Machine Learning, Data Science and Deep Learning with Python
This course covers the basics of Python and data analysis, including visualization and statistical methods. It teaches machine learning techniques such as regression, clustering, classification, and deep learning with practical applications. You will also learn about using advanced tools like Spark, Transformers, and OpenAI APIs for real-world data processing and model deployment.
Skills for certificate:
Python
Scikit Learn
NumPy
Pandas
Matplotlib
Seaborn
Keras
TensorFlow
Jupyter Notebooks
Apache Spark
MLLib
OpenAI
Git
GitHub
Poetry
Computer Vision
Mathematics
Linear Algebra
Probability
Problem Solving
Project Management
Critical Thinking
Creativity
Adaptability
Object Oriented Programming
Algorithms
Machine Learning
Data Science
Continuous Integration
Neural Networks
Data Visualisation
Artificial Intelligence
Deep Learning
Reinforcement Learning
Version Control
Machine Learning, Data Science and Deep Learning with Python
UC-783219a7-f03e-4f0e-b5dd-c643e18e0d4e
Description
This course covers the basics of Python and data analysis, including visualization and statistical methods. It teaches machine learning techniques such as regression, clustering, classification, and deep learning with practical applications. You will also learn about using advanced tools like Spark, Transformers, and OpenAI APIs for real-world data processing and model deployment.
Learning Objectives
- Understanding Python basics and the Pandas library
- Learning and using mean, median, and mode in Python
- Understanding and exploring probability density functions, mass functions, and common data distributions
- Calculating percentiles and moments
- Creating visualizations with matplotlib and Seaborn
- Analyzing covariance and correlation
- Performing multiple regression, predicting car prices, and distinguishing between supervised and unsupervised learning
- Preventing overfitting using train/test methods and K-Fold Cross-Validation
- Exploring Bayesian methods
- Clustering data using K-Means and analyzing clusters based on income and age
- Measuring entropy and predicting decisions with decision trees
- Implementing ensemble learning and XGBoost
- Using SVM to cluster data
- Finding and improving movie similarities using cosine similarity
- Predicting ratings using K-Nearest-Neighbors
- Reducing dimensions using PCA and performing PCA on the Iris dataset
- Understanding ETL and ELT in data warehousing
- Implementing reinforcement learning and Q-learning
- Understanding and measuring classifiers with confusion matrices, precision, recall, F1, ROC, and AUC
- Balancing bias and variance
- Cleaning, normalizing data, and handling outliers
- Engineering features, imputing missing data, and understanding the curse of dimensionality
- Handling unbalanced data with oversampling, undersampling, and SMOTE
- Transforming, encoding, scaling, and shuffling data
- Understanding and using Spark, MLLib, and the Resilient Distributed Dataset for machine learning
- Deploying models to real-time systems
- Understanding and conducting A/B testing, T-tests, and interpreting p-values
- Running experiments and determining duration
- Understanding deep learning basics, using TensorFlow, and implementing Keras for predictions
- Using CNNs for image recognition and RNNs for sentiment analysis
- Applying transfer learning techniques
- Tuning and regularizing neural networks for optimal performance
- Using variational auto-encoders with Fashion MNIST and understanding GANs
- Learning advanced deep learning concepts
- Understanding transformer architecture, GPT applications, and reinforcement learning from human feedback in GPT
- Tokenizing text, applying positional encoding, and implementing masked, multi-headed self-attention with transformers
- Using GPT models in Google CoLab and HuggingFace
- Using the OpenAI Chat Completions API, DALL-E API, and Embeddings API
- Fine-tuning GPT models and moderating content with the OpenAI Moderation API
- Converting speech to text with the OpenAI Audio API
- Simulating data and measuring RAG metrics with Retrieval Augmented Generation (RAG)
- Evaluating RAG-based data with RAGAS and langchain
- Implementing advanced RAG techniques like chunking, query rewriting, and prompt compression
- Simulating data with advanced RAG and langchain techniques