What You’ll Learn

• 1

  Fundamentals of Python Development

  Learn the basics of Python programming, including syntax, data structures, and foundational concepts, to prepare for more advanced topics in data science and machine learning.

• 2

  Introduction to Data Science and Machine Learning

  Explore the principles of data analysis, visualization, and machine learning. Gain hands-on experience with tools and techniques used to process and model data effectively.

• 3

  Classification Models

  Understand how to build and evaluate models that classify data into predefined categories. Study algorithms like logistic regression, decision trees, and support vector machines.

• 4

  Regression Models

  Learn how to predict continuous outcomes using regression techniques. Focus on algorithms like linear regression, polynomial regression, and regularization methods.

• 5

  Clustering Models

  Dive into unsupervised learning by exploring clustering techniques, such as k-means, hierarchical clustering, and DBSCAN, to group similar data points.

• 6

  Artificial Neural Networks

  Study the basics of neural networks, including their architecture, backpropagation, and applications in solving various complex problems.

• 7

  Introduction to Deep Learning

  Gain an understanding of deep learning fundamentals, including concepts like convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

• 8

  Advanced Deep Learning

  Master cutting-edge techniques in deep learning, such as transfer learning, generative adversarial networks (GANs), and fine-tuning large pre-trained models for specific tasks.

• 9

  Portfolio Builder

  Build an industry-relevant portfolio to showcase your skills and projects.

• 10

  Learn to Earn

  Develop professional skills like presenting projects, freelancing, and job readiness for career success.

Download Syllabus