Machine Learning

Machine Learning course is offered by Broaderai. Learn A-Z everything about ML From the basic to advanced.

What You Will Learn:

• Basic fundamentals of Machine learning.
• Data exploration, data preprocessing, handling missing values.
• Feature engineering and exploratory data analysis.
• Data visualization techniques.
• Descriptive and inferential statistics .
• Cross – validation techniques.
• Model selection, model training, model evaluation and model prediction.
• Supervised learning, Unsupervised learning and Reinforcement learning.
• Regression, Classification, Clustering, Association rules.
• Linear regression, Logistic regression, Support vector machine, Naïve bias algorithm, Decision tree, Random forest, K-nearest neighbors and others.
• Ensemble learning – bagging and boosting.
• K-means, DBSCAN, Hierarchical clustering.
• Content based filtering and Collaborative filtering.
• Recommendation system and its working process.
• Adaboost, XGboost, Catboost, Gradient boosting, etc.
• Deep learning and Neural networks.
• Perceptron, Artificial neural networks, Feed forward neural network.
• Back-propagation algorithm.
• Weights, bias and tradeoff.
• Overfitting and underfitting.
• Activation functions, optimizers and loss / cost functions.
• Epochs, step per epochs, batch size, val epochs, learning rate, etc..
• Project management, development and deployment.
• Web scraping techniques.
• API development using DeepLearning framework.
• Working with sklearn, TensorFlow, Pandas, Numpy, Matplotlib, Seaborn, Plotly.
• Hands on experience in real world projects.
• Machine learning interview questions.
• Machine learning mock interview preparation.
• Helping resume creation.

Syllabus:

• 1. Introduction of course
• 2. Fundamental of Machine learning
• 3. Data exploration & Feature engineering techniques
• 4. Data visualization
• 5. Supervised learning
• 6. Ensemble learning
• 7. Unsupervised learning
• 8. Reinforcement learning
• 9. Deep learning
• 10. Additional knowledge