Learn the fundamentals of machine learning (ML) and build a strong foundation in supervised, unsupervised, and reinforcement learning. This comprehensive course will equip you with the tools and techniques needed to analyze data, train predictive models, and apply ML concepts to real-world problems.
Learn industry-relevant machine learning techniques and tools.
Work on hands-on projects to strengthen your understanding of ML workflows.
Receive industry-recognized certification to showcase your expertise.
What is Machine Learning?
Types of ML: Supervised, Unsupervised, and Reinforcement Learning.
Applications of ML in different industries.
Data cleaning, normalization, and transformation.
Handling missing values and outliers.
Feature engineering and selection.
Linear and Logistic Regression.
Decision Trees, Random Forests, and Gradient Boosting.
Support Vector Machines (SVM) and Neural Networks.
Model evaluation: accuracy, precision, recall, and F1 score.
Clustering algorithms: K-Means, DBSCAN, and Hierarchical Clustering.
Dimensionality reduction: Principal Component Analysis (PCA).
Introduction to Artificial Neural Networks (ANNs).
Basics of Deep Learning and TensorFlow.
Building Convolutional Neural Networks (CNNs) for image recognition.
Fundamentals of reinforcement learning.
Markov Decision Processes.
Applications in robotics and game development.
Introduction to time series data.
Forecasting techniques using ARIMA and LSTMs.
Real-world applications: stock price prediction, weather forecasting.
Text preprocessing techniques: tokenization, stemming, and lemmatization.
Sentiment analysis and topic modeling.
Introduction to transformers and BERT models.
Using Flask/Django for deploying ML models.
Tools like Docker and Kubernetes for scaling.
Model monitoring and optimization.
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