Machine Learning Training

BY
Mindmajix Technologies

Acquaint yourself with the core concepts of machine learning through the online training by Mindmajix.

Mode

Online

Fees

₹ 11200 14000

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom
Mode of Delivery Video and Text Based
Frequency of Classes Weekdays, Weekends

Course and certificate fees

Fees information
₹ 11,200  ₹14,000
certificate availability

Yes

certificate providing authority

Mindmajix Technologies

What you will learn

Machine learning Knowledge of python Knowledge of artificial intelligence

The syllabus

Python Programming

  • Introduction to Python

Anaconda Navigator Download & Installation

  • Anaconda Navigator Download
  • Anaconda Navigator Installation
  • Create environment and download libraries
  • Introduction to jupyter notebook
  • Python Object & Data Structure
  • Python Statement
  • Methods and Function
  • OOPs
  • Python Libraries
Python Object & Data Structure
  • Numbers
  • String
  • List 
  • Dictionary
  • Tuples
  • Sets and Booleans
Python Statement
  • Intro python state , if elif else
  • For loop
  • While loops , Useful Operators
Methods and Function
  • lambda expression , nested statement
OOPs
  • OOPs basics
  • OOPs inheritance
  • OOPs polymorphism
Python Libraries
  • Numpy
  • Pandas
  • Matplotlib
  • seaborn

Python Object & Data Structure

Numbers
  • Basic Arithmetic
  • Variable assignment
String
  • String
  • String indexing and slicing
  • String properties
List
  • Lists
  • List method
  • List comprehensions
Dictionary
  • Construct Dict
  • Dict Methods

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 statement
  • map and filter
  • lambda expression
  • nested statement part 1
  • nested statement part 2
  • methods
  • functions part 1
  • functions part 2
  • list compressions

Python Libraries

Numpy
  • Creating array
  • Using array and scalers
  • Indexing arrays
  • Array transposition
  • Universal array functions
  • Array processing
Pandas
  • Series
  • Index objects
  • Reindex
  • Drop entry
  • Selecting entries
  • Data alignment
  • Rank and sort
  • Missing Data
Seaborn
  • Seaborn categorical plots part 1
  • Seaborn categorical plots part 2
  • Seaborn categorical plots part 3
  • Seaborn distribution plot part 1
  • Seaborn distribution plot part 2
  • Seaborn regression plot
  • Seaborn style and color

What is Machine Learning

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

Artificial intelligence

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

Graphica Models

  • What is PGM
  • MRF

Stats and Prob

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

Data Pre-Processing

  • Data preparation process
  • Type of Data
  • Feature Scaling

Machine Learning Types

  • Logistic reg
  • Multi and poly regression
  • Simple linear regression
Logistic reg
  • Logistic regression Data preprocessing
  • Feature scaling _ 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

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

Decision Tree

  • dt classifier
  • dt regressor
  • RF practical
  • Decision tree regression
  • Decision Tree Classification
  • Random Forest
dt classifier
  • Decision 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

SVM

  • SVM introduction
  • SVM Mathematics
  • Non-linear SVM

Clustering Analysis

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

ANN

  • 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
  • CNN Practical
  • RNN Practical
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

NLP

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

Reinforcement Learning

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

Instructors

Mr Abhishek
Instructor
Mindmajix Technologies

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