Anybody can take up this training course regardless of their prior skills.
Machine Learning is one of the most important domains that is being deployed in today’s hypercompetitive world. Be it for self-driving cars or for search engines like Google, Machine Learning is being extensively used for making our lives simple. Intellipaat is offering a comprehensive Machine Learning with Python programming training that can be taken up by professionals to excel in their careers and grab the best jobs in the industry.
Introduction to Python Language, features, the advantages of Python over other programming languages, Python installation, Windows, Mac & Linux distribution for Anaconda Python, deploying Python IDE, basic Python commands, data types, variables, keywords and more.
Built-in data types in Python, tabs and spaces indentation, code comment Pound # character, variables and names, Python built-in data types, Numeric, int, float, complex, list tuple, set dict, containers, text sequence, exceptions, instances, classes, modules, Str(String), Ellipsis Object, Null Object, Ellipsis, Debug, basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise, loop and control statements, while, for, if, break, else, continue.
Introduction to NumPy arrays and matrices, indexing of Numpy array, datatypes, broadcasting of array math, standard deviation, conditional probability, correlation, and covariance.
Hands-on Exercise – How to import NumPy module, creating an array using ND-array, calculating standard deviation on an array of numbers, calculating the correlation between two variables.
Introduction to SciPy and its functions, building on top of NumPy, cluster, linalg, signal, optimize, integrate, subpackages, SciPy with Bayes Theorem.
Hands-on Exercise – Importing of SciPy, applying the Bayes theorem on the given dataset.
Introduction to Python dataframes, importing data from JSON, CSV, Excel, SQL database, NumPy array to dataframe, various data operations like selecting, filtering, sorting, viewing, joining, combining, how to handle missing values, time series analysis, linear regression.
Hands-on Exercise – working on importing data from JSON files, selecting record by a group, applying filter on top, viewing records, analyzing with linear regression, and creation of time series.
Need of Machine Learning, Introduction to Machine Learning, Types of Machine Learning – Supervised, Unsupervised and Re-inforcement Learning. Why Machine Learning with Python. Applications of Machine Learning
Introduction to supervised learning, Types of Supervised Learning – Regression & Classification, Introduction to Regression, Simple Linear Regression, Multiple Linear Regression, Assumptions in Linear Regression, Math behind Linear Regression
Hands-on Exercise – Implementing linear regression from scratch with python. Using Python library Scikit-Learn to perform simple linear regression and multiple linear regression. Implement train-test split and predict the values on the test set.
Introduction to Classification, Linear regression vs Logistic Regression, Math behind Logistic Regression, detailed formulas, logit function and odds, confusion matrix and Accuracy, true positive rate, false positive rate, Threshold evaluation with ROCR.
Hands-on 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 the accuracy, true positive rate, and false positive rate.
Introduction to tree-based classification, Understanding Decision Tree, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of information gain for right split of node, Impurity Function – Gini index, understand the concept of Gini Index for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, Introduction to ensemble techniques, Understanding Bagging, Introduction to Random Forest, Finding the right number of trees in Random Forest.
Hands-on Exercise – Implementing decision tree from scratch in Python. Using Python library Scikit-Learn to build a decision tree and random forest. Visualizing the tree and changing the hyperparameters in the random forest.
Introduction to probabilistic classifiers, Understanding Naïve Bayes, Math behind Bayes theorem, Understanding Support Vector Machine, Kernel Functions in Support Vector Machine, Math behind svm.
Hands-on Exercise – Using Python library Scikit-Learn to build Naïve Bayes Classifier and Support Vector Classifier.
Types of Unsupervised Learning- Clustering and Dimensionality Reduction. Types of clustering, introduction to k-means clustering, the math behind k-means, Dimensionality reduction with PCA.
Hands-on Exercise – Using Python library Scikit-Learn to implement K-means clustering. Implementing PCA on top of a dataset.
Introduction to deep learning with neural networks, Biological neural network vs Artificial neural network, Understanding perceptron learning algorithm, introduction to deep learning frameworks, TensorFlow-Constants, Variables and Place-holders
Project 1: Customer Churn Classification
Topics: This is a real-world project that gives you hands-on experience in working with most of the machine learning algorithms. The main components of the project include the following:
Project 2: Recommendation for Movie, Summary
Topics: This is a real-world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details, and others. The main components of the project include the following:
Intellipaat offers comprehensive training in Machine Learning with Python through hands-on real-world projects and case studies. As part of the training, you will learn about Machine Learning with Python algorithms, classification techniques, linear and logistic regression, supervised and unsupervised learning and more. Upon the successful completion of the training, you will be awarded with Intellipaat Machine Learning Certification.
As part of this online Machine Learning course, you will be working on real-time Machine Learning projects and step-by-step assignments that have high relevance in the corporate world, and the curriculum of this course designed by industry experts. Upon completion of the online course, you can apply for some of the best jobs in top MNCs around the world at top salaries. Intellipaat offers lifetime access to videos, course materials, 24/7 support, and course material upgrading to the latest version at no extra fee. Hence, it is clearly a one-time investment.
This course is designed for clearing Intellipaat Machine Learning with Python Certification Exam. The entire Machine Learning with Python training course content is designed by industry professionals to get the best jobs in top MNCs. As part of this training, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly.
At the end of this Machine Learning with Python training program, there will be quizzes that perfectly reflect the type of questions asked in the certification exam and help you score better.
Intellipaat Course Completion Certification will be awarded upon the completion of the project work (after expert review) and upon scoring of at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high-impact projects in major corporations around the world.
A renowned Data Scientist who has worked with Google and is currently working at ASCAP, Samanth Reddy has a proven ability to develop Data Science strategies that have a high impact on the revenues of various organizations. He comes with strong Data Science expertise and has created decisive Data Science strategies for Fortune 500 corporations.