MACHINE LEARNING ONLINE TRAINING BY DEEKSHIT KUMAR

Description

Machine learning is a branch of artificial intelligence (AI) that allows computers to learn and develop on their own without having to be specifically programmed. Machine learning is concerned with the development of computer programmes that can access data and learn on their own.

Machine learning's main goal is to find trends in user data and then make predictions based on these and other complex patterns in order to address business questions and solve problems. Machine learning aids in the analysis of data as well as the detection of patterns.

Training Outcome
  • Course Complete Certificate


Requirements
  • Knowledge of Basic Computer Technology

Lessons

  • 23 Lessons
  • 23:00:00 Hours
  • (a) INTRODUCTION TO PYTHON (b) VARIABLES IN PYTHON01:00:00
  • (a)DATA TYPES IN PYTHON (b)CONDITIONAL STATEMENTS01:00:00
  • (a)CONDITIONAL STATEMENTS (b) LOOPING STATEMENTS01:00:00
  • (a) FUNCTIONS IN PYTHON (b) CASE STUDY PROBLEM 1 – GETTING STARTED WITH PYTHON (c) CASE STUDY PROBLEM 2 – INTRODUCTION TO PYTHON01:00:00
  • (a) INTRODUCTION TO NUMPY (b) INITIALIZING A NUMPY ARRAY (c) INSPECTING A NUMPY ARRAY01:00:00
  • (a) PERFORMING MATHEMATICAL FUNCTIONS USING (b) NUMPY NUMPY ARRAY MANIPULATION01:00:00
  • (a) INDEXING AND SLICING USING NUMPY (b) NUMPY VS LIST (c) CASE STUDY PROBLEM 1 – NUMPY01:00:00
  • (a) INTRODUCTION TO PANDAS (b) SERIES OBJECT IN PANDAS (c) DATAFRAME IN PANDAS01:00:00
  • (a) MERGE, JOIN AND CONCATENATE (b) IMPORTING AND ANALYZING DATASET (c) CLEANING THE DATASET01:00:00
  • (a) MANIPULATING THE DATASET (b) VISULAIZING THE DATA SETS (c) CASE STUDY PROBLEM 1 – PANDAS01:00:00
  • (a) WHAT IS DATA VISUALIZATION? (b) INTRODUCTION TO MATPLOTLIB01:00:00
  • (a) HOW TO CREATE A LINE PLOT? (b) HOW TO CREATE A BAR PLOT? (c) HOW TO CREATE A SCATTER PLOT? (d) HOW TO CREATE A HISTOGRAM?01:00:00
  • (a) HOW TO CREATE A BOX AND VIOLIN PLOT (b) HOW TO CREATE A PIE CHART AND DOUGHNUT CHART (c) HOW TO CREATE AN AREA CHART01:00:00
  • (a) VISUALIZATION ON DATASET (b) CASE STUDY PROBLEM 1 – DATA VISUALIZATION USING MATPLOTLIB01:00:00
  • PROJECT 1 : SALES DATA ANALYSIS PROJECT 2 : TEXT DATA ANALYSIS01:00:00
  • (a) INTRODUCTION TO MACHINE LEARNING (b) TYPES OF MACHINE LEARNING (c) WHAT CAN YOU DO WITH MACHINE LEARNING?01:00:00
  • (a) INTRODUCTION TO REGRESSION AND CLASSIFICATION (b) LINEAR REGRESSION01:00:00
  • (a) LOGISTIC REGRESSION (b) DECISION TREE (c) SUPPORT VECTOR MACHINE (SVM) (d) RANDOM FOREST 01:00:00
  • K MEANS CLUSTERING01:00:00
  • (a) BAG-OF-WORDS (b) CONVERT SENTENCES TO VECTORS USING BAG-OF-WORDS (c) COUNT VECTORIZER01:00:00
  • (a) TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (b) TEXT CLASSIFICATION (c) CONVERTING TEXT TO FEATURES AND LABELS01:00:00
  • (a) NAIVE BAYES CLASSIFIER (b) MULTINOMIAL INB (c) CONFUSION MATRIX01:00:00
  • (a) PROJECT DESCRIPTION (b) CAPSTON PROJECT (c) TEXT ANALYSIS01:00:00

About instructor

Instructor
Name : Course Monkey
Reviews : 2 Reviews
Student : 1 Students
Courses : 124 Courses

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