Specialisation in Machine Learning (ML)
This specialized course provides a deep dive into machine learning, equipping learners with the skills to apply ML algorithms across various industries, including finance, healthcare, and manufacturing. Topics range from supervised and unsupervised learning techniques to neural networks, deep learning, natural language processing (NLP), and computer vision. Students will also learn to deploy scalable models in real-world scenarios, with hands-on experience in tools like TensorFlow, Keras, OpenCV, and cloud platforms for model deployment.
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Specialisation in Machine Learning (ML)
A course by Navikshaa
This specialized course provides a deep dive into machine learning, equipping learners with the skills to …
- 2,433
- 98%
₹17,990
What you will learn
- Introduction to cutting-edge Machine Learning concepts and industry applications.
- Master data preprocessing techniques for optimal model performance.
- Understand how to clean and normalize data for better predictions.
- Learn essential feature engineering and selection for effective models.
- Dive into regression and classification algorithms like Decision Trees, KNN, and SVM.
- Explore key evaluation metrics such as Accuracy, Precision, and Recall.
- Unlock the secrets of model evaluation and advanced cross-validation methods.
- Get hands-on with Python libraries like NumPy, Pandas, and Scikit-learn.
- Discover the power of ensemble learning techniques for boosting model accuracy.
- Master unsupervised learning techniques including clustering and dimensionality reduction.
- Understand Neural Networks and deep learning basics with TensorFlow and Keras.
- Gain insights into Natural Language Processing and its applications in business.
- Learn how Convolutional Neural Networks are revolutionizing image recognition.
- Explore the potential of RNNs and LSTM for time series forecasting and sequence prediction.
- Delve into Reinforcement Learning and its impact on real-world applications.
- Learn how to deploy and scale ML models using APIs, Docker, and cloud platforms.
- Uncover advanced NLP tools like BERT and GPT for text analysis and generation.
- Explore real-world applications of computer vision in industries such as manufacturing.
- Use ML for predictive analytics in sectors like retail, finance, and operations.
- Prepare for your career with industry insights, interview tips, and emerging ML trends.
The course includes :
- 45+ hours live Sessions
- 25 Quizes
- 4 Assessments
- Query Resolution
- 24/7 Professional Support
- No Resume Building Sessions
- No Mock Interviews
- Updated Recorded sessions
- MNC Simulated Project
- Training Completion Certificate
- Letter of Recommendation
- Wipro Credential Certificate
- No Placement Assistance
Course Content
- 45+ Hours Live Sessions
- 29 Quizzes
- 4 Assessments
Day 1: Introduction to Artificial Intelligence
- Overview of AI, its history, and evolution
- Types of AI: Narrow AI vs. General AI
- Applications of AI in business and technology
- Overview of AI, its history, and evolution
- Quiz
Day 2: Core Concepts of Machine Learning
- Introduction to machine learning
- ML vs. AI vs. Deep Learning
- Real-world examples of ML applications
- Quiz
Day 3: Machine Learning Basics and Tools
- Overview of common machine learning libraries: Scikit-learn, TensorFlow, Keras
- Data handling with Pandas and NumPy
- Basic data preprocessing techniques
- Quiz
Day 4: Supervised Learning – Regression Techniques
- Linear regression and multiple linear regression
- Hands-on with real-world datasets (e.g., sales prediction)
- Model evaluation: Mean Squared Error, R²
- Quiz
Day 5: Supervised Learning – Classification Algorithms
- Introduction to classification: Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees
- Hands-on with classification tasks (e.g., binary classification)
- Quiz
Assessment 1
- Assessment 1
Day 6: Unsupervised Learning – Clustering
- Introduction to unsupervised learning and clustering
- K-Means Clustering, DBSCAN, Hierarchical Clustering
- Real-world applications: Customer segmentation
- Quiz
Day 7: Model Evaluation and Hyperparameter Tuning
- Evaluating machine learning models
- Cross-validation and overfitting/underfitting
- Hyperparameter tuning using Grid Search and Random Search
- Quiz
Day 8: Support Vector Machines (SVM)
- Introduction to Support Vector Machines
- SVM for classification and regression
- Real-world applications: Text classification, image classification
- Quiz
Day 9: Ensemble Learning Techniques
