Accelerated Machine Learning Using Python
₹8,000.00
₹4,499.00
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Week 1
- 1.1 Introduction And Installation
- 1.2 Numbers and Strings
- 1.3 Lists and Dictionaries
- 1.4 Assignment Operators
- 2.1 Development Environment
- 2.2 Visual Studio Code: [VS_Code]
- 2.3 Conditional Statements
- 2.4 User Input
- 2.5 WHILE Loop
- 3.1 FOR Loop
- 3.2 FOR Loop: (Dictionary Enumeration)
- 3.3 Functions
- 4.1 Class and Objects
- 4.2 Constructors
- 5.1 Exception handling
- 5.2 Modules
- 5.3 Statistics Module
- 6.1 CSV Module
- 6.2 PIP
- 6.3 Jupyter Note Book
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Week 2
- 1.1 SQLite
- 2.1 Introduction to ML
- 2.2 Types of Machine Learning
- 2.3 Different Supervised Learning Algorithms
- 3.1 Tkinter
- 3.2 Making [.exe] in Python
- 4.1 Linear Regression + Introduction
- 4.2 Linear Regression Mathematics
- 4.3 Linear Regression Implementation
- 5.1Rock Paper Scissor Python Game
- 6.1 Regression Using Karl Pearson Coefficient
- 6.2 Linear Regression using Karlpearson Coefficient Implementation
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Week 3
- 1.1 Message Encode Decode in Python Project
- 2.1 Linear Regression Library Implementation
- 2.2 Loss Analysis Using Mse
- 2.3 Mean Squared Error
- 3.1 Calculator in Python
- 4.1 Goodness Of Fit
- 4.2 R-Squared Implementation
- 5.1 R Squared Using Karl Pearson Coefficient
- 5.2 R-Square Dusing Kral pearson
- 5.3 Library Implementation of Metrics
- 6.1 Loss Optimizer
- 6.2 Gradient Descent Implementation
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Week 4
- 1.1 Data Processing Using Pandas
- 1.2 Train Test Split
- 1.3 Classification Models
- 2.1 Logistic Regression
- 2.2 Loss For Classification Models
- 2.3 Log Loss Implementation
- 2.4 Score Analysis Basics
- 3.1 Confusion Matrix Implementation
- 3.2 Precision & Recall
- 3.3 F1 Score
- 4.1 Google Colabration
- 4.2 K Nearest neighbors
- 5.1 Iris
- 5.2 Support Vector Machine
- 5.3 Decision Tree Classifier
- 6.1 Digit Classification
- 6.2 K Means Clustering
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Python Projects Source Code
Accelerated Machine Learning Using Python
Footnotes :
- Course is dispersed over a 4 weeks accelerated curriculum.
- It covers all the aspects required to implement Machine Learning in projects and crack interviews.
- Course has inclination of mathematical conceptualization and code implementation.
- Intended for anyone who has a 10+2 level of education.
- Best preferred for Engineering, Computer Science, Mathematics, Physics, Economics or Management graduates.
- No mathematical prerequisites required.
Week 1 Learning Objectives :
- Learning Python from basics.
Week 2 Learning Objectives :
- Work on Python Projects.
- Start Machine Learning.
Week 3 Learning Objectives :
- Regression in Depth.
- Classification in Depth.
Week 4 Learning Objectives :
- 5 Models with end to end datasets
- Google Colab
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Course Features
- Lectures 62
- Quizzes 0
- Students 1
- Certificate No
- Assessments Yes
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