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Module 1:  Introduction

  • Supervised learning
  • Unsupervised learning
  • Introduction to reinforcement learning
  • Introduction to Machine Learning
  • Introduction to Deep learning
  • Understanding concept of Creating Machine-Learning Models with Python and sickie learn.
  • Types of datasets used in Machine Learning.
  • Life Cycle of Machine Learning

Module 2:  Installations

  • Installing Python
  • Installing PyCharm
  • Installing Anaconda
  • Installing Spyder
  • Installing Keras and TensorFlow

Module 3:  Basic Concept   

  • Supervised & unsupervised algorithms
  • Bias/variance, over-fitting/under-fitting
  • Regularization, optimization, Gradient descent
  • Understanding concept of Types of scaling techniques
  • Understanding concept of Eliminating Duplicate Entries
  • Understanding concept of Logistic Regression
  • Applying Logistic Regression to The Iris classification Task
  • Closing Our First Machine Learning Pipeline with a Simple Model Evaluator
  • Creating Formulas that predict the Future
  • Understanding Linear Regression

Module 4: Machine learning Algorithm

  • Gradient Descent Algorithm
  • Batch Gradient Descent
  • Stochastic Gradient Descent algorithm
  • Exploring Unsupervised Learning and Its Usefulness
  • Understanding concept of k-means clustering
  • Understanding concept of PCA
  • Understanding concept of Decision Trees Classifier
  • Understanding concept of Decision Tree Regressor
  • Understanding concept of Random Forest Classifier
  • Understanding concept of Random Forest Regressor

Module 5:     

  • Implementing Support Vector machines
  • Supervise Support Vector machines
  • Implementing KNN on the Data set
  • Decision Tree as Predictive Model
  • Dimensionality Reduction techniques

Module 6:   

  • Random Forest for Classification
  • Gradient Boosting Trees
  • Bayes Optimization
  • CatBoost to Handle Categorical Data
  • Memory-Based Collaborative Filtering
  • Item-to-Item Recommendation with kNN
  • Validation Dataset Tuning.
  • Regularizing model to avoid over fitting
  • Adversarial Validation

Module 7:     

  • Understanding concept of Decision Tree Rules from Scikit-learning
  • Understanding concept of Random Forest Model
  • Understanding concept of scikit-learn
  • Labelling Dimensions with Original Feature Names after PCA
  • Clustering Text Documents with Scikit-learn k-means
  • Listing Word Frequency in a Corpus Using Only scikit-learn
  • Polynomial Kernel Regression Using Pipelines
  • Visualize outputs over two dimensions using Numpy’s Meshgrid
  • Drawing out a Decision Tree Trained in scikit-learn
  • Clarify your Histogram by Labeling each Bin

Module 8: Deep learning

  • Deep neural networks (DNN)
  • Convolutional neural networks (CNN)
  • Recurrent neural networks (RNN)
  • Long short term memory networks (LSTM)
  • Auto encoders (AE)
  • Generative adversarial networks (GAN)
  • DL applications hands-on using Anaconda

 

Module 9: Python Library

  • Theano
  • Scikit-Learn
  • TensorFlow
  • Keras
  • PyTorch

Machine Learning with Python Syllabus

Introduction to Python

Python basic Operators

  • History
  • Features
  • Installation and Working with Python
  • Setting up path
  • Working with Python
  • Basic Syntax
  • Variable and Data Types
  • Operator
  • Understanding python blocks

Python Data Types

  • Declaring and using Numeric data types: int, float, complex
  • Using string data type and string operations
  • Defining list and list slicing
  • Use of Tuple data type
  • Building blocks of python programs
  • Understanding string in build methods
  • List manipulation using in build methods
  • Dictionary manipulation
  • Programming using string, list and dictionary in build functions

String Manipulation

  • Accessing Strings
  • Basic Operations
  • String slices
  • Function and Methods

Lists

  • Introduction
  • Accessing list
  • Operations
  • Working with lists
  • Function and Methods