Machine Learning with Python

Who can take up Machine Learning with Python Training?

Individuals who can take up Machine Learning with Python online training include, but are not limited to:

• IT Professionals
• Technical Leads
• Programmers
• Software Developers
• Machine Learning Engineers
• Python Professionals
• Business Analysts
• Information Architects
• Analytics Managers
• Professionals looking to gain a thorough understanding of the Machine Learning with Python
  

  Learning Objectives of Machine Learning with Python Training

  This Machine Learning with Python course consists of eight modules with interlacing practical labs. The labs offer participants the opportunity to practically demonstrate the information shared over the training. The modules explored are;

  • Statistical Learning
  • Python for Machine Learning
  • Introduction to Machine Learning
  • Optimisation
  • Supervised Learning
  • Unsupervised Learning
  • Ensemble Techniques
  • Recommendation Systems

  Upon completion of this Machine Learning with Python online course, professionals are equipped with a number of skills, including;

  • Building python programs including distribution, user-defined functions, importing datasets and more
  • Manipulating and analyzing data using the Pandas library
  • Visualizing data with Python libraries: Matplotlib, Seaborn and ggplot
  • Building data distribution models: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Performing analysis of variance (ANOVA)
  • Building linear regression models, evaluating model parameters and measuring performance metrics
  • Using Dimensionality Reduction
  • Building logical regression models, evaluating model parameters and measuring performance metrics
  • Performing K-means clustering and hierarchical clustering
  • Building KNN algorithm models to find the optimum value of K
  • Building decision tree models for both regression and classification problems
  • Using ensemble techniques like averaging, weighted averaging and max voting
  • Using techniques of bootstrap sampling, bagging and boosting
  • Building random forest models
  • Finding the optimum number of components/factors using scree plot and one-eigenvalue criterion
  • Performing PCA/Factor Analysis
  • Building appropriate algorithms with key metrics like support, confidence and lift
  • Building recommendation engines using UBCF and IBCF