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The Complete Data Science Road Map (Machine Learning Learning Path) is a comprehensive and structured journey designed to guide aspiring data scientists, machine learning engineers, and AI enthusiasts through the intricate landscape of machine learning. This carefully crafted roadmap provides a step-by-step progression, enabling learners to master the essential concepts, tools, and techniques that underpin modern data-driven decision-making and predictive modeling.
The learning path covers a wide spectrum of topics, ranging from foundational mathematics and statistics to hands-on coding and advanced machine learning algorithms. It equips learners with both theoretical knowledge and practical skills, ensuring a well-rounded understanding of the machine learning domain. Here's an overview of what the roadmap entails:
Throughout this roadmap, learners will engage in hands-on coding exercises, practical projects, and interactive challenges that reinforce their understanding and application of machine learning concepts. By the end of the Complete Data Science Road Map (Machine Learning Learning Path), participants will have developed a robust skill set, enabling them to tackle real-world problems, contribute to cutting-edge research, and thrive in the dynamic field of data science and machine learning.
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What is Machine Learning?
Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
Applications of Machine Learning
Data Manipulation with NumPy and Pandas
Data Visualization with Matplotlib and Seaborn
Logistic Regression
Decision Trees and Random Forests
Support Vector Machines
k-Nearest Neighbors
Hierarchical Clustering
Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Introduction to Neural Networks
Building and Training Neural Networks with TensorFlow/Keras
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Transfer Learning
Text Preprocessing
Word Embeddings (Word2Vec, GloVe)
Recurrent Neural Networks for NLP
Transformers (e.g., BERT)
Cross-Validation
Model Evaluation Metrics (Accuracy, Precision, Recall, F1-score)
Hyperparameter Tuning
Project 1
Project 2
Module 1: Python Basics: It will help learn the tool, Python to be used for working with data Module 2: Python NUMPY Library: It is used to p