- Overview of ensemble methods: Random Forest, Bagging
- Hands-on with ensemble learning for improving model accuracy
- Quiz
Day 10: Advanced Machine Learning – Dimensionality Reduction
- Principal Component Analysis (PCA)
- Feature selection and reduction
- Applications: Reducing dimensionality in large datasets (e.g., image or gene data)
- Quiz
Assessment 2
- Assessment 1
Day 11: Introduction to Deep Learning
- What is deep learning? Differences from traditional ML
- Neural networks: Architecture and learning process
- Activation functions, loss functions, and backpropagation
- Quiz
Day 12: Convolutional Neural Networks (CNN)
- Introduction to CNNs and their architecture
- Applications in image classification, object detection
- Hands-on project: Image recognition using CNN
- Quiz
Day 13: Recurrent Neural Networks (RNN)
- Introduction to RNNs and LSTMs (Long Short-Term Memory)
- Applications in time series forecasting, speech recognition
- Hands-on project: Sentiment analysis with RNN
- Quiz
Day 14: Deep Learning Model Evaluation
- Evaluating deep learning models: Overfitting, underfitting, and tuning
- Hyperparameter optimization for deep learning models
- Advanced techniques: Dropout, Batch Normalization, Early Stopping
- Quiz
Day 15: Transfer Learning and Pre-trained Models
- Understanding transfer learning and fine-tuning pre-trained models
- Practical applications using models like ResNet, Inception, and VGG16
- Hands-on: Transfer learning for image classification
- Quiz
Assessment 3
- Assessment 3
Day 16: Natural Language Processing (NLP) Fundamentals
- Introduction to NLP and text preprocessing
- Tokenization, stemming, lemmatization, stop words removal
- Hands-on with Python libraries: NLTK, spaCy
- Quiz
Day 17: Text Classification and Sentiment Analysis
- Introduction to text classification techniques
- Sentiment analysis using deep learning (e.g., LSTM, BERT)
- Hands-on with real-world datasets (social media posts, reviews)
- Quiz
Day 18: Sequence Modeling with RNN and LSTM
- Advanced sequence modeling using RNN and LSTM
- Applications: Time series prediction, stock market forecasting, NLP tasks
- Hands-on project: Stock price prediction with LSTM
- Quiz
Day 19: Reinforcement Learning Basics
- Introduction to reinforcement learning (RL)
- Key concepts: Agents, environments, rewards, policies
- Hands-on with simple RL algorithms (Q-learning)
- Quiz
Day 20: Advanced Deep Learning: GANs (Generative Adversarial Networks)
- Introduction to GANs and how they work
- Applications: Image generation, style transfer, and data augmentation
- Hands-on with GAN-based projects
- Quiz
Assessment 4
- Assessment 4
Day 21: AI Model Deployment
- Introduction to model deployment concepts
- Packaging models with Docker, deploying on cloud platforms (AWS, GCP, Azure)
- API integration for model serving
- Quiz
Day 22: AI for Business Applications
- AI in business: Automation, recommendation systems, and predictive analytics
- Hands-on: Building a recommendation engine using collaborative filtering
- Applications in e-commerce, healthcare, and finance
- Quiz
Day 23: AI in Healthcare
- AI applications in healthcare: Diagnostics, personalized treatment, medical imaging
- Case studies: AI in radiology, drug discovery, and patient monitoring
- Hands-on: Building a simple AI model for medical diagnosis
- Quiz
Day 24: AI in Finance and Risk Management
- AI for fraud detection, credit scoring, and algorithmic trading
- Case studies: AI in financial institutions
- Hands-on: Building a fraud detection model
- Quiz
Day 25: AI in Manufacturing and Automation
- AI for predictive maintenance, robotics, supply chain optimization
- Case studies: Smart factories, automation solutions
- Hands-on: Building an AI-based predictive maintenance system
- Quiz
Day 26: Introduction to AI on the Cloud
- Cloud AI services: AWS, Google Cloud AI, Azure Machine Learning
- Hands-on: Using Google Cloud AI for model deployment
- Benefits and challenges of cloud-based AI solutions
- Quiz
Day 27: AI for Edge Computing
- Introduction to edge AI and its importance for real-time processing
- Case studies: AI in IoT devices, autonomous vehicles
- Hands-on: Deploying a model on a Raspberry Pi
- Quiz
Day 28: AI Ethics and Responsible AI
- Ethical concerns in AI: Bias, fairness, and transparency
- AI governance and regulation in MNCs
- Building ethical AI systems for business applications
- Quiz
Day 29: Capstone Project: Building an Enterprise AI Solution
- Work on a real-world AI project
- Apply concepts learned in the course to solve a business problem
- Quiz