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Learn Machine Learning with Python at SLA Institute, the leading institute for the Machine Learning with Python Syllabus. Our syllabus covers essential topics to build a strong foundation in data science, AI, and machine learning techniques. Explore key concepts such as data preprocessing, supervised and unsupervised learning, neural networks, and deep learning frameworks. Gain hands-on experience through real-world projects and practical coding exercises. SLA Institute provides expert training and career support to help you excel in machine learning roles. Download our Machine Learning with Python Syllabus PDF for a detailed course structure and topics. Join our Machine Learning with Python Course with 100% Placement Support and take the first step toward a successful career in AI and data science. Start your journey with SLA Institute today!
Course Syllabus
Download SyllabusModule 1: Introduction to Machine Learning
- Overview of Machine Learning and its Importance
- Differences Between AI, ML, and Deep Learning
- Applications of Machine Learning in Various Industries
- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Setting Up the Python Environment for ML (Anaconda, Jupyter Notebook)
Module 2: Python for Machine Learning
- Python Basics: Data Types, Functions, and Control Structures
- Introduction to Data Science Libraries: NumPy, Pandas, Matplotlib, and Seaborn
- Data Preprocessing: Handling Missing Data, Feature Scaling, and Encoding
- Feature Engineering Techniques to Improve Model Performance
- Exploratory Data Analysis (EDA) for Better Data Understanding
Module 3: Supervised Learning Algorithms
- Understanding Regression Models (Linear and Multiple Regression)
- Classification Techniques: Logistic Regression, Decision Trees, Random Forest
- Introduction to Support Vector Machines (SVM) and Naïve Bayes
- Model Evaluation: Confusion Matrix, Accuracy, Precision, Recall, and F1-Score
- Implementing Supervised Learning Algorithms with Python
Module 4: Unsupervised Learning Techniques
- Understanding Clustering (K-Means, Hierarchical, DBSCAN)
- Dimensionality Reduction (PCA, t-SNE, LDA)
- Anomaly Detection Techniques
- Association Rule Learning (Apriori, Eclat) for Market Basket Analysis
- Implementing Unsupervised Learning Algorithms in Python
Module 5: Deep Learning with Neural Networks
- Introduction to Artificial Neural Networks (ANN)
- Activation Functions and Backpropagation
- Convolutional Neural Networks (CNN) for Image Recognition
- Recurrent Neural Networks (RNN) and LSTMs for Time-Series Data
- Implementing Deep Learning with TensorFlow and Keras
Module 6: Natural Language Processing (NLP)
- Introduction to NLP and Text Processing
- Tokenization, Lemmatization, and Stopword Removal
- Sentiment Analysis and Text Classification
- Named Entity Recognition (NER) and Topic Modeling
- Implementing NLP Models Using NLTK and SpaCy
Module 7: Model Optimization and Deployment
- Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV
- Avoiding Overfitting and Underfitting in Machine Learning Models
- Deploying Machine Learning Models Using Flask and Streamlit
- Cloud Deployment: Deploying ML Models on AWS, GCP, and Azure
- Creating APIs for Machine Learning Models
Module 8: Real-World Projects and Case Studies
- Predictive Analytics for Business Decision Making
- House Price Prediction using Regression Models
- Sentiment Analysis of Customer Reviews using NLP
- Image Classification using CNN for Medical Diagnosis
- Fraud Detection in Banking and Finance
In conclusion, our Syllabus of Machine Learning with Python course provides learners with essential skills to master machine learning techniques using Python. The course covers key topics such as data preprocessing, supervised and unsupervised learning, deep learning, and model deployment, offering hands-on experience through real-world projects. With a structured curriculum, students will gain proficiency in building predictive models, working with AI frameworks, and analyzing data effectively. This Machine Learning with Python Syllabus is designed to prepare learners for careers in data science, artificial intelligence, and analytics. Start your journey today and develop expertise in machine learning with Python!
