Machine Learning with Python
Course Overview
Machine Learning with Python course focuses on giving your teams a serious head-start and practical approach on building deployable machine learning models by offering an in-depth understanding of the three major types of machine learning algorithms, comprising of supervised, unsupervised, and reinforcement learning using the most widely used programming language. Learn the various methods for implementing these algorithms with associated business use cases.
Help your team gain comprehensive knowledge of different Classification models and their evaluation techniques. Machine Learning training also introduces you to advanced topics of Machine Learning such as Natural Language Processing (NLP) and Artificial Neural Networks
Work on various data pre-processing techniques, evaluating data sufficiency, different prediction and classification techniques, implementation examples, evaluation methodologies and a comprehensive view of how to go about defining and implementing your Machine Learning solution.
Using our advanced Cloud labs, get seamless hands-on experience working with Python’s functions and libraries for Machine Learning projects. With easy to follow step-by-step instructions, you will learn to implement all Machine Learning algorithms taught in this course using the popular Python programming language.
At the end of the training, participants will be able to:
- 1. Appreciate the breadth & depth of ML applications and use cases in real-world scenarios.
- 2. Import and wrangle data using Python libraries and divide them into training and test datasets
- 3. Data preprocessing techniques, Univariate and Multivariate analysis, Missing values and outlier treatment etc
- 4. Implement linear and polynomial regression, understand Ridge and lasso Regression,
- 5. Implement various type of classification methods including SVM, Naive bayes, decision tree, and random forest
- 6. Interpret Unsupervised learning and learn to use clustering algorithms
- 7. Tuning of ML solutions, Bias-variance tradeoff, Minibatch, and Shuffling, Overfitting avoidance
- 8. Basics of Neural Networks, Perceptron, MLP
- 9. Build real-world solutions using MLP
Pre-requisite
- Basic Python programming knowledge and fundamentals of data analysis required
- Basic knowledge of statistics and mathematics is good to have
Duration
3 days
Course Outline
- What is ML?
- Applications of ML
- Why ML?
- Uses of ML
- Machine learning methods
- Machine learning algorithms(Regression, Classification, Clustering, Association)
- A brief introduction python libraries
- Types of ML algorithms
- Labelled Dataset
- Training and Testing Data
- Importing the Libraries
- Importing the Dataset
- Demo: Creating a machine model
- What is data?
- What is information?
- Analyzing data to fetch the information
- Entropy, Information gain
- Data exploration and preparation
- Univariate, bivariate, and multivariate analysis
- Correlation
- Chi-Square, Z-test, T-test, ANOVA
- Categorical Data
- Feature Scaling
- Dimensionality Reduction
- outliers
- What is regression?
- Applications of regression
- Types of regression
- Fitting the regression line
- Simple linear regression
- Simple linear regression in python
- Polynomial regression
- Polynomial regression in python
- Gradiant Descent
- Cost function
- Regularization
- Demo: Perform regression on a real world dataset
- Ridge and lasso Regression
- How is classification used?
- Applications of classification
- Logistic Regression, Sigmoid function
- Decision tree
- K-Nearest Neighbors (K-NN)
- SVM
- Naive Bayes
- Understand limitations of linear classifer and evaluate abilities of non-linear classifiers using a data set
- Confusion Matrix
- Precision, Recall
- F1-score
- RoC, AuC
- n-fold cross validation
- Measuring classifier performance
- Overfitting
- Ensemble Learning
- Bagging and Boosting
- Application of Unsupervised learning, examples, and applications
- Clustering
- Hierarchical Clustering in Python, Agglomerative and Divisive techniques
- Measuring the distanvce between two clusters
- k-means algorithm
- Limitations of K-means clustering
- SSE and Distortion measurements
- Demo: Agglomerative Hierarchical clustering
- What is dimensionality reduction?
- Applications of dimensionality reduction
- Feature selection
- Feature extraction
- Dimensionality reduction via Principal component analysis
- Eigenvalue and Eigenvectors
- Hands on PCA on MNSIT data
- What is reinforcement learning
- Applications of reinforcement learning
- An Example use case
- Components of RL
- Approachs to RL
- RL algorithms
- Deep reinforcement learning
- What is NLP?
- Why NLP
- Applications of NLP
- Components of NLP
- NLP techniques
- Why deep learning?
- Neural networks
- Applications of neural networks
- Biological Neuron vs Artificial Neuron
- Artificial Neural networks, layers
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