Machine Learning with Python
Artificial Intelligence (AI) is everywhere. One of the popular applications of AI is Machine Learning, in which computers, software, and devices perform via cognition. Machine Learning is where a computer/machine learns from past experiences (input data) and makes future predictions.
Modules
7
Duration
50 Hours
Register By
April 2nd 2023
Modes
, Online,
Course Fee :
Live Online Classes
5000 /- INR
Industry Oriented Curriculum
An exhaustive curriculum designed by our industry experts which will help you to get placed in your dream IT company
5
Case Studies
7
Assignments
7
Quizes
3
Tools
1
Capstone Projects
What you will learn from this course :
The Learning outcomes of a Machine Learning with Python training program are as follows:
Understanding of machine learning concepts and techniques such as supervised and unsupervised learning, decision trees, random forests, support vector machines, and neural networks.
Ability to implement machine learning algorithms using Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
Knowledge of feature selection and engineering techniques, and the ability to perform data preprocessing and cleaning.
Understanding of model evaluation methods and the ability to use metrics such as accuracy, precision, recall, and F1-score to evaluate model performance.
Ability to use Python for data analysis, visualization, and building machine learning models.
Knowledge of best practices and techniques for building and deploying machine learning models, including cross-validation, regularization, and model ensembles.
Machine Learning with Python with Curriculum
Prerequisites for this course
A list of topics that are good to have before you hit the ground running with this course
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Introduction (Supervised vs Unsupervised Learning )
02 Hrs
Machine Learning vs Statistical Modelling
Supervised vs Unsupervised Learning
Supervised Learning Classification
Unsupervised Learning
Exploratory Data Analysis
06 Hrs
Use of built-in functions to explore data
Finding and handling missing values
Visualizing to explore more about data
Replacing null values
Encoding techniques
Supervised Learning I
12 Hrs
Regression Algorithms
Simple Linear Regression
Multiple Linear Regression
Polynomial Linear Regression
Model Evaluation
Model Evaluation: Overfitting & Underfitting
Understanding Different Evaluation Models
Supervised Learning II
16 Hrs
K-Nearest Neighbors
Evaluation metrics for Classification algorithms
Accuracy score
Confusion matrix
Classification report
Logistic Regression
Decision Trees
Random Forests
Reliability of Random Forests
Advantages & Disadvantages of Decision Trees
Bagging and Boosting Techniques
Unsupervised Learning
10 Hrs
K-Means Clustering plus Advantages & Disadvantages
Hierarchical Clustering plus Advantages & Disadvantages
Measuring the Distances Between Clusters - Single Linkage Clustering
Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
Density-Based Clustering
Dimensionality Reduction & Collaborative Filtering
04 Hrs
Dimensionality Reduction: Feature Extraction & Selection
Association Rule
04 Hrs
Support, Confidence, lift, Association rules, Apriory algorithm
System Requirments
Common system requirments that you will need to have before moving ahead with this course
- Atleast 4GB RAM
- Stable Internet Connection
- OS : Linux / Windows / Mac
On Completion
On Successfull Completion of
Traning on
Machine Learning with Python
Trainee will get 2 Certificates:1. “Certificate of Achievement”
2. “Certificate of Internship”
Note : Internship Certificate will be awarded only after Successfull Completion of Capstone Projects
