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'Certified Machine Learning Practitioner' (CMLP) course is a focused 32-hours instructor-led training and certification program that equips participants to understand 'What' & 'How' aspects of Machine Learning using popular tools such as Python & Advanced Excel.
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Machine Learning Course with Python enables professionals to take actionable better Data-Driven decisions to increase market share. By understanding complex algorithms and techniques, participants can effectively analyze large datasets and extract valuable insights. This course also covers practical applications of machine learning in various industries, providing hands-on experience for real-world scenarios.
It is a versatile 10-in-1 program that includes various aspects of competency development and career development.
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Free access to the E-learning Portal and future updates. Get access to PPTs, Projects, Quizzes, self-paced Video-based learning, a question bank, a library, practice tests, final assessment, a forum, and doubt sessions.
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Understanding the need for Analytics in Specific Domain; CRISP Modeling
Properties & Types of Data, Measurement Scale, Basic Statistics on Data
Need for Python, Features of Python, Download, Setup, Installation; Python & Python Studio; Configuration. e.g. Learning to setup Python and share Code
Creating and Understanding Basic Data Structures in Python - Vector, List, Matrix, Array, Data Frame & Factors which help in creating data in Python programming
Understanding how data can be summarized in different ways to do Descriptive Analysis which describes features of data
Understand what is Modeling and how it can be used in Various Domain
P Value, Z Value, Hypothesis, Null Hypothesis, Alternative Hypothesis, F Test, ANOVA Introduction)
Start of Machine Learning, Develop a Prediction Model for predicting a financial values based on one or more than 1 Independent Variable; Understand the assumptions and measures of goodness of Model, Understand its prediction ability
Creating Graph in Python and understanding which graph to be used when
Understanding how missing values & outliers are handled in data summarization & modeling
Predicting Binary Outcome (Buy or not, Churn or not, Loan Default or not) based on Independent Variables e.g. Predicting Cases for Fraud, Default on Payment etc
Grouping Customers based on characteristics so that they can be target for sale increase
When to use CART & CHAID to create a decision tree based on categorical variables.
Random Forest, XGBoost: Problems of Decision Tree covered in Random Forest, How group thinking impacts the decisions (from business point of view)
Understand how Association Rules can be created using Market Basket Analysis. Finding Interesting association between items purchased by Customers and building strategy to sell more
Configure Twitter Account & Application; Setup for downloading tweets and analyse them for positive and negative sentiments related to Financial News/ Articles
Know the complete offerings of our Machine Learning with Python ( CMLP) Course
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Logistic Regression
Analysis
Ensemble
Clustering
Linear Regression
Analytical Modeling
Statistics
Data Structure
Data Manipulation & Summarization
Know more about 50+ tools covered in this Machine Learning with Python ( CMLP) Course in San Diego
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