Advanced Machine Learning & Deep Learning Masterclass 2024
- Description
- Curriculum
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Welcome to the Advanced Machine Learning & Deep Learning Masterclass 2024! This comprehensive course is designed for both business professionals and researchers, offering over 24 hours of in-depth video content. Whether you’re new to Python programming or experienced in the field, this course equips you with essential machine learning and deep learning techniques, from foundational Python skills to advanced neural network architectures.
What You Will Learn:
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Python for Machine Learning: Set up the environment, use popular tools like Anaconda and PyCharm, and learn Python basics through step-by-step tutorials.
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Data Understanding & Preprocessing: Dive deep into statistical analysis, data pre-processing techniques, feature selection, and data visualization with Python.
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Artificial Neural Networks: Build neural networks from scratch, explore deep learning frameworks like Keras, and implement a full deep learning project on handwritten digit recognition.
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Advanced Deep Learning Mastery: Go beyond the basics with comprehensive modules on Convolutional Neural Networks (CNNs), transformers, large language models, and deep generative models. You’ll learn how to construct and train models that power today’s AI innovations, including reinforcement learning and sequence models.
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Naive Bayes Classifier & NLP: Learn the fundamentals of Naive Bayes classification and explore natural language processing, including tokenization, part-of-speech tagging, and real-world NLP projects.
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Linear & Logistic Regression: Master regression models with hands-on demos for univariate and multivariate scenarios.
With practical hands-on demos, coding exercises, and real-world projects, this course is ideal for data scientists, AI enthusiasts, and anyone eager to master machine learning and deep learning concepts. By the end, you’ll have the knowledge and skills to apply these techniques to complex, real-world problems.
Enroll today and take your machine learning expertise to the next level!
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Python For Absolute Beginners - Variables - Part 1 -
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Python For Absolute Beginners - Variables - Part 2
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Python For Absolute Beginners - Variables - Part 3
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Python For Absolute Beginners - Lists
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Python For Absolute Beginners - Lists Part 2
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Python For Absolute Beginners - Lists Part 3
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If conditions
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If else
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boolean
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loops
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demo
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Simple Python Demos
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Software Design - Flowcharts - Sequence
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Software Design - Repetition
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Software Design - Problem Solving
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Flowcharts Questions and Answers # Problem Solving
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Understanding Data with Statistics: Reading data from file
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Understanding Data with Statistics: Checking dimensions of Data
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Understanding Data with Statistics: Statistical Summary of Data
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Understanding Data with Statistics: Correlation between attributes
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Data Pre-processing - Scaling with a demonstration in python
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Data Pre-processing - Normalization , Binarization , Standardization in Python
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feature Selection Techniques : Univariate Selection
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Introduction to Artificial Neural Networks
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Creating the First ANN from Scratch with Python
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Multiple Input Neuron
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Creating a simple layer of neurons, with 4 inputs. # Python # From scratch
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ANN - Illustrative Example
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KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step
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Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ]
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Setting up the Environment for NLP - ACH
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Introduction to Tokenization
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Downloading and Setting up NLTK
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Tokenization Tutorial
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Introduction to Normalization
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Normalization Tutorial
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Introduction to Part of Speech Tagging
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Part of Speech Tagging Tutorial
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Introduction to Stopwords
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Named Entity Recognition Lecture
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Named Entity Recognition Tutorial
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Classification Lecture
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Classification Tutorial Part 1: Preprocessing movie reviews
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Classification Tutorial Part 2: Feature Sets
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Classification Tutorial Part 3: Naive Bayes
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Classification Homework Exercise
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Real World Applications of NLP [Complete Project] - Introduction
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Creating a Twitter Application
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Getting the Test Set
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Preparing the Training Set
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Preprocessing
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Classification
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Testing the Model
