Prismatic Webtech Services

Contents:

Chapter 1: Python Crash Course:

• Introduction to Python
• Environment setup- Jupyter notebook
• Crash course on basics of python

Chapter 2: Python for Data Analysis – Numpy

Chapter 3: Python for Data Analysis – Pandas

Chapter 4: Python for Data visualization – Matplotlib

Chapter 5: Python for Data visualization – Seaborn

Chapter 6: Data Capstone Project

Chapter 7: Introduction to Machine Learning

• Supervised & Unsupervised learning
• Statistics for machine learning.

Chapter 8: Linear Regression machine learning algorithm

Chapter 9: Logistic Regression machine learning algorithm

Chapter 10: Decision Trees & Random Forest machine learning algorithm

Chapter 11: Support Vector Machines machine learning algorithm

Chapter 12: K Nearest Neighbours machine learning algorithm

Chapter 13: K Means Clustering machine learning algorithm

Chapter 14: K fold cross validation technique

Chapter 15: Hyper tuning using GridSearchCV

Chapter 16: Natural Language Processing (NLP)

Chapter 17: Neural Nets & Deep Learning

• Introduction to Artificial Neural Networks (ANN)
• Installing TensorFlow
• Perceptron Model
• Neural Networks
• Activation Functions
• Cost functions & Gradient Descent
• Backpropagation
• Deep learning projects

Chapter 18: Convolutional Neural Network (CNN)

• Introduction & Applications
• Image classification using CNN