Course Objectives for Machine Learning (ML) Training in Jaipur?
Learn Python from scratch
Use Python for Data Science and Machine Learning
Implement Machine Learning Algorithms
Learn to use NumPy for Numerical Data
Learn to use Pandas for Data Analysis
Learn to use Matplotlib for Python Plotting
Learn to use Seaborn for statistical plots
Use Plotly for interactive dynamic visualizations
Use SciKit-Learn for Machine Learning Tasks
Make predictions using linear regression, polynomial regression, and multivariate regression
Logistic Regression
K-Means Clustering
Random Forest and Decision Trees
Support Vector Machines
Neural Networks
Prerequisites -Python | Machine Learning(ML) Training in Jaipur
Goal: In this module, you’ll get a complete knowledge of python and it’s libraries which are going to be used in better understanding in problem solving of Machine Learning(ML).
Topics:
Getting Started with python
Data Types and Variables
Operators
Structural Data Types-Lists, Tuples, Strings & Dictionaries
Conditional Code
Loops and Iterations
Functions
Files I/O
Accessing Web Data
Python Libraries for Data Science| Machine Learning(ML) Training in Jaipur
Introduction to NumPy
Understanding Data Types in Python
Fixed-Type Arrays in Python
Creating Arrays from Python Lists
Creating Arrays from Scratch
NumPy Array Attributes
Reshaping of Arrays
Computation on NumPy Arrays: Universal Functions
Fancy Indexing
Sorting Arrays
Structured Data: NumPy’s Structured Arrays
Data Manipulation with Pandas
Installing and Using Pandas
Introducing Pandas Objects
Data Indexing and Selection
Operating on Data in Pandas
Handling Missing Data
Hierarchical Indexing
Combining Datasets: Concat and Append
Combining Datasets: Merge and Join
Aggregation and Grouping
Pivot Tables
High-Performance Pandas: eval() and query()
Visualization with Matplotlib
Importing matplotlib
Simple Line Plots
Simple Scatter Plots
Visualizing Errors
Density and Contour Plots
Histograms, Binnings, and Density
Multiple Subplots
Text and Annotation
Other Python Libraries for Data Scientists-
Scipy
Scikit-learn
Seaborn
Reading Data; Selecting and Filtering the Data; Data manipulation, sorting, grouping, rearranging
Plotting the data
Descriptive statistics
Inferential statistics
The Math Behind Machine Learning: Statistics| Machine Learning(ML) Training in Jaipur
The Math behind Machine Learning: Linear Algebra
Scalars
Vectors
Matrices
Tensors
Hyperplanes
Probability
Conditional Probabilities
Posterior Probability
Distributions
Samples vs Population
Resampling Methods
Selection Bias
Likelihood
Introduction to Machine Learning
What is it and where is it used ?
Major Applications and the companies using it
Overview of Types of ML
UNDERSTANDING SUPERVISED LEARNING| Machine Learning(ML) Training in Jaipur:
Model Overview : training and testing
Hypothesis formation
Understanding the prameters
COST function (derivation and application)
Types of Errors (SSE,SSR)
Computing Cost by hand
Computing Cost with numpy
Gradient Descent (derivation and types)
Computing Gradient descent with numpy
ALGORITHM : LINEAR REGRESSION:| Machine Learning(ML) Training in Jaipur
Linear Regression with single Variable
OLS (ordinary least square)Estimator
multi Variable Linear Regression
Normal Equation
R2 score
Project: Case Study
House Price Prediction
Implementation in numpy
Implementation in scikit-learn
ALGORITHM : LOGISTIC REGRESSION:
sigmoid function
Decision boundary
Cross-validation
Project: Case Study
IRIS Dataset Prediction
Implementation in numpy
Implementation in scikit-learn
ALGORITHM : Decision Trees:
Intro and Types
ID3 algo from scratch derviation
Regression and Classification Cases
Random Forests Algorithm
Project case study
Implementation in numpy
Implementation in scikit-learn
Clustering (K-Means)
Unsupervised
Features and data vectors
Various steps of algo
Understanding of Clusters and various types of
Applying K-Means on datasets and their practical use
Applications of Clustering and the algorithm
UNSUPERVISED ALGORITHMS| Machine Learning(ML) Training Institute in Jaipur
K-MEANS clustering
Generative Modeling Through Baysian Sampling
Project :Case Study
understanding image structure
creating color pallete with kmeans
Generating Handwritten Digits
Neural Network (NN)
What’s a Neural Network?
Various Structures of NN
Understanding Fundamentals and Various parameters of NN
ANN,CNN and RNN
Deep Dive with the Implementaion of NN on various datasets
Applying CNN on Images
Applications and its complexities over other algorithms Project:- Smart Machine Learning System
Integration of Machine Learning with OpenCV| Machine Learning(ML) Training Institute in Jaipur
Goal : In this module, you’ll about classical image analysis techniques such as Edge
detection,watershed,distance transformations using the OpenCV library .Here you’ll explore the evolution of image analysis ,from classical deep learning techniques.
Objectives - At the end of this module, you should be able to:
Introduction to computer vision and Image Processing
Image processing using OpenCV
Video processing and Image extraction using OpenCV
Convolutional Features for visual recognition
Object ,Face and Gestures Detection using Haar Cascade Classifier
Object Tracking and Action Recognition
This is all about summer, winter and regular training in Machine Learning (ML) using Python at Goeduhub Technologies-Jaipur. Apart from this student will also complete some real time projects during training.
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