Machine Learning with Python Training
Introduction to Machine Learning with Python
Gain an overview of machine learning and its implementation using Python. Learn about the fundamental concepts of machine learning and how Python’s libraries and tools facilitate the process.
Python for Data Preparation and Preprocessing
Learn how to use Python for data preparation and preprocessing. Understand techniques for cleaning data, handling missing values, and feature engineering using libraries like Pandas and NumPy.
Supervised Learning with Python
Explore supervised learning techniques using Python. Learn about algorithms such as linear regression, logistic regression, decision trees, and support vector machines using libraries like scikit-learn.
Unsupervised Learning with Python
Discover unsupervised learning methods with Python. Learn about clustering algorithms like k-means and hierarchical clustering, and dimensionality reduction techniques such as PCA using Python libraries.
Model Evaluation and Selection with Python
Understand how to evaluate and select machine learning models using Python. Learn about metrics for model performance, cross-validation, and hyperparameter tuning with tools available in scikit-learn.
Deep Learning with Python
Explore deep learning with Python using frameworks like TensorFlow and Keras. Learn about neural network architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
Machine Learning Tools and Libraries in Python
Get familiar with popular machine learning tools and libraries in Python. Learn how to use scikit-learn, TensorFlow, Keras, and other libraries to build and deploy machine learning models effectively.
Hands-On Labs and Practical Projects
Engage in hands-on labs and practical projects to apply your knowledge of machine learning with Python. Work on real-world datasets and problems to develop practical skills in building, evaluating, and deploying models.
Machine Learning with Python syllabus
1. Introduction to Python Programming
- Python Core Packages
- Introduction to Jupyter Notebook
- Introduction to Numpy and Pandas
- Data Filtering and Selecting
- Find Duplicates and Treating Missing Values
- Concatenate and Transform Data
2. Introduction to Various Packages and Related Functions
- Numpy, Pandas, and Matplotlib
- Pandas Module
- Series
- Data Frames
- Numpy Module
- Numpy Arrays
- Numpy Operations
- Matplotlib Module
- Plotting Information
- Bar Charts and Histogram
- Box and Whisker Plots
- Heatmap
- Scatter Plots
3. Data Wrangling Using Python
- NumPy – Arrays
- Data Operations (Selection, Append, Concat, Joins)
- Univariate Analysis
- Multivariate Analysis
- Handling Missing Values
- Handling Outliers
4. Introduction to Machine Learning with Python
- What is Machine Learning?
- Introduction to Machine Learning
- Types of Machine Learning
- Basic Probability Required for Machine Learning
- Linear Algebra Required for Machine Learning
5. Supervised Learning - Regression
- Simple Linear Regression
- Multiple Linear Regression
- Assumptions of Linear Regression
- Polynomial Regression
- R2 and RMSE
6. Supervised Learning - Classification
- Logistic Regression
- Decision Trees
- Random Forests
- SVM
- Naïve Bayes
- Confusion Matrix
7. Dimensionality Reduction
- PCA (Principal Component Analysis)
- Factor Analysis
- LDA (Linear Discriminant Analysis)
8. Unsupervised Learning - Clustering
- Types of Clustering
- K-means Clustering
- Agglomerative Clustering
9. Additional Performance Evaluation and Model Selection
- AUC / ROC
- Silhouette Coefficient
- Cross Validation
- Bagging
- Boosting
- Bias vs. Variance
10. Recommendation Engines
- Need for Recommendation Engines
- Types of Recommendation Engines
- Content-Based
- Collaborative Filtering
11. Association Rules Mining
- What are Association Rules?
- Association Rule Parameters
- Apriori Algorithm
- Market Basket Analysis
12. Time Series Analysis
- What is Time Series Analysis?
- Importance of Time Series Analysis
- Understanding Time Series Data
- ARIMA Analysis
13. Reinforcement Learning
- Understanding Reinforcement Learning
- Algorithms Associated with Reinforcement Learning
- Q-Learning Model
- Introduction to Artificial Intelligence
14. Artificial Neural Network and Introduction to Deep Learning
- History of Neural Networks
- Perceptron
- Forward Propagation
- Introduction to Deep Learning
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details