Machine Learning Training

About Machine Learning

Today’s world is advancing with technology. If we observe from the past decade Artificial Intelligence, and Machine Learning are emerging into the computer science world taking the technology to the next level. Machine Learning is simply nothing but the computers to act themselves without a definite program. our Lucency Technologies is the best institute for machineachine learning training in KPHB Hyderabad.we are providing Internships on Machine Learning and Artificial Intelligence also.For the purpose of Data Analysis, Machine Learning is extensively used as the high-level application platform. Many of us think that Machine Learning is almost similar to Artificial Intelligence but it is totally misconception, using Machine Learning we can do Data Mining. In order to automate the decision models, Machine Learning is exclusively implemented. Today’s world is advancing with technology. If we observe from the past decade Artificial Intelligence, and Machine Learning are emerging into the computer science world taking the technology to the next level. Machine Learning is simply nothing but the computers to act themselves without a definite program. For the purpose of Data Analysis, Machine Learning is extensively used as the high-level application platform. Many of us think that Machine Learning is almost similar to Artificial Intelligence but it is totally misconception, Machine Learning is similar to that of Data Mining. In order to automate the decision models, Machine Learning is exclusively implemented.

Lucency Technologies is having expert staff in training Machine Learning with Python. Our working professionals are well versed with the techniques in training Machine Learning with Python. Machine Learning with Python has some special value because it is having a strong hold in creating ML algorithms. This combination is universally accepted as the best and robust platform for having machine learning systems. The reason why Python is popular with Machine Learning is because the Python is having some special libraries for performing algorithms in machine learning.

Prerequisites: How you will get trained in Lucency Technologies?
							  
			                           Structure of our Machine Learning Syllabus Follows.
							 
						

MACHINE LEARNING SYLLABUS

NOTE: Almost every task is explained with an example

Introduction along with Q&A Section: Mathematical Importance and Basic Mathematics. Pre-Algebra Intermediate Algebra & Linear Algebra: Mathematics for Analytical Thinking Decision Science and Machine Learning: Python For data Science:
  1. Using Python As a calculator
  2. Variables
  3. Value Assignment
  4. Data Types in Python
    • Simple Arithmetic Operations
    • Comparison Operators
    • Type Functionality
    • Data type Conversions
Sequences: Lists: 1. Creation of Lists 2. Indexing (positive and negative) 3. Slicing 4. Comprehension 5. List traversal 6. List in Boolean context 7. List methods (CRUD operations) 8. Copy methods on List 9. Functionality of operators supported by lists Tuples: 1. Creation of tuples 2. Advantages of tuple over list 3. Indexing and slicing 4. comparison of List and Tuple 5. Tuple comprehension/ generator expression 6. When to use tuple and list combination [real time example] String: 1. What is a string? 2. Single, double and triple quotes 3. Raw string 4. String in Boolean context 5. Indexing and slicing 6. String traversal 7. String manipulations 8. String methods 9. Formatting a string 10. Converting string to list 11. Converting list to string 12. Functionality of operators supported by strings 13. Join, split, find, index etc. Set: 1. What is a set? 2. Why can’t we use mutable objects inside the Set? 3. Why indexing and slicing is not possible in Set? 4. How to Identify a 5. Set is mutable 6. Set operations 7. Modifying the Set 8. Removing the elements from set 9. Built-in functions with Set Dictionary: 1. Creating a dictionary 2. Reading keys and values from dictionary 3. Reading only keys from dictionaries 4. Reading only values from dictionaries 5. Updating key value pair in dictionary 6. Upset in a dictionary 7. Why can’t we use mutable object as a key in dictionary 8. Functionality of operators supported by dictionaries Functions: 1. What are the uses of Function? 2. Predefined functions for Type conversations 3. How to define a function? 4. Syntax 5. How to make function calls 6. What are arguments? 7. Why does we need return statement? 8. Types of arguments 9. Nested Functions 10. Recursive Functions 11. Advantages and Disadvantages of a recursive Function 12. Anonymous Functions/Lambda functions 13. Map, filter and reduce predefined functions Modules & Packages: 1. What is module 2. Advantages of a module 3. How to define a module 4. How to import a module 5. How to import a specific task in a module 6. Difference between a script and module 7. What is a Doc string? 8. What is Pylint score? Why is it important? 9. What is __name__ and How does it act with module? 10. What is a Package? 11. How does a python understand a folder as python package? 12. What happens when we import a package 13. Understanding predefined datetime module in python File Handling 1. What is a data, Information File? 2. File Objects 3. File Different Modes and Object Attributes 4. How to create a Text Fil and Append Data to a File and Read a File 5. Closing a file 6. Read, read line ,read lines, write, write lines…!! 7. Renaming and Deleting Files 8. Directories in Python 9. Working with CSV files and CSV Module 10. Handling IO Exceptions Introduction to Pandas and numpy Introduction to Machine Learning Introduction to robot Framework(Automation) Introduction to web Frameworks (web developer) Introduction to webscrapping NumPy:Numerical Python : Introduction about NumPy Creating NumPy Arrays Different NumPy Operations Matrix Vectors Broadcasting with NumPy Solving Equations with NumPy Pandas: Introduction about Pandas Data Structures in Python Reading or Loading data into Data frame. Pandas Data Frame Manipulations Data Loading /Reading in different formats CSV Excel Json HTML Explorative Data Analysis. Data Cleaning and Pre-processing/Wrangling Introduction to data Visualizations What is Data Visualization? Theoretical Principles Behind Data Visualizations Histograms-Visualize the Distribution of Continuous Numerical Variables Boxplots-Visualize the Distribution of Continuous Numerical Variables Boxplots-Visualize the Distribution of Continuous Numerical Variables Bar Plots Pie Chart Line Chart Statistics and Probability concepts to understanding Machine learning. Machine Learning Unsupervised Learning in Python Ideology about Unsupervised Learning K- Means Theory/ Implementation Quantifying K-Means Clustering Performance Selection criteria for number of clusters choosing. Hierarchical Clustering Theory / Implementation Principal Component Analysis (PCA) theory / Implementation Supervised Learning. Ideology about Unsupervised Learning Classification Problems Regression Problems Classification and Regression Accuracy metrics Data Preparation Steps for Supervised Learning. Using Logistic Regression as a Classification Model RF – Classification RF – Regression Naive Bayes Classification Support vector Machines (SVM) -Linear Classification Support vector Machines (SVM) - Non-Linear Classification Support Vector Regression (SVR) KNN classification KNN – Regression Gradient Boosting Regression Gradient Boosting Classification Linear Discriminant Analysis (LDA) NLP: Natural Language Processing. Regular Expressions & Word tokenization Simple Topic Identification Named Entity Recognition Building fake News Classifier Artificial Neural Networks (ANN) and Deep Learning (DL) Basics of deep learning and neural networks Perceptron for Binary Classification ANN_ Binary Classification Multilabel Classification With MLP Regression With MLP MLP with PCA on Large Datasets DNN Introduction Specify the Activation Function Default H20 Deep Learning Algorithm H20 Deep Learning for Prediction Optimizing a neural network with backward propagation Building deep learning models with keras Fine-tuning keras models Linear Programming Introduction about ANN Theory and Implementation RNN RNN_LSTM Basic Image Processing Using CNN Foundations of Text mining Foundations of Chat Boatmaking Foundations of bigdata Visualizations Using the Tableau introduction.