Machine Learning

This Machine Learning course is curated and developed by SMEs from top product-based companies to meet the needs of the current data-driven industry.

It covers a detailed overview of various algorithms and techniques, such as regression, classification, time series modeling, supervised and unsupervised learning, Natural Language Processing, etc.

You will also use Python programming language to write code for implementing numerous algorithms in this certification training.

Machine Learning Course Content:

• Module 01 - Introduction to Machine Learning
•  Need of Machine Learning
•  Introduction to Machine Learning
•  Types of Machine Learning, such as supervised, unsupervised, and reinforcement learning, Machine Learning with Python, and the applications of Machine Learning
• Module 02 - Supervised Learning and Linear Regression
•  Introduction to supervised learning and the types of supervised learning, such as regression and classification
•  Introduction to regression
•   Simple linear regression
•  Multiple linear regression and assumptions in linear regression
•  Math behind linear regression
• Exercise:
• Implementing linear regression from scratch with Python
•  Using Python library Scikit-Learn to perform simple linear regress and multiple linear regression
•  Implementing train–test split and predicting the values on the test set
• Module 03 - Classification and Logistic Regression
• Introduction to classification
•  Linear regression vs logistic regression
• Math behind logistic regression, detailed formulas, the logit function and odds, confusion matrix and accuracy, true positive rate, false positive rate, and threshold evaluation with
• ROCR
• Exercise:
•  Implementing logistic regression from scratch with Python
•  Using Python library Scikit-Learn to perform simple logistic regression and multiple logistic regression
•  Building a confusion matrix to find out accuracy, true positive rate, and false positive rate
• Module 04 - Decision Tree and Random Forest
• Introduction to tree-based classification
• Understanding a decision tree, impurity function, entropy, and understanding the concept of information gain for the right split of node
•  Understanding the concepts of information gain, impurity function, Gini index, overfitting,pruning, pre-pruning, post-pruning, and cost-complexity pruning
•  Introduction to ensemble techniques, bagging, and random forests and finding out the right number of trees required in a random forest
• Exercise:
•  Implementing a decision tree from scratch in Python
•  Using Python library Scikit-Learn to build a decision tree and a random forest
•  Visualizing the tree and changing the hyper-parameters in the random forest
• Module 05 - Naïve Bayes and Support Vector Machine (self-paced)
• Introduction to probabilistic classifiers
•  Understanding Naïve Bayes and math behind the Bayes theorem
•  Understanding a support vector machine (SVM)
• Kernel functions in SVM and math behind SVM
• Exercise:
• Using Python library Scikit-Learn to build a Naïve Bayes classifier and a support vector classifier
• Module 06 - Unsupervised Learning
• Types of unsupervised learning, such as clustering and dimensionality reduction, and the
• types of clustering
•  Introduction to k-means clustering
•  Math behind k-means
•  Dimensionality reduction with PCA
• Exercise:
•  Using Python library Scikit-Learn to implement k-means clustering
•  Implementing PCA (principal component analysis) on top of a dataset
• Module 07 - Natural Language Processing and Text Mining (self-paced)
•  Introduction to Natural Language Processing (NLP)
•  Introduction to text mining
•  Importance and applications of text mining
•  How NPL works with text mining
•  Writing and reading to word files
• Language Toolkit (NLTK) environment
•  Text mining: Its cleaning, pre-processing, and text classification
• Exercise:
•  Learning Natural Language Toolkit and NLTK Corpora
•  Reading and writing .txt files from/to a local drive
•  Reading and writing .docx files from/to a local drive
• Module 08 - Introduction to Deep Learning
•  Introduction to Deep Learning with neural networks
•  Biological neural networks vs artificial neural networks
•  Understanding perception learning algorithm, introduction to Deep Learning frameworks,
• and TensorFlow constants, variables, and place-holders
• Module 09 - Time Series Analysis (self-paced)
•  What is time series? Its techniques and applications
•  Time series components
•  Moving average, smoothing techniques, and exponential smoothing
•  Univariate time series models
•  Multivariate time series analysis
•  ARIMA model and time series in Python
•  Sentiment analysis in Python (Twitter sentiment analysis) and text analysis
• Exercise:
•  Analyzing time series data
• The sequence of measurements that follow a non-random order to recognize the nature of the phenomenon
•  Forecasting the future values in the series
• Machine Learning Projects