The Artificial Intelligence and Deep Learning are growing exponentially in today's world. There are multiple application of AI and Deep Learning like Self Driving Cars, Chat-bots, Image Recognition, Virtual Assistance, ALEXA, so on... With this course you will understand the complexities of Deep Learning in easy way, as well as you will have A Complete Understanding of Googles TensorFlow 2.0 Framework TensorFlow 2.0 Framework has amazing features that simplify the Model Development, Maintenance, Processes and Performance In TensorFlow 2.0 you can start the coding with Zero Installation, whether you’re an expert or a beginner, in this course you will learn an end-to-end implementation of Deep Learning Algorithms List of the Projects that you will work on, Part 1: Artificial Neural Networks (ANNs) Project 1: Multiclass image classification with ANN Project 2: Binary Data Classification with ANN Part 2: Convolutional Neural Networks (CNNs) Project 3: Object Recognition in Images with CNN Project 4: Binary Image Classification with CNN Project 5: Digit Recognition with CNN Project 6: Breast Cancer Detection with CNN Project 7: Predicting the Bank Customer Satisfaction Project 8: Credit Card Fraud Detection with CNN Part 3: Recurrent Neural Networks (RNNs) Project 9: IMDB Review Classification with RNN - LSTM Project 10: Multiclass Image Classification with RNN - LSTM Project 11: Google Stock Price Prediction with RNN and LSTM Part 4: Transfer Learning Part 5: Natural Language Processing Basics of Natural Language Processing Project 12: Movie Review Classifivation with NLTK Part 6: Data Analysis and Data Visualization Crash Course on Numpy (Data Analysis) Crash Course on Pandas (Data Analysis) Crash course on Matplotlib (Data Visualization) With this course you will learn, 1) To built the Neural Networks from the scratch 2) You will have a complete understanding of  Artificial Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks 3) You will learn to built the neural networks with LSTM and GRU 4) Hands On Transfer Learning 5) Learn Natural Language Processing by doing a text classifiation project 6) Improve your skills in Data Analysis with Numpy, Pandas and Data Visualization with Matplotlib So what are you waiting for, Enroll Now and understand Deep Learning to advance your career and increase your knowledge ! Regards, Vijay Gadhave

Do you want to be better data Scientist ? Are you looking for way to stand out in the crowd? Interested in increasing your Machine Learning, Deep Learning expertise by effectively applying the mathematical skills ? If the Answer is Yes . Then, this course is for you. Calculus for Deep learning "Mastering Calculus for Deep learning / Machine learning / Data Science / Data Analysis / AI using Python " With this course, You start by learning the definition of function and move your way up for fitting the data to the function which is the core for any Machine learning,  Deep Learning , Artificial intelligence, Data Science Application . Once you have mastered the concepts of this course, you will never be blind while applying the algorithm to your data, instead you have the intuition as how each code is working in background. Whether you are building Self driving cars, or building the recommendation engine for Netflix, or trying to fit the practice data for a function Your data, Will have some type of labelled input and , some type of labelled output. A typical goal would always be fit these data to the function by adjusting the parameters. Hence in our course, We start from understanding the basics of functions which you might have touched upon in highschool. And then, In further sections, we move along and apply the basics and learn some of the important concepts related to approximation which is the core for any Machine learning,  Deep Learning , Artificial intelligence, Data Science model And, in the last two sections of this course, We make use of all our learning from previous sections, and train our Neural networks and understand how we apply in Linear Regression models by writing the code from scratch. We are sure that you will be amazed how well you can perform in your work once you have the intuition of calculus. This course is carefully designed by experts with student’s feedback so that you can have the premium learning experience. Join now to build confidence in Mathematics part of Machine learning,  Deep Learning , Artificial intelligence, Data Science and stay ahead in your career. See you in the Lesson 1.

According to an IBM report, Data Science jobs would likely grow by 30 percent. The estimated figure of job listing is 2,720,000 for Data Science in 2020 And according to the US Bureau of Labor Statistics, about 11 million jobs will be created by 2026 Data Science, Machine Learning and Artificial Intelligence are hottest and trending technologies across the globe, almost every multinational organization is working on it and they need a huge number people who can work on these technologies By keeping all the industry requirements in mind we have designed this course, with this single course you can start your journey in the field of Data Science In this course we tried to cover almost everything that is comes under the umbrella of Data Science, Topics covered: 1) Machine Learning Overview: Types of Machine Learning System, Machine Learning vs Traditional system of Computing, Different Machine Learning Algorithm, Machine Learning Workflow 2) Statistics Basic: Data, Levels of Measurement, Measures of Central Tendency, Population vs Sample, Probability based Sampling methods, Non Probability based Sampling method, Measures of Dispersion, Quartiles and IQR 3) Probability: Introduction to Probability, Permutations, Combinations, Intersection, Union and Complement, Independent and Dependent Events, Conditional Probability, Addition and Multiplication Rules, Bayes’ Theorem 4) Data Pre-Processing: Importing Libraries, Importing Dataset, Working with missing data, Encoding categorical data, Splitting dataset into train and test set, Feature scaling 5) Regression Analysis: Simple Linear Regression, Multiple Linear Regression, Support Vector Regression, Decision Tree, Random Forest Regression 6) Classification Techniques: Logistic Regression, KNN, Support Vector Machine, Decision Tree, Random Forest Classification 7) Natural Language Processing: Tokenization, Stemming, Lemmatization, Stop Words, Vocabulary and Matching, Parts of Speech Tagging, Named Entity Recognition, Sentence Segmentation 8) Artificial Neural Networks (ANNs): The Neuron, Activation Function, Cost Function, Gradient Descent and Back-Propagation, Building the Artificial Neural Networks, Binary Classification with Artificial Neural Networks 9) Convolutional Neural Networks (CNNs): Theory behind Convolutional Neural Networks, Different layers in Convolutional Neural Networks, Building Convolutional Neural Networks, Credit Card Fraud Detection with CNN 10) Recurrent Neural Network (RNNs): Theory behind Recurrent Neural Networks, Vanishing Gradient Problem, Working of LSTM and GRU, IMDB Review Classification with RNN - LSTM 11) Data Analysis with Numpy: NumPy Arrays, Indexing and Selection, NumPy Operations 12) Data Analysis with Pandas: Pandas Series, DataFrames, Multi-index and index hierarchy, Working with Missing Data, Groupby Function, Merging Joining and Concatenating DataFrames, Pandas Operations, Reading and Writing Files 13) Data Visualization with Matplotlib: Functional Method, Object Oriented Method, Subplots Method, Figure size, Aspect ratio and DPI, Matplotlib properties, Different type of plots like Scatter Plot, Bar plot, Histogram, Pie Chart 14) Python Crash Course: Part 1: Data Types,  Part 2: Python Statements, Part 3: Functions, Part 4: Object Oriented Programming Learn Data Science to advance your Career and Increase your knowledge in a fun and practical way ! Regards, Vijay Gadhave