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Complete Data Science Road Map (Machine Learning Learning Path)

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USD 100.00 USD 75.00

25% Discount

The first class is FREE upon registration.

Akhil Vydyula

Course Language: EN (English)

This course includes:

30h 0m live online session

Certificate of completion

Level:
Beginner
Target Audience:
  • Beginning developers who wish to advance their skills by using python
  • Beginners into Machine Learning
Course objective:
  • Learn the concepts of Python, Machine learning, Deep Learning, Time series.
  • Implement Real World Projects with Proof Of Concept
Course prerequisites:
  • There is no specific prerequisite to learn machine learning. But you need to be from engineering/science/Maths/Stats background to understand the theory and the techniques used.
  • You need to be good in mathematics. If you are not, you can still learn machine learning, but you may face difficulty when solving complex real-world problems. Many say you need to know Linear algebra, Calculus etc., but I never learnt them, yet I am able to work on machine learning.
Video Recording Available:
  •  No

Description :

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.

John Doe

Akhil Vydyula

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India
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Course Content : Expand all sections

  • 30 lectures
  • 30h 0m total length

What is Machine Learning?

Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

Applications of Machine Learning

Python Basics

Data Manipulation with NumPy and Pandas

Data Visualization with Matplotlib and Seaborn

Linear Regression

Logistic Regression

Decision Trees and Random Forests

Support Vector Machines

k-Nearest Neighbors

K-Means Clustering

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

Project 3

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