle real-world data challenges. Become proficient in data manipulation using Pandas and NumPy, create insightful visualizations with Matplotlib and Seaborn, and delve into machine learning techniques such as linear regression, decision trees, and support vector machines. You'll also explore deep learning using TensorFlow, and discover the world of natural language processing and computer vision. This isn't just theory; we'll guide you through practical projects and applications that will bolster your portfolio and prepare you for success.

This course covers:

• Foundational Python Programming

• Data Wrangling and Analysis with Pandas

• Data Visualization using Matplotlib and Seaborn

• Core Machine Learning Algorithms (Regression, Classification)

• Deep Learning with TensorFlow (DNNs and CNNs)

• Natural Language Processing (NLP) Techniques

• Computer Vision Applications

• Model Deployment Strategies

Enroll now and transform your data science aspirations into reality!

Curriculum

Introduction to Python Programming

This foundational section covers essential Python programming concepts, starting with environment setup and moving through core data types (integers, strings, booleans, lists, tuples, dictionaries). You'll learn about fundamental programming constructs like variables, operators, control flow (if statements, loops), functions (including lambda functions), and object-oriented programming principles. We'll also explore helpful libraries such as the `math` and `os` libraries. Lectures include: Setup the Environment, Print, comments, Variables, Strings, Arithmetic, Boolean Operations, Datatype Conversion, Lists, Tuples, Dictionaries, Data Type Conversion, Functions, Functions 2, Lambda, Math Library, OS Library, If Statements, While loop, For Loop, Object Oriented Programming (OOP), Constructor, Example.

Data Science Libraries: NumPy and Pandas

This section delves into the powerful NumPy and Pandas libraries, crucial for data science tasks. You'll learn to work with NumPy arrays, perform array operations, utilize the `pickle` library for data serialization, and master Pandas DataFrames for data manipulation and analysis. We will cover data processing techniques and visualize data using `matplotlib` and `seaborn`. Lectures include: Numpy, Array Operation, Pickle, Pandas, DataFrame, DataFrame 2, plot DataFrame, Data Processing, matplotlib, multi plot, Bar Chart, Example.

Machine Learning with Scikit-learn

This section introduces core machine learning algorithms using the Scikit-learn library. You will learn to apply linear and logistic regression, k-nearest neighbors, decision trees, random forests, and support vector machines for both classification and regression tasks. You'll also gain proficiency in evaluating model performance using key metrics. Lectures include: Scikit Learn, Linear Regression, Logistic Regression, KNN, Decision Tree, Random Forest, Support Vector Machine, Evaluation Metrics.

Deep Learning with TensorFlow

Dive into the world of deep learning using TensorFlow. This section covers building and training deep neural networks (DNNs) using different APIs (Sequential Model, Functional API, Subclassing API). Lectures include: Tensor Flow, Simple Model, Sequencial Model, Factional API, Factional API 2, Subclassing API.

Convolutional Neural Networks (CNNs) for Image Processing

Explore convolutional neural networks (CNNs) specifically designed for image processing. You'll learn about image labeling, data augmentation, and building CNN models. You'll also get introduced to transfer learning. Lectures include: Introdction, Labeling, Augmentation, plot images, Build the Model, Transfer Learning.

Computer Vision Applications

This section focuses on practical computer vision applications, including face recognition techniques. Lectures include: Applications, Face Recognition #1, Face Recognition #2, Face Recognition #3.

Natural Language Processing (NLP)

This section explores the field of Natural Language Processing (NLP), focusing on sequence modeling, machine translation techniques, building a chatbot, and methods for measuring text similarity. Lectures include: Sequence Modeling, Machine Translation #1, Machine Translation #2, Machine Translation #3, Machine Translation #4, Chatbot, Text Similarity #1, Text Similarity #2.

Model Deployment

Learn how to deploy your trained models for real-world applications. This section covers deployment using TensorFlow Lite and TFX Model Serving. Lectures include: TFLite, TFX Model Serving.

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