Course Overview
Machine learning is an application of AI that enables systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves.
Similar to how the human brain gains knowledge and understanding, machine learning relies on input, such as training data or knowledge graphs, to understand entities, domains and the connections between them. With entities defined, deep learning can begin.
The machine learning process begins with observations or data, such as examples, direct experience or instruction. It looks for patterns in data so it can later make inferences based on the examples provided. The primary aim of ML is to allow computers to learn autonomously without human intervention or assistance and adjust actions accordingly.
Details of the course is mentioned below :
Course Modules
You will learn :
- Perform basic workflow for machine learning with text
- - Extract features from unstructured data using CountVectorization
- - Build a MultinomialNB model for text classification
- - Gather insights
- - Model evaluation using
- accuracy_score
- confusion_matrix
- roc_auc_score
- Compare Multinomial NB with Logistic Regression
- Create test datasets from Text files using Numpy and Pandas libraries.
- Unicode Basics
- Error Handling
- What is Natural Language Processing(NLP)?
- Tuning CountVectorization for better model performance
- - n-grams
- - stop words
- - corpus specific stop words
- - minimum document frequency
- How to use Term Frequency-Inverse Document Frequency (TF-IDF) using TfidVectorization
- Text Summary
- Sentiment analysis using TextBlob
- Extract text from messy unstructured data using regular expression
- Python functions and library for regular expression
- Rules of regular expression
- Search with re.search
- Metacharacters
- Greedy and lazy quantifiers
- Match groups
- Character classes
- Alternative character class
- String substitution with re.sub
- Anchor
- Option Flags
- Efficiently searching for multiple matches with re.findall
- Improving performance with re.compile
- Writing readable regular expressions with re.VERBOSE
- Feature Engineering
- - Data exploration and visualization
- - Feature engineering using Pandas
- - Custom tokenization using regular expression
- - Multi-class classification
- Model evaluation
- - train-test split
- - cross-val score
- - DummyClassifier
- Search for optimal tuning parameters using GridSearchCV
- Chaining steps into a Pipeline
- Making predictions for out-of-sample data
- Using a Pipeline for proper cross-validation
- Tuning a Pipeline with GridSearchCV
- Efficiently searching for tuning parameters using RandomizedSearchCV
- Stacking sparse and dense feature matrices using SciPy
- Combining the results of multiple feature extraction processes using FeatureUnion
- Building multi-level pipelines and feature unions
- Building custom transformers using FunctionTransformer
- Improving classifier performance through ensembling
- Unsupervised document clustering using cosine similarity
- Basic strategies for model stacking
-
Capstone Project
FAQ
Any graduate, fresher, experienced and looking to start or change their career into data analytics can attend. No prior experience is mandatory.
Absolutely. We start the training from scratch. After completing the course you will not feel that you have never programmed.
Yes
Our trainers are from the industry. Every class is followed by real time assignments which will make you feel confident when you get into a job.
Fully online. You can attend from anywhere. You just need a laptop and a good internet connection
Yes 100%. It is live and fully interactive. We dont believe in running through power point presentations.
Please contact on the given number.
Contact Us
+91 70198 30464 / +91 79911 97543
Enroll / Enquire
Address : Saroj Iris, No. 106 (Wing A), Gunjur, Varthur Hobli, Bangalore - 560087
Phone : +91 70198 30464 / +91 79911 97543
Email :
info@dakshahanalyticsacademy.com, dakshahanalyticsacademy@gmail.com