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Machine Learning With Python
Upgrade your coding skills with Placement Point Solutions’ Best-Selling Machine Learning Course With Python
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Why Study Machine Learning With Python
Python is easy to learn, has powerful ML libraries (like Scikit-learn,
TensorFlow, and PyTorch), and is widely used in AI and data science.
Its versatility and strong community support make it the best choice
for ML development!
Benefits of Learning Machine Learning With Python
✨ Easy to Learn – Simple syntax and beginner-friendly.
✨ Powerful Libraries – Includes Scikit-learn, TensorFlow, PyTorch, etc.
✨ Versatile & Scalable – Used in AI, automation, and big data.
✨ Strong Community Support – Large global community for help and updates.
✨ Industry Demand – High-paying job opportunities in tech, finance, and healthcare.
✨ Great for Prototyping – Fast development and testing of ML models.
✨ Seamless Integration – Works well with other tools and languages
Future Scope of Machine Learning With Python
✨ High Demand – Essential in AI, automation, and data science.
✨ Advancements in AI – Key for deep learning, NLP, and robotics.
✨ Big Data & Analytics – Growing role in real-time data processing.
✨ Industry-Wide Adoption – Used in healthcare, finance, and tech.
✨ Automation & IoT – Driving smart systems and AI-powered applications.
✨ Research & Innovation – Expanding in academia and scientific breakthroughs.
Kickstart Your Career with Machine Learning With Python
With its endless applications and growing demand, Machine Learning with Python is the key to a successful tech career. Enroll in a course today and step into a world of innovation and opportunity!
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Course Duration : 40 hours
Intensive, focused training designed to quickly upskill you in a short timeframe.
100% Job-Oriented Training
Curriculum tailored to industry requirements, ensuring you gain the skills employers are looking for.
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Flexible Demo Class Available
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Completed 400+ Batches
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Certification Guidance
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Live Practical Sessions (if needed)
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Syllabus of Machine learning Python
- Overview of Machine Learning:
What is Machine Learning?
Applications and impact across industries
Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning - Python for Machine Learning:
Setting up Python and Anaconda
Introduction to Jupyter Notebooks
Python essentials for ML: Data types, functions, libraries, and packages
- Data Handling with Python:
Using Pandas for data manipulation
Handling missing data, duplicates, and inconsistencies - Exploratory Data Analysis (EDA):
Summary statistics, data visualization, and insights
Visualization tools: Matplotlib, Seaborn, and Plotly - Feature Engineering:
Data transformation and scaling (Normalization, Standardization)
Encoding categorical variables
Feature selection and extraction techniques
- Regression Techniques:
Simple and Multiple Linear Regression
Polynomial Regression
Performance metrics (MSE, RMSE, MAE, R²) - Classification Techniques:
Logistic Regression
k-Nearest Neighbors (k-NN)
Decision Trees
Performance metrics (Accuracy, Precision, Recall, F1-Score, ROC Curve) - Hands-on Labs:
Building and evaluating regression and classification models using Scikit-Learn PPS
- Ensemble Methods:
Bagging, Boosting (AdaBoost, Gradient Boosting)
Random Forests - Support Vector Machines (SVM):
Concept and working of SVM
Kernel trick and applications - Model Optimization:
Cross-Validation
Hyperparameter Tuning (Grid Search, Random Search) - Practical Applications:
Real-world case studies using ensemble methods and SVMs
- Clustering:
K-Means Clustering
Hierarchical Clustering
DBSCAN - Dimensionality Reduction:
Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE) - Association Analysis:
Apriori Algorithm for Market Basket Analysis - Case Studies:
Applying clustering and dimensionality reduction to real datasets
- Fundamentals of Neural Networks:
Perceptron, activation functions, and architecture
Forward and backpropagation - Deep Learning with TensorFlow and Keras:
Building a simple neural network
Convolutional Neural Networks (CNN) basics
Recurrent Neural Networks (RNN) for sequence data - Practical Implementation:
Hands-on projects with image recognition and text classification PPS
- Evaluation Strategies:
Confusion Matrix and ROC curves
Bias-Variance Trade-off - Model Validation:
Cross-validation techniques
Dealing with overfitting and underfitting - Deployment Strategies:
Saving and loading models (Pickle, Joblib)
Introduction to deploying models using Flask/Django or cloud services - Monitoring & Maintenance:
Model performance tracking in production
- Natural Language Processing (NLP):
Text preprocessing and feature extraction (TF-IDF, word embeddings)
Sentiment analysis and topic modeling - Reinforcement Learning Basics:
Concepts and key algorithms - Time Series Forecasting:
Techniques and models (ARIMA, LSTM) - Ethics in Machine Learning:
Bias, fairness, and responsible AI
- Project Lifecycle:
Problem identification and data collection
Data cleaning, EDA, and feature engineering
Model selection, building, and evaluation
Model deployment and reporting - Presentation & Documentation:
Creating reproducible work with notebooks and documentation
Final project presentation and peer review PPS
- Emerging Technologies:
AutoML, Explainable AI, and MLOps - Research & Innovation:
Staying updated with latest trends and publications - Career Guidance:
Preparing for roles in Machine Learning and Data Science
Building a portfolio and resume for industry jobs
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