Machine Learning

Introduction to Python

• Download
• Anaconda Navigator Installation
• Create environment and download libraries
• Introduction to jupyter notebook

• Python Object & Data Structure
  • Numbers
    • Basic Arithmatic
    • Variable assignment
  • String
    • String
    • String indexing and slicing
    • String properties
  • List
    • Lists
    • List method
    • List comprehsions
  • Dictionary
    • Construct Dict
    • Dict Methods
  • Tuples
  • Sets and Booleans
• Python Statement
  • Intro python state , if elif else
    • introduction to python statements
    • if,elif and else statement part 1
    • if,elif and else statement part 2
  • For loop
    • Introduction to for loops
    • For loop examples
  • While loops , Useful Operators
    • while loop
    • break , cont , pass
    • useful operator part 1
    • useful operator part 2
• Methods and Function
  • lambda expression , nested statemet
    • map and filter
    • lambda expression
    • nested statement part 1
    • nested statement part 2
    • methods
    • functions part 1
    • functions part 2
    • list compressions
• OOPs
  • OOPs basics
  • OOPs inheritance
  • OOPs polymorphism
• Python Libraries
  • Numpy
    • Creating array
    • Using arrary and scalers

• Machine Learning application
• Machine learning Process
• How to become a machine learning engineer?
• Pattern Recognition

• What is AI
• What is deep learning
• AI tools and Models

• What is PGM
• MRF

• Introduction to statistic
• Statistical analysis process
• Kurtosis
• Co-relation matrix
• Statistics practical

• Data preparation process
• Type of Data
• Feature Scaling

• Logistic reg
  • Logistic regression Data preprocessing
  • Feture scalling _ model making
  • Visualize training results
• Multi and poly regression
  • Multiple linear regression
  • Polynomial regression part 1
  • Polynomial regression part 2
• Simple linear regression
  • Regression data preprocessing
  • Regression model making
  • Supervised learning introduction
  • Linear regression
  • LMS algorithm
  • Objective and application of linear regression
  • Multiple and polynomial regression
  • Logistic regression
  • Objective and model eval
  • Intro unsupervised learning
  • Semi-supervised and important consideration

• KNN practical
  • KNN Data preprocessing
  • KNN modeling
  • Visualize KNN model

• dt classifier
  • Decison tree Classifier
  • Visualize the DT
• dt regressor
  • step 1 making DT regression
  • step 2 DT Structure
• RF practical
  • RF practical part 1
  • RF practical part 2
• Decision tree regression
• Decision Tree Classification
• Random Forest

• SVM introduction
• SVM Mathematics
• Non-linear SVM

• Clustering Introduction
• K-means theory
• k-means Mathematical
• kmeans practical part 1
• kmeans practical part 2

• Rise of artificial neuron
• Introduction to ANN
• Perceptron
• Activation Functions
• Feed forward Neural networks
• Cost function in neural network
• Back-propagation neural network
• Introduction to CNN
• CNN arch and Convolutional layer
• Pooling layer and fully connected layer
• RNN introduction
• Recurrent neurons
• Various configuration of RNNs
• Training recurrent neural network
• Tensorflow Introduction
• Computationsl Graph
• ANN practical
  • Intro to ANN
  • Part 1 data preprocessing
  • Part 2 building ANN
  • Part 3 testing ANN
• CNN Practical
  • Import libraries
  • Part 1 data preprocessing
  • Part 2 Building the CNN
  • Part 3 Training CNN
  • Part 4 making a single prediction
• RNN practical
  • Part 1 data preprocessing
  • Part 2 Building RNN
  • Part 3 testing the model

• Basics of NLP
• NLP application
• Feature extraction
• Gaussian NB
• NLP practicals
  • NLP practical part 1
  • NLP practical part 2
  • NLP practical part 3

• Rf intro
• Case study overview
• Bellman eq
• MDP
• Q-learning
• Dynamic programming
• Q-learning practical
  • Q-learning practical part 1
  • Q-learning practical part 2
  • Q-learning practical part 3