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Machine Learning with Python Training

Self-Paced, Live Online & Classroom Enterprise Training

Machine Learning with python course delves deeper into the Machine Learning fundamental concepts further explaining Machine Learning algorithms and their implementation. Equip your team with the necessary skills to develop AI based intelligent applications for your organization with hands-on training in Machine Learning with Python.

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Prof. Dr. James G. Shanahan
Program Architect

Prof. Dr. James G. Shanahan

Dr. James has spent the past 25 years developing and researching cutting-edge artificial intelligent systems. He has (co)founded several companies, advises high-tech startups and held appointments at AT&T (Executive Director of Research), Turn Inc., Xerox Research, Mitsubishi Research, and at Clairvoyance Corp. He teaches at UC Berkeley and has published six books, more than 50 research publications, and has over 20 patents in the areas of machine learning and information processing.

What is Python Machine Learning Training about?

SpringPeople’s 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.

What are the objectives of Python Machine Learning Training ?

At the end of this Machine Learning with Python Certification course, you will be able to:

 

  • Appreciate the breadth & depth of ML applications and use cases in real-world scenarios.

  • Import and wrangle data using Python libraries and divide them into training and test datasets

  • Data preprocessing techniques, Univariate and Multivariate analysis, Missing values and outlier treatment etc

  • Implement linear and polynomial regression, understand Ridge and lasso Regression,

  • Implement various type of classification methods including SVM, Naive bayes, decision tree, and random forest

  • Interpret Unsupervised learning and learn to use clustering algorithms

  • Tuning of ML solutions, Bias-variance tradeoff, Minibatch, and Shuffling, Overfitting avoidance

  • Basics of Neural Networks, Perceptron, MLP

  • Build real-world solutions using MLP

Who is Python Machine Learning Training for?

  • Data Analyst who want to gain expertise in Predictive Analytics
  • Developers 
  • Data Architects 
  • Tech Leads handling a team of Analysts

What are the prerequisites for Python Machine Learning Training?

  • Basic Python programming knowledge and fundamentals of data analysis required 
  • Basic knowledge of statistics and mathematics is good to have

Available Training Modes

Live Online Training

24 Hours

Classroom Training

3 Days

Course Outline Expand All

Expand All

  • 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

Reviews

Resources

Webinar
AI & ML - State of the Art and Future

AI & ML - State of the Art and Future

By Vishal Chahal, Chief Architect for Cognitive Solutions, IBM